Africa GIS data

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

Point locations of named places in Guinea, including streams and other feature types. Each feature records a name, alternate ASCII name, coordinates, feature class and code, country, administrative units, population, elevation and timezone. Published by Orion Terrametrics.

Point locations of named places in Seychelles, including shoals, points and reefs. Each row is one place with its name, coordinates, feature class and code, administrative units, population, elevation and timezone. Published by Orion Terrametrics.

Point locations of named places in Somalia, including islands and hills. Each row is one place with its name, coordinates, feature class and code, administrative units, population, elevation and timezone. Published by Orion Terrametrics.

Named inland water bodies of Africa including lakes, lagoons, and reservoirs derived from the Digital Chart of the World at 1:1,000,000 scale. Each feature includes the water body name, type classification, and area in square kilometres. Use this layer for water resource mapping, fisheries management, environmental monitoring, climate analysis, and inland water body studies across Africa. No additional information provided.

Plasmodium falciparum parasite rate (PfPR, ages 2-10) modelled at ~5 km for 2020, subset to Africa. Malaria Atlas Project global PfPR coverage (Explorer__2020_Global_PfPR) clipped to lon -25..60, lat -40..40. CC-BY.

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

Total plant-available water holding capacity (mm) of the soil whole earth fraction (including coarse fragments), summed over the Effective Root Zone Depth for Maize, mapped at 1km resolution

Spatial distribution of the population of Guinea, 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 health facilities in Malawi 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.

Extractable Calcium (Ca) content of the soil fine earth fraction in mg/kg (ppm) as measured according to the soil analytical procedure of Mehlich 3 and spatially predicted for 0-30 cm depth interval at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.

Extractable Manganese (Mn) content of the soil fine earth fraction in mg/kg (ppm) as measured according to the soil analytical procedure of Mehlich 3 and spatially predicted for 0-30 cm depth interval at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.

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

National-level administrative boundaries for countries in West and Central Africa, including Burkina Faso, Benin, and the Democratic Republic of the Congo. Each feature represents one country and includes names, codes, area, and validity dates. Published by Orion Terrametrics.

River network of Africa derived from the WWF HydroSHEDS drainage direction and stream network layers at 15 arc-second resolution. Includes major rivers, tributaries and stream networks with basin and sub-basin attribution, Strahler stream order, and upstream area. Use this layer for hydrological analysis, water resource mapping, flood risk assessment, infrastructure planning near waterways, and environmental impact studies across Africa. No additional information provided.

Electricity transmission and distribution grid network across Africa including all existing and planned lines, ranging from sub-kilovolt to 700 kV extra-high-voltage lines. Compiled from multiple sources including the Africa Infrastructure Country Diagnostic, OpenStreetMap, the West African Power Pool GIS database, World Bank project archives, and country utility data. Use this layer for energy access analysis, grid infrastructure planning, electrification studies, renewable energy siting, and power sector investment analysis across Africa. No additional information provided.

The national boundary of Guinea, 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 Guinea.

Seasonal rainfall accumulation anomaly for East Africa derived from CHIRPS data, showing deviation from the 1981-2010 historical average in millimetres. Use this layer for drought early warning, seasonal forecast validation, agricultural planning, and humanitarian response in the Horn of Africa.

Volumetric coarse fragments content (v%) of the soil whole earth, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution

Date des données : 24 Septembre 2026 Fréquence de mise à jour : Quotidienne Source des données : Géorisques Ce jeu de données fait partie de la thématique des données de pollution industrielle, publiées dans le cadre des analyses de risques technologiques. Il correspond aux Secteurs d'informations sur les sols (SIS). Ceux-ci comprennent les terrains où la connaissance de la pollution des sols justifie, notamment en cas de changement d’usage, la réalisation d’études de sols et la mise en place de mesures de gestion de la pollution pour préserver la sécurité, la santé ou la salubrité publique et l’environnement. Ils sont mis à disposition du public après consultation des mairies et information des propriétaires. L’article L.125-6 du code de l’environnement prévoit que l’État élabore, au regard des informations dont il dispose, des Secteurs d’Information sur les Sols (SIS). Le décret n° 2015-1353 du 26 octobre 2015 relatif aux secteurs d’information sur les sols prévus par l’article L.125-6 du code de l’environnement précise les modalités d’application, notamment les modalités de création et de diffusion des SIS.   Veuillez noter que certaines données présentent des incohérences géographiques. Celles-ci étant déjà présentes dans les données sources, elles ont été conservées telles qu’à l’origine. Les données ont été adaptées par Esri France. Elles sont projetées en Web Mercator et couvrent la France entière. Des métadonnées et une documentation sont disponibles sur le site web. Les données sources sont fournies en format shapefile. Cette couche d’entités est offerte par l’équipe GéoData d’Esri France. Les données sont optimisées pour l’usage dans la plateforme ArcGIS. Plus d’information sur les offres sur esrifrance.fr/contenus. Contactez-nous avec des questions ou des commentaires via livingatlas@esrifrance.fr.

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. Guinea Region 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 Prefecture

High-resolution vector coastline and shoreline dataset for Africa at 30-metre resolution, derived from Landsat satellite imagery. Includes continental coastlines and major island shorelines. Use this layer for coastal zone management, maritime boundary analysis, coastal erosion monitoring, infrastructure planning, and environmental assessments across Africa. No additional information provided.

Spatial distribution of estimated pregnancies across Guinea 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.

Plant available water holding capacity (v%) of the soil fine earth fraction, with field capacity defined at h=200 cm or pF 2.3, aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

Point locations of named places in Seychelles, including shoals, points, reefs and other features. Each point records a name, coordinates, feature class and code, country, administrative unit, population and timezone. Published by Orion Terrametrics.

These shapes design administrative levels 1 and 2 plus the localities of the Lac Chad Basin affected by the crisis

This dataset contains cell signal strength data collected by 100+ Red Cross volunteers across West Africa (Guinea, Liberia, and Sierra Leone border regions). Data have been cleaned and converted to a consistent scale (in dBm). This package contains a zipped shapefile, QGIS style file, and a text file with the complete data. Procedure is documented here: https://github.com/AmericanRedCross/workflows/blob/master/opensignal-data.md

The dataset has been developed by merging Admin2 geodata of the nine countries of the Sahel region coming from several sources (SALB, governments, WFP....).

EbolaGeonode was a partnership platform for sharing geospatial data, analysis and maps related to the Ebola emergency response. The platform was intended to minimize the time that GIS analysts spend locating up-to-date data. Users were able to make maps on the fly, view metadata, and access the reports behind GIS layers. Curators worked to ensure that the layers were recent, clean, useful, and legally and technically open.

Limites de la République Démocratique du Congo et de 20 pays avoisinants, extraites de la base de données OpenStreetMap et mises à disposition pour faciliter la cartographie.

This master list of health facilities was developed from a variety of government and non-government sources from 50 countries in sub-Saharan Africa. It uses multiple geocoding methods to provide a comprehensive spatial inventory of 98 745 public health facilities. Each data record represents a health facility and has 8 descriptive variables – Location identifiers including: country, first level administrative division, latitude, longitude and LL source (source of the coordinates). Coordinates are rounded off to four decimal places for uniformity, allowing an accuracy of 5–10 metres in decimal degrees coordinate format. This geocoded master facility list has been made publicly and freely available through both the figshare repository and through the World Health Organization’s Global Malaria Programme in Microsoft Excel format.

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for April-June 2013 * southernafrica201304_ML2 Most likely food security outcome for July-September 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * WA201304_ML1 Most likely food security outcome for April-June 2014 * WA201304_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

Aggregated data on food insecure population in Kenya, Ethiopia, Uganda, Sudan, South Sudan, Rwanda, Burundi, Djibouti and Somalia from Dec 2010 to Jan 2015

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for January-March 2013 * southernafrica201304_ML2 Most likely food security outcome for April-June 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for April-June 2013 * xx201304_ML2 Most likely food security outcome for July-September 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)