Age and sex structures Age and sex structures / Constrained individual countries 2020 UN adjusted / India 100m. Age structures

India 100m Age structures in 2020 with country total adjusted to match the corresponding UNPD estimate.

Estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 80+) in 2020 for India.

The dataset is available to download in Geotiff format at a resolution of 3 arc seconds (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male/female in each age group per grid square 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 (2019 Revision of World Population Prospects). "NoData" values represent areas that were mapped as unsettled based on the outputs of the Built-Settlement Growth Model (BSGM) developed by Nieves, J. J et al. 2019 and 2020

The mapping approach is Pezzulo, C. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89 (2017)

Filenames: Example ind_f_5_2020_constrained_UNadj.tif People per pixel (PPP) for female age group 5 to 9 years (f_05) in India for year 2020. For other datasets, m = male, 00 = age group 0 to 12months, 01 = age group 1 to 4 years, 80 = age 80 years and over

REFERENCES:

- 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). https://dx.doi.org/10.5258/SOTON/WP00646

- 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.

- Pezzulo, C., Hornby, G., Sorichetta, A. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci Data 4, 170089 (2017). https://doi.org/10.1038/sdata.2017.89


Region : India
DOI : 10.5258/SOTON/WP00698
Date of production : 2020-11-30
Recommended citation

Bondarenko M., Kerr D., Sorichetta A., and Tatem, A.J. 2020. Estimates of 2020 total number of people per grid square, adjusted to match the corresponding UNPD 2020 estimates and broken down by gender and age groupings, produced using Built-Settlement Growth Model (BSGM) outputs. WorldPop, University of Southampton, UK. doi:10.5258/SOTON/WP00698


Data Files :
Name Size
ind_f_0_2020_constrained_UNadj.tif 457.17 MB
ind_f_10_2020_constrained_UNadj.tif 457.19 MB
ind_f_15_2020_constrained_UNadj.tif 457.16 MB
ind_f_1_2020_constrained_UNadj.tif 457.17 MB
ind_f_20_2020_constrained_UNadj.tif 457.13 MB
ind_f_25_2020_constrained_UNadj.tif 457.17 MB
ind_f_30_2020_constrained_UNadj.tif 457.16 MB
ind_f_35_2020_constrained_UNadj.tif 457.17 MB
ind_f_40_2020_constrained_UNadj.tif 457.18 MB
ind_f_45_2020_constrained_UNadj.tif 457.16 MB
ind_f_50_2020_constrained_UNadj.tif 457.12 MB
ind_f_55_2020_constrained_UNadj.tif 457.16 MB
ind_f_5_2020_constrained_UNadj.tif 457.19 MB
ind_f_60_2020_constrained_UNadj.tif 457.17 MB
ind_f_65_2020_constrained_UNadj.tif 457.13 MB
ind_f_70_2020_constrained_UNadj.tif 457.15 MB
ind_f_75_2020_constrained_UNadj.tif 457.13 MB
ind_f_80_2020_constrained_UNadj.tif 457.17 MB
ind_m_0_2020_constrained_UNadj.tif 457.17 MB
ind_m_10_2020_constrained_UNadj.tif 457.17 MB
ind_m_15_2020_constrained_UNadj.tif 457.16 MB
ind_m_1_2020_constrained_UNadj.tif 457.17 MB
ind_m_20_2020_constrained_UNadj.tif 457.15 MB
ind_m_25_2020_constrained_UNadj.tif 457.15 MB
ind_m_30_2020_constrained_UNadj.tif 457.16 MB
ind_m_35_2020_constrained_UNadj.tif 457.18 MB
ind_m_40_2020_constrained_UNadj.tif 457.17 MB
ind_m_45_2020_constrained_UNadj.tif 457.15 MB
ind_m_50_2020_constrained_UNadj.tif 457.14 MB
ind_m_55_2020_constrained_UNadj.tif 457.16 MB
ind_m_5_2020_constrained_UNadj.tif 457.19 MB
ind_m_60_2020_constrained_UNadj.tif 457.18 MB
ind_m_65_2020_constrained_UNadj.tif 457.13 MB
ind_m_70_2020_constrained_UNadj.tif 457.16 MB
ind_m_75_2020_constrained_UNadj.tif 457.15 MB
ind_m_80_2020_constrained_UNadj.tif 457.17 MB
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