Democratic Republic of the Congo population data

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

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 consist of modelled gridded population estimates produced at a spatial resolution of approximately 100m across the Haut-Katanga, Haut-Lomami, Ituri, Kasaï, Kasaï-Oriental, Lomami and Sud-Kivu provinces in the Democratic Republic of the Congo (DRC). The estimates comprise total population counts created using a Bayesian statistical model and post-hoc breakdowns in 40 age and sex groups. The main input data were derived from a dedicated microcensus survey carried out in the targeted provinces throughout March and April 2021. The microcensus was led by the Flowminder Foundation, the École de Santé Publique de Kinshasa, the WorldPop Research Group at the University of Southampton and the Bureau Central du Recensement, which is part of the Institut National de la Statistique of the DRC. 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 microcensus but their consistency may be impacted by the accuracy of the building footprints, particularly in the areas where the satellite imagery used for automatic delineation was outdated. These data were produced by the WorldPop Research Group at the University of Southampton as part of the GRID3 Mapping for Health Project. This project was delivered under the leadership of the Ministry of Public Health, Hygiene and Prevention of the DRC and funded by Gavi, the Vaccine Alliance (RM 867204 20A2). The project was led by the Flowminder Foundation and the Center for International Earth Science Information Network (CIESIN) at Columbia University, in collaboration with the WorldPop Research Group at the University of Southampton and national partners including, but not limited to, the École de Santé Publique de Kinshasa and both the Bureau Central du Recensement and the Institut National de la Statistique. This work was a continuation of the 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 62716). The production of these data was led by Gianluca Boo (WorldPop](https://www.worldpop.org/) ) with support from Roland Hosner (Flowminder Foundation), Pierre Z Akilimali (École de Santé Publique de Kinshasa), Edith Darin (WorldPop), Heather R Chamberlain (WorldPop), Warren C Jochem (WorldPop), Patricia Jones (WorldPop), Roger Shulungu Runika (Institut National de la Statistique), Henri Marie Kazadi Mutombo (Bureau Central du Recensement), 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 citation:__ _G Boo, R Hosner, PZ Akilimali, E Darin, HR Chamberlain, WC Jochem, P Jones, R Shulungu Runika, HM Kazadi Mutombo, AN Lazar and AJ Tatem. 2021. Modelled gridded population estimates for the HautKatanga, Haut-Lomami, Ituri, Kasaï, Kasaï-Oriental, Lomami and Sud-Kivu provinces in the Democratic Republic of the Congo (2021), version 3.0. WorldPop, University of Southampton, Flowminder Foundation, École de Santé Publique de Kinshasa, Bureau Central du Recensement and Institut National de la Statistique. doi:10.5258/SOTON/WP00720_

A 1-kilometre population raster for the Democratic Republic of the Congo, published by Orion Terrametrics. Each cell holds a population value.

Spatial distribution of the population of Congo, showing where people live and in what densities across the country. Use this layer for understanding settlement patterns, sizing infrastructure, planning service delivery, and conducting demographic analysis.

Localités Les localités de la RDC sont issues au de la comparaison de deux bases pour lesquelles les doublons ont été supprimés. Des relevés GPS ainsi que des numérisation sur images satellites ont été réalisés par différentes acteurs présent en RDC et viennent compléter le fichier initial. Des ajouts se font régulièrement (2010). Sourced from Référentiel Géographique Commun

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Statistiques des populations : population désagrégée par âge et sexe pour toutes les zones de santé du pays.

No description yet.

No description yet.

No description yet.

No description yet.

Gridded raster of population distribution for the Democratic Republic of the Congo, published by WorldPop (University of Southampton). Each cell value measures the population count within that grid cell. The data cover the Democratic Republic of the Congo for the year 2030.

Gridded raster of population distribution for the Democratic Republic of the Congo, with cell values measuring population. Published by WorldPop (University of Southampton). The data cover the Democratic Republic of the Congo for the year 2030.