Democratic Republic of the Congo GIS data
＋ Follow new data133 datasets.
Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. 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: waterway, water, natural - Part of the country exports on HDX by HOT
Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access 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: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT
Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. 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: railway - Part of the country exports on HDX by HOT
Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban 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: building - Part of the country exports on HDX by HOT
Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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, building - Part of the country exports on HDX by HOT
Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic 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: amenity - Part of the country exports on HDX by HOT
Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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, healthcare - Part of the country exports on HDX by HOT
Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT
Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - Part of the country exports on HDX by HOT
The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.
The Democratic Republic of the Congo Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021
Congo, The Democratic Republic of the Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2018
Forward-looking analysis representing the most likely food security outcomes for the near term (April 2023 - May 2023) and medium term (June 2023 - September 2023) periods.
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.
Vector dataset representing top score, optimal/recommended locations, for Nile tilapia cage farming systems in the Democratic Republic of the Congo. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Evaluation suitability assessment. Intensive systems are characterized by high fish densities and the use of artificial feed. Recommended locations require the presence of permanent waters with a surface area larger than 5 acres. The score is determined by processing outputs from sub-models that describe biophysical, infrastructure, and demand factors: Criteria considered: a. Market accessibility, considering the weighted population and AWI in urban areas. b. Crop input the availability of agricultural by-products. c. Livestock input - based on the weighted animal density aggregation. d. Accessibility to ports - high-quality tilapia feed is often imported in large quantities. e. Potential yield of tilapia. The score is calculated as a simple arithmetic weighted sum of normalized or scaled grids, theoretically ranging from 0 to 100: (“Accessibility MajorUrbanAreas” X 0.40) + (“CropsInput” X 0.15) + (“LivestockInput” X 0.15) + (Accessibility to ports x 0.15) + (Tilapia Yield x 0.15) **Data creation:** 2023-11-27 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets: a. Market Accessibility (urban areas) - OpenStreetMap and Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km. (Urban areas = pop density>1500 habitants/km² AND area>25km²) b. Water Balance: WaPOR (https://wapor.apps.fao.org/catalog/2) precipitation and evapotranspiration monthly time-series from 2009 to 2020 for calculating a mean water balance layer modelling values: (Precipitation X 1.1) - (evapotranspiration X 1.3) c. Input crops - IFPRI Global Spatially-Disaggregated Crop Production Statistics Data for 2017 (MAPSPAM) - https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a d. Input livestock – GLW4 (https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704) : weighted animal density aggregate **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**
Water resources and water infrastructure in Congo including rivers, lakes, boreholes, wells and water points. Use this layer for water access analysis, WASH programme planning, drought monitoring, flood risk assessment, and water resource management.
Point locations of health facilities in Congo including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.
A 1-kilometre population raster for the Democratic Republic of the Congo, published by Orion Terrametrics. Each cell holds a population value.
Third-level administrative divisions of DR Congo such as sub-districts or wards. Use this layer for fine-grained spatial analysis, community health planning, and detailed humanitarian response.
République Démocratiques du Congo, Limites des provinces (admin level 2)
Locations of seaports, harbours and maritime infrastructure in Congo. Includes commercial ports, fishing harbours and ferry terminals. Use this layer for maritime transport planning, trade logistics analysis, coastal zone management, and shipping route mapping.
Road and transport network of DR Congo showing the classification and extent of road infrastructure. Includes primary roads, secondary roads, tracks and footpaths where available. Use this layer for route planning, accessibility analysis, transport modelling, and logistics planning.
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)](https://data.humdata.org/organization/ocha-dr-congo). Quality assured, configured, and published by [OCHA Field Information Services (FIS)](https://data.humdata.org/organization/ocha-fiss) and [HDX](https://data.humdata.org/organization/hdx). Part of the dataset: [Global Subnational Administrative Boundaries](https://data.humdata.org/dataset/cod-ab-global)
Point locations of educational facilities across Congo, including primary schools, secondary schools and other learning institutions. Each feature records the facility name, type and administrative location. Use this layer to map education access, identify underserved areas, plan school placement, and analyse catchment areas and travel distances to schools.
Second-level administrative divisions of DR Congo such as districts or departments. Use this layer for local government analysis, service delivery planning, and community-level spatial work.
geoBoundaries ADM2 boundaries for Democratic Republic of the Congo: 189 units, representing 2019. Source: Referenciel Géographique Commun, OCHA DR Congo.
The national boundary of DR Congo, defining the outermost extent of the country's territory. Use this layer as a base for national-scale maps, spatial analysis, and geographic context in any GIS project covering DR Congo.
Point locations of named places in Sudan, including wadis and wells. Each row is one place with its name, coordinates, feature class and code, administrative units, population, elevation and timezone. Published by Orion Terrametrics.
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.
Point locations of towns, cities, villages and populated places in DR Congo. Each feature includes the place name, type and population where available. Use this layer for location referencing, travel planning, service delivery mapping, and spatial analysis at the settlement level.
Quartiers (385) de 12 villes de la République démocratique du Congo, extraits de la base de données OpenStreetMap. Quarters (385) from 12 cities of the Democratic Republic of the Congo, extracted from the OpenStreetMap database.
GSI’s Human Geography Database for the South Kivu Province of the Democratic Republic of Congo provides tribal hierarchy data in a geospatial format and identifies approximate locations for specific tribes as well as their tribal hierarchies.
GPS points for health facilities in Aire de Santé Tamende, Zone de Santé Beni, North Kivu, DRC
Les 11 anciennes provinces administratives de la République démocratique du Congo, extraites de la base de données OpenStreetMap. En 2015, celles-ci ont été remplacées par la nouvelle configuration à 26 provinces. The 11 old administrative provinces of the Democratic Republic of the Congo, extracted from the OpenStreetMap database.