Skip to main content

News

News
This dataset represents a geographic clip to Africa of the World Database on Protected Areas (WDPA), which is the authoritative and most complete global dataset on terrestrial and marine protected areas. The parent database is a joint initiative between the UN Environment Programme (UNEP) and the International Union for Conservation of Nature (IUCN), managed by the UNEP World Conservation Monitoring Centre (UNEP-WCMC). Information is submitted and verified by international secretariats, national and regional governments, NGOs, communities, and landowners. This spatial dataset has been specifically filtered and clipped to include only protected areas located within the terrestrial and marine boundaries of the African continent and its associated island nations. The dataset comprises both spatial data (GIS boundaries) and attribute data (descriptive information) for protected sites across Africa. Key attributes include site name, designation type (national, regional, or international), governance model, IUCN management category, marine/terrestrial status, and legal establishment date. The WDPA is the primary global mechanism used to track progress toward international area-based conservation targets. This African subset is widely used across various sectors for: Conservation & Science: Ecological research, protected area monitoring, and biodiversity assessments specific to African biomes. Corporate & Financial Risk: Environmental impact assessments and supply chain risk analyses for infrastructure and commercial projects operating in Africa. Policy & Reporting: Informing national and international decision-making and tracking regional progress toward the UN Sustainable Development Goals and the Kunming-Montreal Global Biodiversity Framework. The global WDPA dataset is updated and released on a monthly basis through the Protected Planet platform. This clip was extracted on May 2026.
This spatial dataset contains the geographic boundaries and ecological evaluations of 45 Key Landscapes for Conservation and Development (KLCDs) across Sub-Saharan Africa. Building on the Key Landscapes for Conservation (KLCs) identified in the EU report “Larger than elephants”, the NaturAfrica initiative is rolled out across these key biodiversity and development landscapes. NaturAfrica's efforts are concentrated on ‘mega-landscapes’—biogeographical regions identified as crucial for both conservation and development. These regions encompass: The forest ecosystems of the Congo Basin; Landscapes of transhumance pastoralists in North Cameroon, the Central African Republic, and Chad; Guinean forests of West Africa; Savannahs in the Sudano-Sahelian zone of West Africa; Savannahs and watersheds of the East Africa rift; Transfrontier conservation areas of Southern Africa. The layer was developed to support the prioritization of intervention areas for the second phase of this initiative, which combines conservation with sustainable job creation. It provides a spatially explicit characterization of ecological value across these landscapes, evaluated and attributed based on five core ecological and environmental dimensions: Threatened Species Richness: Concentration and diversity of threatened species. Species Endemicity: Presence of endemic species unique to the specific geographic region. Ecosystem Integrity: The integrity of protected and conserved areas, accounting for degrees of human modification and habitat fragmentation. Ecological Connectivity: The degree of connectivity and spatial linkages between protected and conserved areas. Ecosystem Services: Provisioning and regulating services, with a specific focus on carbon storage and water services. Purpose The layer was created to inform the EC Directorate General for International Partnerships (DG INTPA) and policymakers in selecting and prioritizing funding intervention areas based on specific targets (e.g., species conservation, connectivity, or water security) within different biogeographical regions. It serves as a spatial decision-support tool to align with the European Green Deal and the EU Biodiversity Strategy for 2030. Geographic Extent Region: Sub-Saharan Africa Coverage: 45 distinct Key Landscapes for Conservation and Development (KLCDs) Keywords Thematic: Biodiversity Conservation, NaturAfrica, Ecosystem Services, Protected Areas, Habitat Connectivity, Species Endemicity, European Green Deal, Ecosystem Integrity, Transfrontier Conservation, Mega-landscapes. Spatial: Sub-Saharan Africa, Congo Basin, West Africa, East Africa, Southern Africa, Sudano-Sahelian zone. Lineage / Data Source This dataset is derived from the technical assessment conducted for the European Commission Knowledge Centre for Biodiversity (KCBD) .
The dataset is the result of a crop simulation and machine learning exercise to evaluate the irrigation potentials in sub-Saharan Africa. Samples of points (representative for each Agro-ecological zone in Africa) have been selected from a cropland map (from Tubliello et al., 2023) and, by using the DSSAT crop model fed with soil information from the ISRIC project (https://isric.org/projects/soil-information-system-for-africa-soils4africa/) and 10 years of daily climate data (from NASAPOWER), simulations have been conducted in each location under rainfed conditions and optimal irrigation. This last has been achieved y letting the crop model add irrigation water each time a water stress was perceived. Other management practices have been set as realistic as possible, particularly the amount of provided fertilization has been kept low to represent current conditions. Yields have been finally averaged along the different years of simulations to generate expected yields. Finally, by using the XGboost algorithm fed with the same soil and climatic variables used for the crop model, yields under both rainfed and irrigation conditions have been imputed to all remaining cropland locations in Sub-Saharan Africa. What is presented here are the yield increases, as percentages over rainfed yields, obtainable in each location for the four simulated cereals: maize, millet, rice and sorghum.
This layer shows the percentage of children under 6 months who are exclusively breastfed, meaning they receive only breast milk without any additional food or drink. Exclusive breastfeeding is a key determinant of child survival, growth, and development.
This layer provides estimates of the percentage of children aged 2 to 10 years (PfPR2–10) with detectable Plasmodium falciparum parasites in 2022. The estimates are generated using geostatistical models based on point prevalence surveys, routine surveillance data, and a wide range of geospatial covariates representing mosquito habitat and environmental conditions. The data are available annually from 2000 onward, covering all malaria-endemic countries, at a spatial resolution of 5 × 5 km.
This layer represents the estimated travel time (in hours) by foot to the nearest primary school. The accessibility map is generated using a well-established geospatial methodology that integrates road and rail networks, land cover, and topographic features. The resulting gridded "friction surface" represents the time required to traverse each ~1 km × 1 km pixel of Africa.
This layer represents the estimated travel time (in hours) by foot to the nearest healthcare facility. The underlying methodology is described in Weiss et al. (2020), which leverages major data collection efforts from OpenStreetMap, Google Maps, and academic sources to compile the most comprehensive global inventory of healthcare facility locations to date. The approach is based on the creation of friction surfaces that quantify the time required to traverse each ~1 km × 1 km pixel of the Earth's surface.
This layer shows the estimated number of deaths per 1,000 live births among children under 5 years of age (in 2017) — a key indicator of child survival and overall health system performance. High under-5 mortality rates often reflect limited access to essential health services, clean water, sanitation, and adequate nutrition. Expanding decentralized systems can play a key role in improving child survival outcomes in underserved areas.
This layer shows the percentage of children under 5 experiencing severe wasting (in 2019) — the most critical form of acute malnutrition. These children face a significantly higher risk of mortality and require immediate therapeutic support. This indicator helps prioritize emergency nutrition interventions and monitor high-risk areas in need of health and food system strengthening.
This layer shows the percentage of children under 5 years of age (in 2019) who are underweight, based on low weight-for-age. Underweight is a composite indicator capturing both chronic and acute undernutrition. It is useful for identifying vulnerable populations and informing multisectoral strategies to improve child health, food access, and caregiving practices.
Loading...
What's New
This dataset shows the African power plants and their installed capacity in MegaWatt (MW) and generation type. It includes thermal plants (coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity and generation.
Shapefile showing the location of powerplants along the East-African rift system, derived from the World Bank Transmission and distribution: Energy Atlas (ARDERNE, C. 2017) This dataset is part of the LEAP-RE project collection. For more information visit https://www.leap-re.eu/
Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.
Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, the terrestrial carbon cycle, hydrological regimes, indigenous population subsistence, and human health (1-5), and are increasingly recognized as an essential element of any strategy to mitigate climate change. Deforestation, degradation, and fragmentation compromise the functioning of tropical forests as an ecosystem, leading to biodiversity loss, reduced carbon storage capacity, and an increased risk of viral disease outbreaks. Accurate characterization of changes in tropical moist forests is therefore needed to support conservation policies and to better quantify their contribution to global carbon fluxes. This dataset comprises the following map layers: Undisturbed tropical moist forest: Shows the coverage of undisturbed tropical moist forests remaining at the end of the year 2019. Deforestation year map: Shows where deforestation occurred over three decades (between 1982 and 2020) and indicates the year when the forest cover was deforested for the first time (followed or not by regrowth). Degradation year map: Shows where degradation occurred over three decades (between 1982 and 2020) and indicates the year when the forest was degraded for the first time (and remained degraded up to 2020). Transition map: Captures the dynamics of changes in tropical moist forests between the initial observation period (1990) and the end of the year 2020. Source: European Commission Joint Research Centre (JRC) - Tropical Moist Forests (TMF)
Magnetic anomaly map of Africa, derived from the World Digital Magnetic Anomaly map (WDMAM). Note that the blurry areas are interpolated due to source not containing data points. Values are expressed in nT. The WDMAM is an international scientific project under the auspices of IAGA (International Association of Geomagnetism and Aeronomy) and CGMW (Commission for the Geological Map of the World), aiming to compile and make available magnetic anomalies caused by the Earth lithosphere, on continental and oceanic areas, in a comprehensive way, all over the World. This layer is part of the Geothermal Atlas for Africa developed within the LEAP-RE project"
This dataset comprises three distinct layers delineating the boundaries of sedimentary basins across Africa. In particular Basins (greater than 2km): Boundaries of sedimentary basins in Africa with sedimentary thicknesses of at least 2 km. Contains geological information about the sedimentary basins such as basin name, class/type and age. Created by extracting data from open sources into a series of shapefiles and rasters containing information on culture, geology, geothermal and geophysical data. Basins Nyberg & Howell (2015): Boundaries active sedimentary basin/depositional centers of Africa categorised by basin structure and climate-zone. Basins Robertson (2019): Boundaries of sedimentary basins in Africa. Part of the Robertson new ventures product suite. Includes information on the name, geometry, type and the hydrocarbon exploration status of a given basin. Basins Evenick (2021): Boundaries of sedimentary basins in Africa compiled from previous work. The sedimentary basins have been revised to simplify the borders of sedimentary basins, as the definition of a basin (boundary) is often contested. Includes the name, geometry, type, age and evaporite content of a given basin. These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project
Loading...