Democratic Republic of the Congo GIS data

satellite imagery by Satellogic, acquired 2026-05-22 to 2026-05-22, 50.1 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM0 boundaries for Democratic Republic of the Congo: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

geoBoundaries ADM1 boundaries for Democratic Republic of the Congo: 26 units, representing 2017. Source: OpenStreetMap, Wambacher.

Quartiers des communes de Goma (7) et Karisimbi (11) de la ville de Goma, en République démocratique du Congo, extraites de la base de données OpenStreetMap. Quarters of the Goma (7) and Karisimbi (11) communes of Goma city in the Democratic Republic of the Congo, extracted from the OpenStreetMap database.

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.

République démocratique du Congo (RDC), Localités, lieux habités Points : Interactive Online Map and Tabular spreadsheet * Localities populated settlements * OSM amenity IN ('places') * Overpass API / OpenStreetMap Database Variables * osm_id, lat (latitude), lon(longitude), place[city, town, suburb, quarter, village, hamlet, localities, isolated_dwelling], abandoned_place, name

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

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_

The aim of this work is to generate two friction surfaces for modeling physical accessibility in the Democratic Republic of the Congo: one incorporating walking speed only, and another combining walking and motorized travel speeds. Motorized speeds are applied exclusively on roads that are present and passable by vehicles; in all other areas, walking speeds are used. The GRID3 COD - Travel Time Friction Surface v1.0 dataset is a spatial data layer in tiff format. The datasets contain 2 layers: (1) GRID3_COD_walk_travel_time_friction_surface_v1_0.tif - Applies walking speeds across the entire surface, adjusted for slope and elevation. (2) GRID3_COD_mix_travel_time_friction_surface_v1_0.tif - Applies motorized speeds on roads and walking speeds in all other areas. The resolution is 30 m. The unit of measurement in both surfaces is minutes per meter. Suggested Dataset Citation Center for Integrated Earth System Information (CIESIN), Columbia University. 2026. GRID3 COD - Travel Time Friction Surface v1.0. New York: Columbia University. URL. Accessed DAY-MONTH-YEAR.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Democratic Republic of the Congo. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Democratic Republic of the Congo. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

République démocratique du Congo (RDC), Établissements éducatifs Points + Polygons : Interactive Online Map and Tabular spreadsheet (For multipolygons lat,lon=centroid) * First Column *Thematic* describes line Feature content : school, college, university * Objets Institutions de la santé (écoles, universités, collèges) Columns : thematic, osmtyhpe, lat (latitude), lon (longitude), osm_id (unique point id), amenity, school:FR, name, name:fr, name:en, operator:type, addr:housenumber, addr:street, addr:city, phone, building, source, source:data, source:date, source:name, source_ref, survey:date, note, attribution * OpenStreetMap data extraction from * ETL2HDX Procedures

République démocratique du Congo (RDC), Infrastructures, Mines et Fabrication Points + Lines + Polygons OSM Features Interactive Online Map - NOTE : WORK IN PROGRESS - Will be revised with the thematic variable describing more in detail group of facilities + adding Water facilities Thematic Variable = Infrastructures, Mining, Fabrication The file contains Industrial facilities, Mining, quarry, pipelines, Power lines and stations, communication towers and piers. The mkeys variable describes which OSM major keys are used to describe the facility. Variables : osmtype(node, way, relation) geomtype(point, line, polygone) , osm_id, lat, lon, thematic, mkeys (osm major keys), man_made, landuse, power, industrial, embankment, content, product, substance, location, name, operator, operator:type, private, address, addr:housenumber, addr:street, addr:city, building, building:levels, building:material, building:condition, building:status, note, layer, other_tags name, name:en, name:fr, operator, operator:type, natural, aerialway, aerodrome, aeroway, runway, taxiway, apron, helipad, heliport, iata, icao, public_transport, rail, railway, ref, route, network, station, service, shelter, harbor, bus, highway, bridge, tunnel, landuse, surface, building, building:levels, building:material, building:condition, building:status, building:min_level, address, addr:housenumber, addr:street, addr:city, layer, private, source_ref, source, source:name, source:date, source:data, source_ref:1, survey:date, timezone, toilets, type, voltage, weelchair, wikidata, wikipedia OSM Filter OSM features : thematic in ( 'Infrastructures, Mining, Fabrication');

République démocratique du Congo (RDC), Réseau routier principal Lines + Polygons : Main roads Network Interactive Online Map OSM Roads Columns: osm_id, highway, name, layer, bridge, tunnel, oneway, ref , route, junction, lanes, width, maxspeed, surface, practicability, smoothness, condition, geometry OSM Filter WHERE highway in ('motorway','motorway_link','trunk','trunk_link','primary','primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') OR route='ferry' OR amenity in ('ferry_terminal') OR ( (bridge is not null OR tunnel is not null ) AND highway in ('motorway','motorway_link','trunk','trunk_link','primary', 'primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') ) The second part of the Road network is available from the Local Roads dataset

République démocratique du Congo (RDC), Transport Points + Lines + Polygons OSM Features Interactive Online Map - First Column Thematic Variable shows line content : Airport facility, Airport runway, Helipad, Harbour, Shipyard, Rail line, Rail yard, Transport Station & Stop, Ferry Terminal, Transport Ferry Route, Transport Route Itinaries Aeroways (Airports) Railways Aerialways Railway, Tram Routes Variables : name, name:en, name:fr, operator, operator:type, natural, aerialway, aerodrome, aeroway, runway, taxiway, apron, helipad, heliport, iata, icao, public_transport, rail, railway, ref, route, network, station, service, shelter, harbor, bus, highway, bridge, tunnel, landuse, surface, building, building:levels, building:material, building:condition, building:status, building:min_level, address, addr:housenumber, addr:street, addr:city, layer, private, source_ref, source, source:name, source:date, source:data, source_ref:1, survey:date, timezone, toilets, type, voltage, weelchair, wikidata, wikipedia OSM Filter OSM features : thematic in ( 'Rail yard', 'Rail line', 'Transport Route Itinary', 'Helipad', 'Airport Runway', 'Airport Facility', 'Shipyard', 'Ferry Terminal', 'Transport Station, Stop')

République démocratique du Congo (RDC), Établissements de santé Points + Polygons : Interactive Online Map and Tabular spreadsheet (For multipolygons lat,lon=centroid) * First Column *Thematic* describes line Feature content : hospital, doctors, clinic, pharmacy, dentist,veterinary * Objets Institutions de la santé (hopitaux, médecins, cliniques, pharmacies, dentiste) * OSM amenity IN ('doctors','clinic','hospital','pharmacy','dentist') Columns :thematic, osmtype, lat (latitude), lon (longitude) osm_id (unique point id), amenity, name, name:fr, name:en, health_facility:type, beds, health_services:description, heliport, operator, operator:type, private, address, addr:housenumber, addr:street, addr:city, building, building:levels, building:material, building:condition, building:status, note, layer, other_tags * OpenStreetMap data extraction from *ETL2HDX Procedures

**ADAM ID: **FL-20250415-COD-00 A flood occurred on Apr 15 2025 in Congo, The Democratic Republic of the. It affected an estimated 182007 people and 2603 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

Democratic Republic of the Congo administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 26 Province - Admin 2: 164 Territory / Territorie - Admin 3: 519 Health zone / Zone de santé Note: Officially, there are 145 administrative units. However, to align with the admin3 level—which in practice represents health district areas rather than administrative boundaries—19 admin2 units corresponding to the main cities have been created. Dates associated with this dataset: - 20 December 2024: dataset reviewed for accuracy and completeness - 11 September 2019: valid for use by the humanitarian community - 12 September 2019: last edit to the dataset before publishing - 12 September 2019: boundaries created by the source Contributed by OCHA Democratic Republic of the Congo (DRC). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

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.

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

The Democratic Republic of the Congo Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

The Democratic Republic of the Congo Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The Democratic Republic of the Congo Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

The Democratic Republic of the Congo Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The Democratic Republic of the Congo Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. The Democratic Republic of the Congo Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

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_

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT