WorldPop

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

These data were produced by the WorldPop Research Group at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using Subnational Population Statistics 2020 for Ukraine provided in the Common Operational Dataset on Population Statistics (COD-PS) and ORNL LandScan HD for Ukraine 2022 settlement layer. The datasets are produced using the "top-down" method, with both the unconstrained and constrained top-down disaggregation methods used to produce two different datasets. The differences between constrained and un-constrained methods are described here . Main data sources Subnational Population Statistics for Ukraine provided by Common Operational Dataset on Population Statistics (COD-PS). The subnational population statistics were estimated using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since the last census. Settlement layer ORNL LandScan HD for Ukraine. Subnational Administrative Boundaries for Ukraine provided by OCHA. Geospatial covariate layers available at WorldPop. For further details, please, read the Release Statement. Release content ukr_pop_2020_100m_unconstrained_v1_0.zip ukr_pop_2020_100m_constrained_v1_0.zip ukr_pop_2020_1km_unconstrained_v1_0.zip ukr_pop_2020_1km_constrained_v1_0.zip ukr_agesex_2020_100m_unconstrained_v1_0.zip ukr_agesex_2020_100m_constrained_v1_0.zip ukr_agesex_2020_1km_unconstrained_v1_0.zip ukr_agesex_2020_1km_constrained_v1_0.zip ukr_agesex_0_18_2020_1km_unconstrained_v1_0.zip ukr_agesex_0_18_2020_1km_constrained_v1_0.zip Recommended citations Bondarenko M., Sorichetta A., Leasure DR. and Tatem AJ. 2022 Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 1.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00734 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

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 2.1 is an update to the previous version 2.0 gridded population estimates and is based on a correction of the settlement map. 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. This work was part of the GRID3 Bridge Funding project with funding from the Bill and Melinda Gates Foundation (INV-045694). Project partners included the GRID3 Inc and the Center for International Earth Science Information Network in the Earth Institute at Columbia University. Statistical modelling was originally led by Chris Jochem and Doug Leasure with additional support and oversight from Attila Lazar and Andy Tatem. Ortis Yankey led the population map update with additional support from Edith Darin. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org. SUGGESTED CITATIONS WorldPop. 2023. Bottom-up gridded population estimates for Nigeria, version 2.1. WorldPop, University of Southampton. DOI: 10.5258/SOTON/WP00765

These data were produced by WorldPop at the University of Southampton. These data include gridded estimates of population at approximately 100m for 2021, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Turkey in 2021 provided in the Common Operational Dataset on Population Statistics (COD-PS) and built-up surfaces/volumes/height covariates extracted from GHSL datasets. The constrained and unconstrained top-down disaggregation method was used to produce the datasets. The modelling work and geospatial data processing was led by Bondarenko M., Priyatikanto R., Sorichetta A. Oversight was provided by Tatem A.J. For further details, please, read the Release Statement. Recommended citations Bondarenko M., Priyatikanto R., Sorichetta A., and Tatem A.J.. 2023 Gridded population estimates for Turkey using UN COD-PS estimates 2021, version 1.0. WorldPop University of Southampton. doi:10.5258/SOTON/WP00758 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release@worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

These data were produced by WorldPop at the University of Southampton and the ‘Smart Cities and Spatial Development’ team at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR). These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS) and building height/area/fraction/volume covariates extracted from the World Settlement Footprint (WSF) imperviousness and WSF-3D by DLR. The constrained top-down disaggregation method was used to produce the datasets. The modelling work and geospatial data processing was led by Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., ,Zeidler J., Marconcini M., and Esch T.. Oversight was provided by Tatem A.J. Internal WorldPop peer reviews that helped to improve the results and documentation was provided by Lazar A.N.. Main data sources The German Aerospace Centre’s (DLR) WSF imperviousness and WSF 3D products (WSF-3D). Subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS). The subnational population estimates were produced using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since then. Subnational Administrative Boundaries for Ukraine provided by OCHA . Geospatial covariate layers available at WorldPop. For further details, please, read the Release Statement. Release content ukr_pop_2020_100m_constrained_v2.zip ukr_pop_2020_1km_constrained_v2.zip ukr_agesex_2020_100m_constrained_v2.zip ukr_agesex_2020_1km_constrained_v2.zip ukr_agesex_0_18_2020_100m_constrained_v2.zip ukr_agesex_0_18_2020_1km_constrained_v2.zip Recommended citations Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., Zeidler J., Marconcini, M., Esch T., and Tatem A.J. 2022 Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 2.0. WorldPop and DLR, University of Southampton. doi:10.5258/SOTON/WP00735 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.