Population Counts Population Counts / Constrained Individual countries 2020 UN adjusted (100m resolution) / Nigeria 100m. Population

The spatial distribution of population in 2020 with country total adjusted to match the corresponding UNPD estimate, Nigeria

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 (2019 Revision of World Population Prospects). "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).

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).
  • Building footprints were provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies (2020) and gridded building patterns derived from the datasets produced by Dooley 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).

- 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"© 2020. doi:10.5258/SOTON/WP00677

- 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

- Ecopia.AI and Maxar Technologies. 2020. Digitize Africa data. http://digitizeafrica.ai


Region : Nigeria
DOI : 10.5258/SOTON/WP00683
Date of production : 2020-09-12
Recommended citation

Bondarenko M., Kerr D., Sorichetta A., and Tatem, A.J. 2020. Census/projection-disaggregated gridded population datasets, adjusted to match the corresponding UNPD 2020 estimates, for 51 countries across sub-Saharan Africa using building footprints. WorldPop, University of Southampton, UK. doi:10.5258/SOTON/WP00683


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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).