Gambia GIS data

Water resources and water infrastructure in Gambia 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.

Road surface line features in Gambia, published by Orion Terrametrics. Each feature represents a road segment from OpenStreetMap, with attributes covering surface type, smoothness, road class, width, lanes, and bridge status. The data includes 61,783 features.

Spatial distribution of the population of Gambia, 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.

A 100 m resolution raster of population data for Gambia for 2015, published by Orion Terrametrics. Each cell value represents the population count or density within that grid cell.

Point locations of health facilities in Gambia 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.

Third-level administrative divisions of Gambia such as sub-districts or wards. Use this layer for fine-grained spatial analysis, community health planning, and detailed humanitarian response.

Locations of seaports, harbours and maritime infrastructure in Gambia. 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 Gambia 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.

Point locations of educational facilities across Gambia, 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.

Point locations of named geographic places in Gambia, including streams, populated places and other features. Each feature records a name, coordinates, feature class and code, country, administrative unit, population, elevation and timezone. Published by Orion Terrametrics.

Gambia administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 8 nan - Admin 2: 49 nan - Admin 3: 115 nan Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 01 September 2022: valid for use by the humanitarian community - 17 August 2022: last edit to the dataset before publishing - 17 August 2022: boundaries created by the source 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)

Administrative boundary lines of Gambia as linear features. Use this layer for cartographic production and boundary demarcation visualization.

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. Gambia District 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 LocalGovernmentArea

Spatial distribution of estimated pregnancies across Gambia at 1km resolution. Each grid cell shows the estimated number of pregnancies including live births, stillbirths and abortions. Use this layer for maternal health planning, antenatal care programme design, health facility placement, and reproductive health analysis.

A 1 km resolution raster of population data for Gambia for 2020, published by Orion Terrametrics. Each cell value represents the population count or density within that grid cell.

The national boundary of Gambia, 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 Gambia.

Point locations of airports, airstrips and airfields in Gambia. Each feature includes the facility name, type, elevation and runway information where available. Use this layer for aviation planning, accessibility analysis, emergency response logistics, and transport network mapping.

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. Gambia Local Government Area 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 District

First-level administrative divisions of Gambia such as regions, provinces or states. Use this layer for regional analysis, planning, resource allocation, and thematic mapping at the provincial scale.

Travel time in minutes to the nearest health facility in Gambia, showing healthcare accessibility across the country by motorised transport or walking. Use this layer for healthcare access analysis, facility gap identification, health equity studies, and humanitarian planning.

Second-level administrative divisions of Gambia such as districts or departments. Use this layer for local government analysis, service delivery planning, and community-level spatial work.

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.

Gambia administrative level 0-2 edge-matched 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 Gambia - Subnational Population Statistics tables using the ADM0, ADM1, and ADM2_PCODE fields. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

**UNOSAT code: FL20220801GMB** This map illustrates satellite-detected surface waters in Kombo North/St Marie District, Brikama Region, Gambia as observed from a Sentinel-2 image acquired on 5 Aug 2022 at 11:33 local time. Within the analyzed area of about 2,340 hectares, about 22 hectares of lands appear to be flooded and about 3 km of road appear to be likely affected by the flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

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: FL20220801GMB** This map illustrates satellite-detected surface waters in Satellite detected water in Banjul, Kanifing, and Brikama Regions, Gambia as observed from a Sentinel-2 image acquired on 10 Aug 2021 at 18:18 local time. Within the analyzed cloud free zones of about 960 km2, about 26 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the districts of Kanifing with ~ 7,300 people, Kombo North/St Marie with ~ 7,000 and Kombo South with ~ 4,100 people This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

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This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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 raster dataset represents top location score areas suitable for groundnut storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-05-13 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Groundnut (The Gambia - ~ 500 m)

The raster dataset represents top location score areas suitable for maize storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-05-13 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Maize (The Gambia - ~ 500 m)

The raster dataset represents top location score areas suitable for oil palm storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-05-13 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Crop Storage Final Location: Oil Palm (The Gambia - ~ 500 m)