Education is widely recognized as one of the most essential components for poverty reduction. Education infrastructure in Sub-Saharan Africa is one of the social sectors with less access to electricity, only 35.1% of Sub-Saharan African primary schools have access to electricity and half of secondary schools in sub-Saharan Africa do not have power. Irrespective of this evidence, there is a lack of systematic, recently updated data on which schools are with or without access to electricity and hence a limited awareness of the electrification status. Consequently, it can be difficult to prioritise the provision of reliable electricity when planning and implementing energy investments to the education sector in rural areas The dataset collects geolocation of schools (primary and secondary) with the current situation of access to electricity with a binary description (With electricity access WEA, Non electricity access NEA).
European Commission - Joint Research Centre and UNICEF
This datasets are obtained using a series of proxies, combining satellite data, use deep-learning methods, national statistical data, and several open-source data, as further described in the upcoming publication (2023)
GOAL 4
Education is widely recognized as one of the most essential components for poverty reduction. Education infrastructure in Sub-Saharan Africa is one of the social sectors with less access to electricity, only 35.1% of Sub-Saharan African primary schools have access to electricity and half of secondary schools in sub-Saharan Africa do not have power. Irrespective of this evidence, there is a lack of systematic, recently updated data on which schools are with or without access to electricity and hence a limited awareness of the electrification status. Consequently, it can be difficult to prioritise the provision of reliable electricity when planning and implementing energy investments to the education sector in rural areas The dataset collects geolocation of schools (primary and secondary) with the current situation of access to electricity with a binary description (With electricity access WEA, Non electricity access NEA).
European Commission - Joint Research Centre and UNICEF
This datasets are obtained using a series of proxies, combining satellite data, use deep-learning methods, national statistical data, and several open-source data, as further described in the upcoming publication (2023)
GOAL 4
The world is shrinking. Cheap flights, large scale commercial shipping and expanding road networks all mean that we are better connected to everywhere else than ever before. Accessibility - whether it is to markets, schools, hospitals or water - is a precondition for the satisfaction of almost any economic need. The new map of Travel Time to Major Cities -developed by the European Commission and the World Bank- captures this connectivity and the concentration of economic activity. It also highlights that there is little wilderness left. The map shows the travel time (in hours/days) to major cities (i.e. cities of 50,000 or more people in year 2000) using land (road/off road) or water (navigable river, lake and ocean) based travel.
European Commission - Joint Research Centre
This map was made for the World Bank's World Development Report 2009 "Reshaping Economic Geography". Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. Each pixel value on the map represents the travel time to the nearest city of 50,000 or more people in year 2000. For a description of the model and data sources used, visit https://forobs.jrc.ec.europa.eu/products/gam/description.php
GOAL 7, GOAL 11, GOAL 13
The world is shrinking. Cheap flights, large scale commercial shipping and expanding road networks all mean that we are better connected to everywhere else than ever before. Accessibility - whether it is to markets, schools, hospitals or water - is a precondition for the satisfaction of almost any economic need. The new map of Travel Time to Major Cities -developed by the European Commission and the World Bank- captures this connectivity and the concentration of economic activity. It also highlights that there is little wilderness left. The map shows the travel time (in hours/days) to major cities (i.e. cities of 50,000 or more people in year 2000) using land (road/off road) or water (navigable river, lake and ocean) based travel.
European Commission - Joint Research Centre
This map was made for the World Bank's World Development Report 2009 "Reshaping Economic Geography". Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. Each pixel value on the map represents the travel time to the nearest city of 50,000 or more people in year 2000. For a description of the model and data sources used, visit https://forobs.jrc.ec.europa.eu/products/gam/description.php
GOAL 7, GOAL 11, GOAL 13
The world is shrinking. Cheap flights, large scale commercial shipping and expanding road networks all mean that we are better connected to everywhere else than ever before. Accessibility - whether it is to markets, schools, hospitals or water - is a precondition for the satisfaction of almost any economic need. The new map of Travel Time to Major Cities -developed by the European Commission and the World Bank- captures this connectivity and the concentration of economic activity. It also highlights that there is little wilderness left. The map shows the travel time (in hours/days) to major cities (i.e. cities of 50,000 or more people in year 2000) using land (road/off road) or water (navigable river, lake and ocean) based travel.
European Commission - Joint Research Centre
This map was made for the World Bank's World Development Report 2009 "Reshaping Economic Geography". Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. Each pixel value on the map represents the travel time to the nearest city of 50,000 or more people in year 2000. For a description of the model and data sources used, visit https://forobs.jrc.ec.europa.eu/products/gam/description.php
GOAL 7, GOAL 11, GOAL 13
The world is shrinking. Cheap flights, large scale commercial shipping and expanding road networks all mean that we are better connected to everywhere else than ever before. Accessibility - whether it is to markets, schools, hospitals or water - is a precondition for the satisfaction of almost any economic need. The new map of Travel Time to Major Cities -developed by the European Commission and the World Bank- captures this connectivity and the concentration of economic activity. It also highlights that there is little wilderness left. The map shows the travel time (in hours/days) to major cities (i.e. cities of 50,000 or more people in year 2000) using land (road/off road) or water (navigable river, lake and ocean) based travel.
European Commission - Joint Research Centre
This map was made for the World Bank's World Development Report 2009 "Reshaping Economic Geography". Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. Each pixel value on the map represents the travel time to the nearest city of 50,000 or more people in year 2000. For a description of the model and data sources used, visit https://forobs.jrc.ec.europa.eu/products/gam/description.php
GOAL 7, GOAL 11, GOAL 13
This dataset shows the African power plants. 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 type.
European Commission - Joint Research Centre
This list of power plants is entirely based on open sources datasets: ECOWREX (ECOWREX.org), WRI Global Power Plant Database (https://www.wri.org/publication/global-power-plant-database), ENERGYDATA.INFO open data platform (https://energydata.info/en/dataset/africa-power-stations-2012.), Harvard. Africa power plants (http://worldmap.harvard.edu/data/geonode:africa_power_plants_gd4) and S&P Global Platts (https://www.platts.com/products/world-electric-power-plants-database). The datasets are available per region at: http://doi.org/10.5281/zenodo.2620189 (West Africa), https://zenodo.org/record/4030905#.X2xIsHtS-Mo (Southern Africa), https://zenodo.org/record/3839756#.X3WPN3tS-Mo (North, Eastern and Central Africa)
GOAL 7, GOAL 9
This dataset provides mobile (cellular) network performance metrics in zoom level 16 web Mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed is collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. Speedtest data is used today by commercial mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. This data can be used for rural and urban connectivity development, to help make the internet better, faster, and more accessible for everyone.
OOKLA
Hundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of "zoom level" (or "z"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters. For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.
GOAL 10, GOAL 11
This dataset shows the Longterm yearly average of global horizontal irradiation (GHI) in kWh/m2, covering the period 1999-2018
Solargis, The World Bank - Global Solar Atlas
This data layer represents an output from the Solargis global solar model. It has been delivered for the Global Solar Atlas (https://globalsolaratlas.info/), online platform funded by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank, under a global initiative on Renewable Energy Resource Mapping.
GOAL 7, GOAL 9
The mean wind power density is a measure of the wind resource. Higher mean wind power densities indicate better wind resources.
Global Wind Atlas
To run the modeling over the very large area, a system of software and servers called the Global Wind Atlas Frogfoot was developed. This method is very similar to what is used in the WAsP software. For example, the flow modeling for orography, roughness and roughness change is the same in the WAsP software. However, the GWA calculation differs in a number of ways in order to allow a very large area to be covered. For example, local wind climate calculations are based on more than a single generalized wind climate, and terrain data is input as raster maps rather than vector maps.
GOAL 7, GOAL 9
Droughts affect millions of people in the world each year and have long-lasting socioeconomic impacts. They can occur over most parts of the world, even in wet and humid regions, and can profoundly impact agriculture, basic household welfare, tourism, ecosystems and the services they provide. The Risk of Drought Impact for Agriculture (RDrI-Agri) is a categorized risk index, indicating the probability of having impacts from a drought, with particular focus on vegetation. Higher risk (in red) means that the areas affected will be the most likely to report impacts due to droughts. It is updated every ten days.
EC-JRC
The layer shows the latest map available for the Risk of Drought Impacts for Agriculture (RDrI-Agri) as implemented in the Global Drought Observatory (GDO) of the Copernicus Emergency Management Service. It is used for determining areas likely to be affected by droughts, with emphasis on impacts on the agriculture sector. The RDrI-Agri indicator combines drought hazard (i.e. the possible future occurrence of drought events of a certain severity), drought exposure (i.e. the total population, its livelihoods and assets in an area in which drought events may occur), and vulnerability (i.e. the propensity of exposed elements to suffer adverse effects when impacted by a drought event). Drought hazard is expressed as the combination of rainfall anomaly (Standardized Precipitation Index – SPI), photosynthetic activity of the vegetation cover anomaly (fraction of Absorbed Photosynthetically Active Radiation (fAPAR) Anomaly) and soil moisture anomaly (SMA). The indicator is computed for the first, second and third decade of each month, and updated respectively on the 11th, 21st and 1st of the following month. The layer displayed in the Africa Knowledge Platform is the latest map available, for the last full decade. More information on the product factsheet: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_rdri_agri.pdf
GOAL 15, GOAL 3, GOAL 2, GOAL 13
The Undisturbed tropical moist forest map shows the coverage of undisturbed tropical moist forests remaining at the end of the year 2021.
NASA-SRTM
For a full description of the dataset refer to the publication: C. Vancutsem, F. Achard, J.-F. Pekel, G. Vieilledent, S. Carboni, D. Simonetti, J. Gallego, L.E.O.C. Aragão, R. Nasi. Long-term (1990-2019) monitoring of forest cover changes in the humid tropics. Science Advances 2021.(https://doi.org/10.1126/sciadv.abe1603)
GOAL 15
Protected areas have long played a crucial role in protecting natural landscapes and wildlife, and many consider them to be one of the most effective tools in protecting biodiversity. The International Union for Conservation of Nature (IUCN) officially defines a protected area as ' a clearly defined geographical space, recognised, dedicated and managed, through legal or other effective means, to achieve the long term conservation of nature with associated ecosystem services and cultural values'. Protected areas also play a key role in preserving the benefits that nature brings to people, often referred to as 'ecosystem services'. They come in many shapes and sizes, ranging from strict nature reserves where only scientific research is permitted, to areas that allow natural resources to be used. The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas.
UNEP-WCMC
The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via Protected Planet, the online interface for the WDPA. The WDPA is a joint initiative of the International Union for Conservation of Nature (IUCN) and the UN Environment Programme's World Conservation Monitoring Centre (UNEP-WCMC) to compile spatially referenced information about protected areas. The data are provided as shapefiles and updated monthly.
GOAL 15, GOAL 14
This product is a global-scale climate diagnostic tool and provides a big picture overview of average global temperatures compared to a reference value.
NOAA
Anomalies are with respect to the 20th century average (1901-2000). Monthly and annual global anomalies are available through the most recent complete month and year, respectively.
GOAL 15, GOAL 14
Digital Elevation Models (DEMs) provide information on bare ground topography, that is, elevation irrespective of land cover such as buildings/trees. DEMs are often derived from Light Detection and Ranging (LiDAR) data sources. DEMs are used in several applications such as understanding how climatic variables change with topography..
NASA-SRTM
The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations.
GOAL 15
Digital Elevation Models (DEMs) provide information on bare ground topography, that is, elevation irrespective of land cover such as buildings/trees. DEMs are often derived from Light Detection and Ranging (LiDAR) data sources. DEMs are used in several applications such as understanding how climatic variables change with topography..
NASA-SRTM
The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations.
GOAL 15
Whether you’re monitoring crops, modelling green energy installations or soil sealing, combatting loss of natural resources or just helping countries meet their Sustainable Development Goals, chances are high that you’ll need an accurate and spatially detailed map on land cover and land use. Earth Observation satellites, like those from EU’s flagship programme Copernicus, are key to providing such maps, at a global scale, with free and open access. Derived from the Copernicus Global Land Cover, this map represents the distribution of areas where land cover is not heavily disturbed by man’s activities. In other words, it shows areas where natural ecosystems and their associated species are expected to be found.
NASA-SRTM
This layer of Natural Areas is calculated using Copernicus Global 100m Land Cover map 2019, by aggregation of all land cover classes except ‘Urban/Built up’ and ‘Cultivated and managed vegetation/agriculture (cropland)’.
GOAL 15, Goal 3
In an age of Development Agendas that call for universal inclusiveness of people, global population grids are essential to support analyses and inform policy-making in a wide range of fields, from environmental assessment to disaster risk analysis and reduction. This map combines the best-available population estimates (from CIESIN Gridded Population of the World) with the best-available assessment of the spatial extents of human settlements (inferred from Landsat satellite data). It depicts the distribution and density of population, expressed as the number of people per cell, at high spatial resolution (250m) for the year 2015.
EC-JRC
Residential population estimates provided by the CIESIN Gridded Population of the World, version 4.10 (GPWv4.10) were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer. The novel population grids constitute currently the de facto state-of-the-art in terms of open global geospatial population data, potentially enabling advances in many of the fields where this information is relevant.
Goal 11, Goal 10, Goal 8
This map, compiled to support the analysis of SOLAW report concerning trends and current use of water use in agriculture, shows the percentage of irrigated area supplied by groundwater. Irrigation mainly relies on renewable freshwater resources, either surface water or groundwater.
FAO
While the extent of irrigation and related water uses are reported in statistical databases or by model simulations, information on the source of irrigation water is still very rare. A recent global inventory undertaken by FAO and the University of Bonn reports that 113 million ha, or 38 percent of the total area equipped for irrigation of 301 million ha, is irrigated by groundwater
GOAL 15, GOAL 2
Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. This dataset contains the global distribution of Livestock in 2010 expressed in total number of goats per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3)
FAO
The Gridded Livestock of the World, developed by FAO, is publicly available for download at https://dataverse.harvard.edu/dataverse/glw, and is described in detail in: Gilbert M, G Nicolas, G Cinardi, S Vanwambeke, TP Van Boeckel, GRW Wint, TP Robinson (2018) Global Distribution Data for Cattle, Buffaloes, Horses, Sheep, Goats, Pigs, Chickens and Ducks in 2010. Nature Scientific data, 5:180227. doi: 10.1038/sdata.2018.227
GOAL 15, GOAL 2
In global studies related to water use, food production or climate change, knowing the location of irrigate agriculture is important in order to assess better the impact of water use on the available water resources. The Global map of irrigation areas shows the amount of area equipped for irrigation around the turn of the twenty-first century as a percentage of the total area on a raster with a resolution of 5 minutes.
FAO
The data layer on area equipped for irrigation was developed by combining sub-national irrigation statistics with geospatial information on the position and extent of irrigation schemes to compute the fraction of 5 arc minute cells that was equipped for irrigation, which is called irrigation density.
GOAL 15, GOAL 2
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.
EC-JRC
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.
GOAL 3
In Sub-Saharan Africa, medium- and low-voltage data are often non-existent, uncompleted, or unavailable. This is a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. This layer presents the spatial distance to the existing and planned electricity grid (high, medium, low voltage level).
EC-JRC
This layer presents the spatial distance to the existing and planned electricity grid (high, medium, low voltage level) compiled using multiple sources that enumerate elements of the existing transmission and distribution network. These sources include Open Street Map, the Word Bank datasets, Arderne et al. (2020), the Economic Community of West African States Observatory for Renewable Energy and Energy Efficiency (http://www.ecowrex.org/), and rural electrification agencies and EU delegations in Africa (Burkina Faso, Kenya, Tanzania).
GOAL 7
The data reports about the built-up surface allocated to dominant non-residential (NRES) uses expressed as number of square meters.
EC-JRC
Data are spatial-temporal interpolated from 1975 to 2030 in 5 years intervals. For the temporal anchor point of 2018 the data is published at 10m as observed from the S2 image data.
GOAL 11
Most conflicts initially start out as very local phenomena. Monitoring political violence events at local-level can help anticipate the escalation of conflicts within states, recognise signs of crisis development and determine likely conflict trajectories. The Armed Conflict Location & Event Data Project (ACLED) collects reported information on internal political conflict disaggregated by date, location and actors to facilitate local and scale-dependant research on war patterns and processes. This layer shows all political violence and protest events recorded by ACLED in Africa for the period 2019-2022.
Armed Conflict Location & Event Data Project (ACLED)
The Armed Conflict Location & Event Data (ACLED) project is a disaggregated data collection, analysis, and crisis mapping project. ACLED data are derived from a wide range of local, regional and national sources and the information is collected by trained data experts worldwide. ACLED records the dates, actors, types of violence, locations, and fatalities of all reported political violence and protest events across Africa, South Asia, Southeast Asia, the Middle East, Central Asia and the Caucasus, and Southeastern and Eastern Europe and the Balkans. Political violence and protest activity includes events that occur within civil wars and periods of instability, public demonstrations, and regime breakdown. Version 8 of ACLED covers African states from 1997 into real-time.
GOAL 16