[{"id":"49926","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"dza_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Algeria","resolution":"100","type":"Population","file_html":""},{"id":"49674","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"ago_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Angola","resolution":"100","type":"Population","file_html":""},{"id":"49684","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"ben_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Benin","resolution":"100","type":"Population","file_html":""},{"id":"49675","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"bwa_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Botswana","resolution":"100","type":"Population","file_html":""},{"id":"49723","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"bfa_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Burkina Faso","resolution":"100","type":"Population","file_html":""},{"id":"49676","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"bdi_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Burundi","resolution":"100","type":"Population","file_html":""},{"id":"49677","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"cmr_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Cameroon","resolution":"100","type":"Population","file_html":""},{"id":"49678","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"cpv_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Cape Verde","resolution":"100","type":"Population","file_html":""},{"id":"49679","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"caf_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Central African Republic","resolution":"100","type":"Population","file_html":""},{"id":"49680","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"tcd_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Chad","resolution":"100","type":"Population","file_html":""},{"id":"49681","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"com_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Comoros","resolution":"100","type":"Population","file_html":""},{"id":"49693","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"civ_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Cote d'Ivoire","resolution":"100","type":"Population","file_html":""},{"id":"49688","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"dji_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Djibouti","resolution":"100","type":"Population","file_html":""},{"id":"49683","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"cod_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"DRC","resolution":"100","type":"Population","file_html":""},{"id":"50088","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"egy_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Egypt","resolution":"100","type":"Population","file_html":""},{"id":"49685","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gnq_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Equatorial Guinea","resolution":"100","type":"Population","file_html":""},{"id":"49687","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"eri_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Eritrea","resolution":"100","type":"Population","file_html":""},{"id":"49719","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"swz_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Eswatini","resolution":"100","type":"Population","file_html":""},{"id":"49686","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"eth_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Ethiopia","resolution":"100","type":"Population","file_html":""},{"id":"49689","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gab_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Gabon","resolution":"100","type":"Population","file_html":""},{"id":"49690","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gmb_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Gambia","resolution":"100","type":"Population","file_html":""},{"id":"49691","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gha_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Ghana","resolution":"100","type":"Population","file_html":""},{"id":"49692","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gin_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Guinea","resolution":"100","type":"Population","file_html":""},{"id":"49706","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"gnb_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Guinea-Bissau","resolution":"100","type":"Population","file_html":""},{"id":"49694","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"ken_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Kenya","resolution":"100","type":"Population","file_html":""},{"id":"49695","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"lso_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Lesotho","resolution":"100","type":"Population","file_html":""},{"id":"49696","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"lbr_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Liberia","resolution":"100","type":"Population","file_html":""},{"id":"50009","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lby_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Libya","resolution":"100","type":"Population","file_html":""},{"id":"49697","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"mdg_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Madagascar","resolution":"100","type":"Population","file_html":""},{"id":"49698","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"mwi_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Malawi","resolution":"100","type":"Population","file_html":""},{"id":"49699","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"mli_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Mali","resolution":"100","type":"Population","file_html":""},{"id":"49700","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"mrt_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Mauritania","resolution":"100","type":"Population","file_html":""},{"id":"49701","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"mus_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Mauritius","resolution":"100","type":"Population","file_html":""},{"id":"49955","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"myt_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Mayotte","resolution":"100","type":"Population","file_html":""},{"id":"50024","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mar_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Morocco","resolution":"100","type":"Population","file_html":""},{"id":"49702","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"moz_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Mozambique","resolution":"100","type":"Population","file_html":""},{"id":"49703","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"nam_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Namibia","resolution":"100","type":"Population","file_html":""},{"id":"49704","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"ner_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Niger","resolution":"100","type":"Population","file_html":""},{"id":"49705","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"nga_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Nigeria","resolution":"100","type":"Population","file_html":""},{"id":"49682","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"cog_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Rep. Congo","resolution":"100","type":"Population","file_html":""},{"id":"49707","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"reu_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"R\u00e9union","resolution":"100","type":"Population","file_html":""},{"id":"49708","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"rwa_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Rwanda","resolution":"100","type":"Population","file_html":""},{"id":"50056","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"shn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Saint Helena, Ascension and Tristan da Cunha","resolution":"100","type":"Population","file_html":""},{"id":"49709","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"stp_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Sao Tome and Principe","resolution":"100","type":"Population","file_html":""},{"id":"49710","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"sen_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Senegal","resolution":"100","type":"Population","file_html":""},{"id":"49711","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"syc_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Seychelles","resolution":"100","type":"Population","file_html":""},{"id":"49712","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"sle_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Sierra Leone","resolution":"100","type":"Population","file_html":""},{"id":"49713","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"som_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Somalia","resolution":"100","type":"Population","file_html":""},{"id":"49714","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"zaf_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"South Africa","resolution":"100","type":"Population","file_html":""},{"id":"49716","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"ssd_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"South Sudan","resolution":"100","type":"Population","file_html":""},{"id":"49717","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"sdn_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Sudan","resolution":"100","type":"Population","file_html":""},{"id":"49722","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"tza_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Tanzania","resolution":"100","type":"Population","file_html":""},{"id":"49720","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"tgo_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Togo","resolution":"100","type":"Population","file_html":""},{"id":"50081","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tun_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Africa","country":"Tunisia","resolution":"100","type":"Population","file_html":""},{"id":"49721","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"uga_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Uganda","resolution":"100","type":"Population","file_html":""},{"id":"49718","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"esh_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Western Sahara","resolution":"100","type":"Population","file_html":""},{"id":"49724","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"zmb_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Zambia","resolution":"100","type":"Population","file_html":""},{"id":"49715","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020).<\/p> <p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li>Building footprints were provided by the Digitize Africa project of <cite>Ecopia.AI and Maxar Technologies (2020)<\/cite> and gridded building patterns derived from the datasets produced by <cite>Dooley et al. 2020.<\/cite><\/li><li>Geospatial covariates representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\"(OPP1134076)<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Dooley, C. A., Boo, G., Leasure, D.R. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. University of Southampton: Southampton, UK. Source of building footprints \"Ecopia Vector Maps Powered by Maxar Satellite Imagery\"&#169; 2020. doi:10.5258\/SOTON\/WP00677<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p><p><small><em>- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http:\/\/digitizeafrica.ai<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00683","popyear":"2020","date":"2020-09-12","file_img":"zwe_ppp_100m_Constrained_Maxar_2020_Image.png","continent":"Africa","country":"Zimbabwe","resolution":"100","type":"Population","file_html":""},{"id":"50058","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"aia_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Anguilla","resolution":"100","type":"Population","file_html":""},{"id":"49929","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"atg_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Antigua and Barbuda","resolution":"100","type":"Population","file_html":""},{"id":"49931","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"arg_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Argentina","resolution":"100","type":"Population","file_html":""},{"id":"50030","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"abw_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Aruba","resolution":"100","type":"Population","file_html":""},{"id":"49933","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bhs_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Bahamas","resolution":"100","type":"Population","file_html":""},{"id":"49937","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"brb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Barbados","resolution":"100","type":"Population","file_html":""},{"id":"49943","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"blz_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Belize","resolution":"100","type":"Population","file_html":""},{"id":"49939","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bmu_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Bermuda","resolution":"100","type":"Population","file_html":""},{"id":"49941","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bol_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Bolivia","resolution":"100","type":"Population","file_html":""},{"id":"50032","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bes_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Bonaire, Sint Eustatius and Saba","resolution":"100","type":"Population","file_html":""},{"id":"49921","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bra_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Brazil","resolution":"100","type":"Population","file_html":""},{"id":"49945","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vgb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"British Virgin Islands","resolution":"100","type":"Population","file_html":""},{"id":"49922","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"can_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Canada","resolution":"100","type":"Population","file_html":""},{"id":"49951","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cym_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Cayman Islands","resolution":"100","type":"Population","file_html":""},{"id":"49923","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"chl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Chile","resolution":"100","type":"Population","file_html":""},{"id":"49954","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"col_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Colombia","resolution":"100","type":"Population","file_html":""},{"id":"49957","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cri_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Costa Rica","resolution":"100","type":"Population","file_html":""},{"id":"49959","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cub_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Cuba","resolution":"100","type":"Population","file_html":""},{"id":"50029","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cuw_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Cura\u00e7ao","resolution":"100","type":"Population","file_html":""},{"id":"49963","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"dma_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Dominica","resolution":"100","type":"Population","file_html":""},{"id":"49964","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"dom_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Dominican Republic","resolution":"100","type":"Population","file_html":""},{"id":"49965","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ecu_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Ecuador","resolution":"100","type":"Population","file_html":""},{"id":"49966","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"slv_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"El Salvador","resolution":"100","type":"Population","file_html":""},{"id":"49969","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"flk_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Falkland Islands (Malvinas)","resolution":"100","type":"Population","file_html":""},{"id":"49973","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"guf_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"French Guiana","resolution":"100","type":"Population","file_html":""},{"id":"49918","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"grl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Greenland","resolution":"100","type":"Population","file_html":""},{"id":"49981","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"grd_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Grenada","resolution":"100","type":"Population","file_html":""},{"id":"49982","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"glp_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Guadeloupe","resolution":"100","type":"Population","file_html":""},{"id":"49984","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"gtm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Guatemala","resolution":"100","type":"Population","file_html":""},{"id":"49985","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"guy_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Guyana","resolution":"100","type":"Population","file_html":""},{"id":"49986","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"hti_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Haiti","resolution":"100","type":"Population","file_html":""},{"id":"49988","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"hnd_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Honduras","resolution":"100","type":"Population","file_html":""},{"id":"49998","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"jam_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Jamaica","resolution":"100","type":"Population","file_html":""},{"id":"50017","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mtq_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Martinique","resolution":"100","type":"Population","file_html":""},{"id":"50018","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mex_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Mexico","resolution":"100","type":"Population","file_html":""},{"id":"50023","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"msr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Montserrat","resolution":"100","type":"Population","file_html":""},{"id":"50036","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"nic_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Nicaragua","resolution":"100","type":"Population","file_html":""},{"id":"50044","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pan_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Panama","resolution":"100","type":"Population","file_html":""},{"id":"50046","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pry_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Paraguay","resolution":"100","type":"Population","file_html":""},{"id":"50047","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"per_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Peru","resolution":"100","type":"Population","file_html":""},{"id":"50052","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pri_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Puerto Rico","resolution":"100","type":"Population","file_html":""},{"id":"50055","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"blm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Barth\u00e9lemy","resolution":"100","type":"Population","file_html":""},{"id":"50057","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kna_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Kitts and Nevis","resolution":"100","type":"Population","file_html":""},{"id":"50059","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lca_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Lucia","resolution":"100","type":"Population","file_html":""},{"id":"50060","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"maf_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Martin","resolution":"100","type":"Population","file_html":""},{"id":"50061","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"spm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Pierre and Miquelon","resolution":"100","type":"Population","file_html":""},{"id":"50062","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vct_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Saint Vincent and the Grenadines","resolution":"100","type":"Population","file_html":""},{"id":"50031","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"sxm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Sint Maarten (Dutch part)","resolution":"100","type":"Population","file_html":""},{"id":"50071","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"sur_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Suriname","resolution":"100","type":"Population","file_html":""},{"id":"50079","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tto_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Trinidad and Tobago","resolution":"100","type":"Population","file_html":""},{"id":"50084","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tca_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Turks and Caicos Islands","resolution":"100","type":"Population","file_html":""},{"id":"49916","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"usa_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"United States of America","resolution":"100","type":"Population","file_html":""},{"id":"49917","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vir_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"United States Virgin Islands","resolution":"100","type":"Population","file_html":""},{"id":"50091","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ury_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Uruguay","resolution":"100","type":"Population","file_html":""},{"id":"50093","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ven_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Americas","country":"Venezuela","resolution":"100","type":"Population","file_html":""},{"id":"49924","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"afg_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Afghanistan","resolution":"100","type":"Population","file_html":""},{"id":"49936","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"arm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Armenia","resolution":"100","type":"Population","file_html":""},{"id":"49930","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"aze_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Azerbaijan","resolution":"100","type":"Population","file_html":""},{"id":"49934","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bhr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Bahrain","resolution":"100","type":"Population","file_html":""},{"id":"49935","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bgd_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Bangladesh","resolution":"100","type":"Population","file_html":""},{"id":"49940","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"btn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Bhutan","resolution":"100","type":"Population","file_html":""},{"id":"49946","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"brn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Brunei Darussalam","resolution":"100","type":"Population","file_html":""},{"id":"49950","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"khm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Cambodia","resolution":"100","type":"Population","file_html":""},{"id":"49919","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"chn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"China","resolution":"100","type":"Population","file_html":""},{"id":"49960","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cyp_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Cyprus","resolution":"100","type":"Population","file_html":""},{"id":"49975","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"geo_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Georgia","resolution":"100","type":"Population","file_html":""},{"id":"49989","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"hkg_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Hong Kong","resolution":"100","type":"Population","file_html":""},{"id":"49992","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ind_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"India","resolution":"100","type":"Population","file_html":""},{"id":"49915","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"idn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Indonesia","resolution":"100","type":"Population","file_html":""},{"id":"49993","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"irn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Iran","resolution":"100","type":"Population","file_html":""},{"id":"49994","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"irq_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Iraq","resolution":"100","type":"Population","file_html":""},{"id":"49996","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"isr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Israel","resolution":"100","type":"Population","file_html":""},{"id":"49999","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"jpn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Japan","resolution":"100","type":"Population","file_html":""},{"id":"50001","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"jor_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Jordan","resolution":"100","type":"Population","file_html":""},{"id":"50000","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kaz_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Kazakhstan","resolution":"100","type":"Population","file_html":""},{"id":"50004","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kwt_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Kuwait","resolution":"100","type":"Population","file_html":""},{"id":"50005","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kgz_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Kyrgyz Republic","resolution":"100","type":"Population","file_html":""},{"id":"50006","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lao_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Lao People's Democratic Republic","resolution":"100","type":"Population","file_html":""},{"id":"50007","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lbn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Lebanon","resolution":"100","type":"Population","file_html":""},{"id":"50013","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mac_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Macao","resolution":"100","type":"Population","file_html":""},{"id":"50014","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mys_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Malaysia","resolution":"100","type":"Population","file_html":""},{"id":"50015","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mdv_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Maldives","resolution":"100","type":"Population","file_html":""},{"id":"50020","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mng_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Mongolia","resolution":"100","type":"Population","file_html":""},{"id":"49948","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mmr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Myanmar","resolution":"100","type":"Population","file_html":""},{"id":"50027","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"npl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Nepal","resolution":"100","type":"Population","file_html":""},{"id":"50002","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"prk_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"North Korea","resolution":"100","type":"Population","file_html":""},{"id":"50025","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"omn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Oman","resolution":"100","type":"Population","file_html":""},{"id":"50043","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pak_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Pakistan","resolution":"100","type":"Population","file_html":""},{"id":"49976","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pse_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Palestinian Territory","resolution":"100","type":"Population","file_html":""},{"id":"50048","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"phl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Philippines","resolution":"100","type":"Population","file_html":""},{"id":"50053","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"qat_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Qatar","resolution":"100","type":"Population","file_html":""},{"id":"50064","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"sau_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Saudi Arabia","resolution":"100","type":"Population","file_html":""},{"id":"50066","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"sgp_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Singapore","resolution":"100","type":"Population","file_html":""},{"id":"50003","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kor_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"South Korea","resolution":"100","type":"Population","file_html":""},{"id":"49952","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lka_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Sri Lanka","resolution":"100","type":"Population","file_html":""},{"id":"50074","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"syr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Syrian Arab Republic","resolution":"100","type":"Population","file_html":""},{"id":"49953","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"twn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Taiwan","resolution":"100","type":"Population","file_html":""},{"id":"50075","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tjk_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Tajikistan","resolution":"100","type":"Population","file_html":""},{"id":"50076","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tha_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Thailand","resolution":"100","type":"Population","file_html":""},{"id":"50051","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tls_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Timor-Leste","resolution":"100","type":"Population","file_html":""},{"id":"50082","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tur_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Turkiye","resolution":"100","type":"Population","file_html":""},{"id":"50083","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tkm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Turkmenistan","resolution":"100","type":"Population","file_html":""},{"id":"50080","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"are_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"United Arab Emirates","resolution":"100","type":"Population","file_html":""},{"id":"50092","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"uzb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Uzbekistan","resolution":"100","type":"Population","file_html":""},{"id":"50068","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vnm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Vietnam","resolution":"100","type":"Population","file_html":""},{"id":"50096","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"yem_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Asia","country":"Yemen","resolution":"100","type":"Population","file_html":""},{"id":"49925","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"alb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Albania","resolution":"100","type":"Population","file_html":""},{"id":"49928","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"and_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Andorra","resolution":"100","type":"Population","file_html":""},{"id":"49932","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"aut_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Austria","resolution":"100","type":"Population","file_html":""},{"id":"49949","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"blr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Belarus","resolution":"100","type":"Population","file_html":""},{"id":"49938","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bel_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Belgium","resolution":"100","type":"Population","file_html":""},{"id":"49942","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bih_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Bosnia and Herzegovina","resolution":"100","type":"Population","file_html":""},{"id":"49947","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"bgr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Bulgaria","resolution":"100","type":"Population","file_html":""},{"id":"49958","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"hrv_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Croatia","resolution":"100","type":"Population","file_html":""},{"id":"49961","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cze_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Czech Republic","resolution":"100","type":"Population","file_html":""},{"id":"49962","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"dnk_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Denmark","resolution":"100","type":"Population","file_html":""},{"id":"49967","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"est_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Estonia","resolution":"100","type":"Population","file_html":""},{"id":"49968","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"fro_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Faroe Islands","resolution":"100","type":"Population","file_html":""},{"id":"49971","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"fin_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Finland","resolution":"100","type":"Population","file_html":""},{"id":"49972","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"fra_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"France","resolution":"100","type":"Population","file_html":""},{"id":"49977","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"deu_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Germany","resolution":"100","type":"Population","file_html":""},{"id":"49978","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"gib_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Gibraltar","resolution":"100","type":"Population","file_html":""},{"id":"49980","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"grc_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Greece","resolution":"100","type":"Population","file_html":""},{"id":"49987","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vat_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Holy See (Vatican City State)","resolution":"100","type":"Population","file_html":""},{"id":"49990","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"hun_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Hungary","resolution":"100","type":"Population","file_html":""},{"id":"49991","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"isl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Iceland","resolution":"100","type":"Population","file_html":""},{"id":"49995","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"irl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Ireland","resolution":"100","type":"Population","file_html":""},{"id":"50090","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"imn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Isle of Man","resolution":"100","type":"Population","file_html":""},{"id":"49997","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ita_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Italy","resolution":"100","type":"Population","file_html":""},{"id":"50008","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lva_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Latvia","resolution":"100","type":"Population","file_html":""},{"id":"50010","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lie_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Liechtenstein","resolution":"100","type":"Population","file_html":""},{"id":"50011","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ltu_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Lithuania","resolution":"100","type":"Population","file_html":""},{"id":"50012","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"lux_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Luxembourg","resolution":"100","type":"Population","file_html":""},{"id":"50087","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mkd_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Macedonia","resolution":"100","type":"Population","file_html":""},{"id":"50016","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mlt_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Malta","resolution":"100","type":"Population","file_html":""},{"id":"50021","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mda_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Moldova","resolution":"100","type":"Population","file_html":""},{"id":"50019","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mco_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Monaco","resolution":"100","type":"Population","file_html":""},{"id":"50022","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mne_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Montenegro","resolution":"100","type":"Population","file_html":""},{"id":"50028","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"nld_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Netherlands","resolution":"100","type":"Population","file_html":""},{"id":"50038","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"nor_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Norway","resolution":"100","type":"Population","file_html":""},{"id":"50049","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pol_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Poland","resolution":"100","type":"Population","file_html":""},{"id":"50050","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"prt_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Portugal","resolution":"100","type":"Population","file_html":""},{"id":"50054","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"rou_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Romania","resolution":"100","type":"Population","file_html":""},{"id":"49914","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"rus_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Russian Federation","resolution":"100","type":"Population","file_html":""},{"id":"50063","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"smr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"San Marino","resolution":"100","type":"Population","file_html":""},{"id":"50065","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"srb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Serbia","resolution":"100","type":"Population","file_html":""},{"id":"50067","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"svk_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Slovakia (Slovak Republic)","resolution":"100","type":"Population","file_html":""},{"id":"50069","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"svn_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Slovenia","resolution":"100","type":"Population","file_html":""},{"id":"50070","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"esp_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Spain","resolution":"100","type":"Population","file_html":""},{"id":"50072","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"swe_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Sweden","resolution":"100","type":"Population","file_html":""},{"id":"50073","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"che_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Switzerland","resolution":"100","type":"Population","file_html":""},{"id":"50086","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ukr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"Ukraine","resolution":"100","type":"Population","file_html":""},{"id":"50089","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"gbr_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Europe","country":"United Kingdom of Great Britain & Northern Ireland","resolution":"100","type":"Population","file_html":""},{"id":"49927","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"asm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"American Samoa","resolution":"100","type":"Population","file_html":""},{"id":"49920","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"aus_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Australia","resolution":"100","type":"Population","file_html":""},{"id":"49956","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"cok_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Cook Islands","resolution":"100","type":"Population","file_html":""},{"id":"49970","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"fji_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Fiji","resolution":"100","type":"Population","file_html":""},{"id":"49974","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"pyf_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"French Polynesia","resolution":"100","type":"Population","file_html":""},{"id":"49983","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"gum_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Guam","resolution":"100","type":"Population","file_html":""},{"id":"49979","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"kir_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Kiribati","resolution":"100","type":"Population","file_html":""},{"id":"50041","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mhl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Marshall Islands","resolution":"100","type":"Population","file_html":""},{"id":"50040","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"fsm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Micronesia","resolution":"100","type":"Population","file_html":""},{"id":"50026","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"nru_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Nauru","resolution":"100","type":"Population","file_html":""},{"id":"50033","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ncl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"New Caledonia","resolution":"100","type":"Population","file_html":""},{"id":"50035","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"nzl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"New Zealand","resolution":"100","type":"Population","file_html":""},{"id":"50037","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"niu_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Niue","resolution":"100","type":"Population","file_html":""},{"id":"50039","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"mnp_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Northern Mariana Islands","resolution":"100","type":"Population","file_html":""},{"id":"50042","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"plw_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Palau","resolution":"100","type":"Population","file_html":""},{"id":"50045","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"png_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Papua New Guinea","resolution":"100","type":"Population","file_html":""},{"id":"50095","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"wsm_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Samoa","resolution":"100","type":"Population","file_html":""},{"id":"49944","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"slb_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Solomon Islands","resolution":"100","type":"Population","file_html":""},{"id":"50077","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tkl_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Tokelau","resolution":"100","type":"Population","file_html":""},{"id":"50078","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"ton_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Tonga","resolution":"100","type":"Population","file_html":""},{"id":"50085","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"tuv_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Tuvalu","resolution":"100","type":"Population","file_html":""},{"id":"50034","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"vut_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Vanuatu","resolution":"100","type":"Population","file_html":""},{"id":"50094","desc":"<p>Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (<a target=\"_blank\" href=\"https:\/\/population.un.org\/wpp\/Download\/Files\/1_Indicators%20(Standard)\/EXCEL_FILES\/1_Population\/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx\">2019 Revision of World Population Prospects<\/a>). \"NoData\" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/p><p>The mapping approach is the Random Forests-based dasymetric redistribution developed by <cite>Stevens et al. (2015)<\/cite>.  The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (<cite>Bondarenko et al., 2020<\/cite>), with oversight from Alessandro Sorichetta (WorldPop).<\/p><p class=\"mt-3\"><b>SOURCE DATA:<\/b><\/p><ul><li>This dataset was produced based on the 2020 population census\/projection-based estimates for 2020  (information and sources of the input population data can be found <a href=\"https:\/\/www.worldpop.org\/resources\/docs\/national_boundaries\/global-input-population-data-summary.xlsx\">here<\/a>).<\/li><li><a href=\"https:\/\/www.worldpop.org\/geodata\/listing?id=32\" target=\"_blank\">Built-Settlement Growth Model (BSGM)<\/a> outputs produced by <cite>Jeremiah J.Nieves et al. 2020<\/cite>.<\/li><li><a href=\"https:\/\/www.worldpop.org\/project\/categories?id=14\" target=\"_blank\">Geospatial covariates<\/a> representing factors related to population distribution, were obtained from the \"<cite>Global High Resolution Population Denominators Project<\/cite>\" (OPP1134076).<\/li><\/ul><p class=\"mt-3\"><b>REFERENCES:<\/b><\/p><p><small><em>- Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. https:\/\/doi.org\/10.1371\/journal.pone.0107042<\/em><\/small><\/p><p><small><em>- WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).<\/em><\/small><\/p><p><small><em>- Jeremiah J. Nieves, Alessandro Sorichetta, Catherine Linard, Maksym Bondarenko, Jessica E. Steele, Forrest R. Stevens, Andrea E. Gaughan, Alessandra Carioli, Donna J. Clarke, Thomas Esch, Andrew J. Tatem, Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night, Computers, Environment and Urban Systems,Volume 80,2020,101444,ISSN 0198-9715,https:\/\/doi.org\/10.1016\/j.compenvurbsys.2019.101444<\/em><\/small><\/p><p><small><em>- Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations. Remote Sens. 2020, 12, 1545.<\/em><\/small><\/p><p><small><em>- Bondarenko M., Nieves J. J., Stevens F. R., Gaughan A. E., Tatem A. and Sorichetta A. 2020. wpgpRFPMS: Random Forests population modelling R scripts, version 0.1.0. University of Southampton: Southampton, UK. https:\/\/dx.doi.org\/10.5258\/SOTON\/WP00665<\/em><\/small><\/p>","doi":"10.5258\/SOTON\/WP00685","popyear":"2020","date":"2020-09-16","file_img":"wlf_ppp_100m_Constrained_BSGM_2020_Image.png","continent":"Oceania","country":"Wallis and Futuna","resolution":"100","type":"Population","file_html":""}]