Population Counts Population Counts / Constrained Individual countries 2020 ( 100m resolution ) / Germany 100m. Population

Germany 100m. Population
The spatial distribution of population in 2020, Germany

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. "NoData" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by Jeremiah J.Nieves et al. 2020.

The mapping approach is the Random Forests-based dasymetric redistribution developed by Stevens et al. (2015). The disaggregation was done by Maksym Bondarenko (WorldPop) and David Kerr (WorldPop), using the Random Forests population modelling R scripts (Bondarenko et al., 2020), with oversight from Alessandro Sorichetta (WorldPop).

SOURCE DATA:

  • 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 here).
  • Built-Settlement Growth Model (BSGM) outputs produced by Jeremiah J.Nieves et al. 2020.
  • Geospatial covariates representing factors related to population distribution, were obtained from the "Global High Resolution Population Denominators Project" (OPP1134076).

REFERENCES:

- 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

- 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).

- 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

- 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.

- 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

Region : Germany
DOI : 10.5258/SOTON/WP00684
Date of production : 2020-09-16
Recommended citation

Bondarenko M., Kerr D., Sorichetta A., and Tatem, A.J. 2020. Census/projection-disaggregated gridded population datasets for 189 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs. WorldPop, University of Southampton, UK. doi:10.5258/SOTON/WP00684

Data Files :
WorldPop datasets are available under the Creative Commons Attribution 4.0 International License. This means that you are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, provided attribution is included (appropriate credit and a link to the licence).