Central African Republic GIS data

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Retirement Notice: This item is in mature support as of October 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Central African Republic Prefecture Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. Other administrative boundaries for this country are also available: Country Region Subprefecture

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

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

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

Locations of refugee camps, IDP sites and displacement settlements in Central African Republic. Use this layer for humanitarian response planning, protection monitoring, service delivery to displaced populations, and humanitarian needs assessment.

CAR Shapefile Contour line - Equidistant 10m

Shapfile des cours d'eau en République Centrafricaine (fleuves, rivière...)

Aérodromes en Centrafrique

Central African Republic administrative level 0-3 and Bangui level 4 gazetteer, shapefiles, geodatabase, and geoservice. COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. These layers are suitable for database or GIS linkage to the Central African Republic - Subnational Population Statistics tables using the ADM0, ADM1, ADM2, and ADM3_PCODE fields. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

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.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface water in Basse-Kotto Prefecture of Central African Republic as observed from Sentinel-1 imagery acquired on 17 November 2019. Within the analysed extent of about 425 km2, a total about 2 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important note: Flood analysis from SAR Sentinel-1 images may significantly underestimate the presence of standing waters in built up and/or densely vegetated areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface water in Ouaka Prefecture of Central African Republic as observed from Sentinel-1 imagery acquired on 17 November 2019. Within the analysed extent of about 1,950 km2, a total about 4 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important note: Flood analysis from SAR Sentinel-1 images may significantly underestimate the presence of standing waters in built up and/or densely vegetated areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface water in Ouaka and Basse-Kotto Prefecture of Central African Republic as observed from Sentinel-1 imagery acquired on 17 November 2019. Within the analysed extent of about 940 km2, a total about 3 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important note: Flood analysis from SAR Sentinel-1 images may significantly underestimate the presence of standing waters in built up and/or densely vegetated areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface waters in Basse-Kotto Prefecture of the Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 390 km2, a total of about 7 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT. Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface water in Ouaka Prefecture of Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 2,000 km2, a total about 10 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT. Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected water surface in Central African Republic and Democratic Republic of The Congo as observed from Sentinel-1 imagery acquired on 18 November 2019. Within the analysed extent of about 760 km2, a total about 6 km2 of land appear to be flooded in Basse-Kotto Prefecture of Central African Republic and about 1 km2 of land appear to be flooded in Nord-Ubangi Province of Democratic Republic of The Congo. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important note: Flood analysis from SAR Sentinel-1 images may significantly underestimate the presence of standing waters in built up and/or densely vegetated areas due to backscattering of the radar signal.

**UNOSAT code: FL20191028CAF** This map illustrates satellite-detected surface water in Ouaka and Basse-Kotto Prefectures of the Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 970 km2, a total about 9 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT. Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Central African Republic. 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

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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

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

This geospatial dataset represents the area of intervention of the Projet d’Appui à la Résilience et à la Sécurité Alimentaire et Nutritionnelle (PARSANKO) in the Central African Republic, funded by the African Development Bank (AfDB). The project aims to improve food and nutrition security and strengthen the resilience of vulnerable populations through sustainable agriculture, climate adaptation, and livelihood support. The dataset is designed for integration with other geospatial layers under the FAO Hand-in-Hand Initiative (HiHI), supporting evidence-based investment planning and targeted interventions in the agricultural sector. **Data creation:** 2025-07-30 **Data publication:** 2025-08-04 **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** The project area of intervention was created with information from the FAO TCP/CAF/3904/LERSA, RAPPORT FINAL, ETUDE D’IDENTIFICATION ET DE CARTOGRAPHIE DES CHAINES DE VALEURS DES FILIERES PRIORITAIRES ET DES PARTENAIRES POTENTIELS ET DE LEURS DOMAINES D’INTERET, from the 31st March 2025. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

Vector polygon geometry features representing zones with high potential for intensive tilapia farming using cages in water bodies. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis (GIS-MCDA) suitability assessment. Intensive systems are characterized by high densities and artificial feed input. The score is achieved by processing sub-model outputs characterizing biophysical, infrastructure, and demand factors Criteria: a. Market accessibility (large urban areas). b. Crop input (CropAggGAEZ) availability of agricultural by-products. c. Livestock input (weighted animal density aggregation) availability of livestock byproducts. d. Accessibility to ports - considers that high quality/performance tilapia feed is still imported. e. Tilapia potential yield. The location score is a simple arithmetic weighted sum of normalized/scaled grids (0 to 100): (“Accessibility MajorUrbanAreas” X 0.40) + (“CropsInput” X 0.15) + (“LivestockInput” X 0.15) + ("Accessibility Ports" X 0.15) + (Tilapia Yield x 0.15) Constraints: a. Protected areas. The Global Surface Water seasonality (seasonality = 12 months) to identify permanent water. Based on the literature the minimum area considered for tilapia cage farming is 5 acres. The final zoning follows the approach: 1. Clipping - Location score mapping is clipped using a 1km buffer from permanent waters with >5ac area polygon layer. 2. Zonal statistics extracted with the maximum (max) score value from the grid layer to the buffer layer polygons. 3. The polygons are selected and displayed for the top 95th percentile.

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

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