Modelled gridded population estimates for the Haut-Katanga, Haut-Lomami, Ituri, Kasaï, Kasaï Oriental, Lomami and Sud-Kivu provinces in the Democratic Republic of the Congo (2021), version 3.0.

These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) project funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom Foreign, Commonwealth & Development Office (OPP1182425). Project partners include WorldPop at the University of Southampton, the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation. The modelling work was led by Gianluca Boo and Edith Darin with the support from Douglas R. Leasure and Claire A. Dooley. Coordination was provided by Heather R. Chamberlain and oversight by Andrew J. Tatem and Attila N. Lazar. The support of the whole WorldPop Research Group is acknowledged. The UCLA-DRC Health Research and Training Program, the Kinshasa School of Public Health (KSPH), and the Bureau Central du Recensement (BCR) coordinated and conducted the two microcensus rounds. The Oak Ridge National Laboratory contributed to the first round of microcensus. We acknowledge the contribution of the many individuals within these institutions. __Recommended citation:__ _Boo G, Darin E, Leasure DR, Dooley CA, Chamberlain HR, Lazar AN, Tatem AJ. 2020. Modelled gridded population estimates for the Kinshasa, Kongo-Central, Kwango, Kwilu, and Mai-Ndombe provinces in the Democratic Republic of the Congo, version 2.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00669_

Democratic Republic of the Congo gridded population estimates. This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for the Democratic Republic of the Congo (DRC), along with uncertainty estimates. The data is a mosaic of gridded population estimates produced for all 26 provinces namely Bas-Uele, Equateur, Haut-Katanga, Haut-Lomami, Haut-Uele, Ituri, Kasai, Kasai-Central, Kasai-Oriental, Kinshasa, Kongo-Central, Kwango, Kwilu, Lomami, Lualaba, Mai-Ndombe, Maniema, Mongala, Nord-Kivu, Nord-Ubangi, Sankuru, Sud-Kivu, Sud-Ubangi, Tanganyika, Tshopo and Tshuapa. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC, settlement extent and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. Please note that PNLP data were collected across the DRC, except in Kasai, Kasai Central, Kasai Oriental, Kwango, Nord Ubangi and Lualaba, where the modelling utilised observations from neighbouring provinces The projection for all GIS files is the geographic coordinate system WGS84 (World Geodetic System 1984). Grid cells with values of NA represent areas that were mapped as unsettled according to building footprints data or being outside the national boundary. Suggested Dataset Citation: Yankey O., Nnanatu C., Boo G., Chaudhuri S., Krishnaveni K S, Megheib M, Bonnie A., Chamberlain H., Lazar A. N., Tatem A. J. 2025. Bottom-up gridded population estimates for the Democratic Republic of Congo, version 4.4. WorldPop. URL. Accessed DAY-MONTH-YEAR.

These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) project funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom’s Department for International Development (OPP1182408). Project partners include WorldPop at the University of Southampton, the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation. The Zambia Statistics Agency supported and facilitated this work, and provided the household survey datasets. The modelling work was led by Claire A. Dooley with support from Douglas R. Leasure. Geospatial data processing was carried out by Heather R. Chamberlain, Claire A. Dooley and Oliver Pannell. Coordination and stakeholder engagement was led by Heather R. Chamberlain and Claire A. Dooley with support from Polly Marshall. Oversight was provided by Andrew J. Tatem and Attila N. Lazar. Note, these data are operational population estimates and are not official government statistics For further details, please, read ZMB_population_v1_0_README.pdf __Recommended citation__ _WorldPop (School of Geography and Environmental Science, University of Southampton). 2020. Bottom-up gridded population estimates for Zambia, version 1.0. https://dx.doi.org/10.5258/SOTON/WP00662_

These data consist of modelled gridded population estimates produced at a spatial resolution of approximately 100m across Mali. The estimates comprise a combination of total population counts at enumeration area level collected by the census cartography team of the Mali Statistics Office and modelled population counts created using a Bayesian statistical model for areas that could not be covered by the cartography team because of security issues. The main input data for the model are the cartography data collected in the safe part of the country in 2019-2020 (628 out of 714 Communes –administrative level 3–, that is 87% of the country territory). Other essential input data include metrics derived from building footprints, which were automatically delineated by Ecopia.AI in 2021 using satellite imagery collected by Maxar Technologies between 2010 and 2021. The modelled population estimates represent the period of the census cartography, but their consistency may be impacted by the accuracy of the building footprints. These data were produced by the WorldPop Research Group at the University of Southampton as part of the GRID3 Project, GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office (INV 009579, formerly OPP 1182425). The study was approved by the Faculty Ethics Committee of the University of Southampton (ERGO II 64957). The project was led by the Center for International Earth Science Information Network (CIESIN) at Columbia University, in collaboration with the WorldPop Research Group at the University of Southampton, the United Nations Fund for Population (UNFPA) and the Malian Institut National de la Statistique (INSTAT). The production of these data was led by Edith Darin (WorldPop) with support from Matthias Kuépié and Jean Wakam (UNFPA), Abdoul Karim Diawara, Assa Gakou and Siaka Cissé (Institut National de la Statistique), and Attila N Lazar (WorldPop) and Andrew J Tatem (WorldPop). The authors acknowledge the support of their respective institutions in the completion of this work. **Recommended citations** WorldPop and Institut National de la Statistique du Mali. 2022. *Census-cartography-based gridded population estimates for Mali (2020), version 1.0.* WorldPop, University of Southampton. doi:10.5258/SOTON/WP00745 **License** These data may be redistributed following the terms of a Creative Commons Share-Alike Attribution 4.0 International (CC BY SA 4.0) license

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to various age-sex groups. Version 2.0 is an update to the previous version 1.2 gridded population estimates and is based on more recent and detailed settlement information and a different regional boundary definition. These model-based population estimates most likely represent the time period around 2019, corresponding to the period when the satellite imagery was processed to generate building footprints. Populations are mapped only into areas where residential settlements are predicted. These data were produced by the WorldPop Research Group at the University of Southampton in collaboration with the National Population Commission of Nigeria. This work was part of the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3 ) programme with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (INV 009579, formerly OPP 1182425). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network (CIESIN), a center within the Columbia Climate School at Columbia University, and the Flowminder Foundation. Statistical modelling was led by Chris Jochem and Doug Leasure additional support and oversight from Attila Lazar and Andy Tatem. Chris Lloyd provided the residential building classification. The microcensus data were originally collected by eHealth Africa and Oak Ridge National Laboratory with support from the Bill and Melinda Gates Foundation. The WorldPop grou and GRID3 partners are acknowledged for their project support. __RELEASE CONTENT__ 1. NGA_population_v2_0_gridded.zip 2. NGA_population_v2_0_admin.zip 3. NGA_population_v2_0_sql.sql 4. NGA_population_v2_0_mastergrid.tif 5. NGA_population_v2_0_tiles.zip 6. NGA_population_v2_0_agesex.zip __LICENSE__ These data (1-6) may be redistributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License. __Recommended citations__ _WorldPop and National Population Commission of Nigeria. 2021. Bottom-up gridded population estimates for Nigeria, version 2.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00729._ For further details, please, read NGA_population_v2_0_README.pdf

This data release includes gridded population estimates (~100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to individual age-sex groups. These population estimates represent the time period 2016 to 2017 corresponding to when the household surveys were conducted. Populations were mapped into areas where residential settlements were detected based on satellite imagery mostly from 2014. The data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom's Department for International Development (OPP1182408). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation. Statistical modellingwas led by Doug Leasure and Chris Jochem with oversight from Andy Tatem. In-country implementation was led by Tracy Adole. Oak Ridge National Laboratories (ORNL), eHealth Africa, and the Bill and Melinda Gates Foundation collected microcensus data and produced the settlement map used as inputs for this work. The whole WorldPop group and GRID3 partners are acknowledged for overall project support. __RELEASE CONTENT__ 1. NGA_population_v1_2_gridded.zip 2. NGA_population_v1_2_admin.zip 3. NGA_population_v1_2_sql.sql 4. NGA_population_v1_2_mastergrid.tif 5. NGA_population_v1_2_tiles.zip 6. NGA_population_v1_2_agesex.zip 7. NGA_population_v1_2_methods.zip __LICENSE__ These data (1-6) may be redistributed using a Creative Commons Attribution Share-Alike 4.0 License. The methods documentation (7) may be redistributed using a Creative Commons Attribution 4.0 License. __Recommended citations__ _WorldPop. 2019. Bottom-up gridded population estimates for Nigeria, version 1.2. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00655_ _WorldPop. 2020. Bottom-up gridded population estimates for individual age-sex groups in Nigeria, version 1.2.1. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00661_ _Leasure DR, Jochem WC, Weber EM, Seaman V, Tatem AJ. 2020. National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1913050117_ For further details, please, read NGA_population_v1_2_README.pdf