Infrastructure

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.

The current version of the GDW database (version 1.0) aims to catalogue all types of anthropogenic instream barriers. While initial mapping efforts prioritize major dams that form reservoirs, as well as run-of-river barriers on larger rivers where more information is readily available, the dataset comprises two distinct but interconnected spatial layers: Barrier and Dam Locations (Point Layer): Contains barrier and dam locations, geospatially referenced as point coordinates and co-registered to the global river network of HydroSHEDS. Reservoir Extents (Polygon Layer): Contains associated reservoir polygons, which includes data representing the maximum storage capacity of the reservoirs measured in million cubic meters.

Percentage of children in a school attendance age (approximately 3-17 years old depending on the country) that have internet connection at home. Also in this case the indicator relates to the potential educational impact of electrification on children and young people.

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/

Shapefile showing the location and voltage of the electricity grid network of some parts of Africa. This dataset is part of the LEAP-RE project collection. For more information visit https://www.leap-re.eu/

This data represents the total built-up volume between 1975 and 2030 in 5 year intervals over cells of 1x1 km size. It derives from the GHS-BUILT-V - R2023A spatial raster dataset, that depicts the distribution of built-up volumes, expressed as number of cubic metres. The data report about the total built-up volume and the built-up volume allocated to dominant non-residential (NRES) uses. The dataset is part the Global Human Settlement Layer , Global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report

These layers present the application of the Degree of Urbanisation stage I methodology recommended by UN Statistical Commission to the global population grid generated by the JRC in the epochs 1975-2030 (5 years timestep). They derive from the GHS-SMOD - R2023A. The layers have been generated by integration of built-up surface extracted from Landsat and Sentinel-2 image data processing (GHS-BUILT-S R2023), and population data derived from the CIESIN GPW v4.11 (GHS-POP R2023). The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 1x1 km size. It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

This data represents the distribution of human population between 1975 and 2030 in 5 year intervals over cells of 100x100 m size. It derives from the GHS-POP - R2023A. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. The dataset is part the Global Human Settlement Layer, that provide global, high-resolution, multi-temporal gridded data on built-up environment (built-up surface, built-up volume, residential vs. non-residential function), resident population, and settlement classification by the UN-recommended methodology “degree of urbanisation”. The complete information about the GHSL main products can be found in the GHSL Data Package 2023 report.

Africa is projected to have the fastest urban growth rate in the world — by 2050, Africa’s cities will be home to an additional 950 million people. Urban planning and management are essential development challenges. Understanding urbanisation, its drivers, dynamics and impacts, is key to designing targeted, inclusive and foward-looking policies at the local, national and continental levels. Africapolis data and evidence supports cities and governments to make urban areas more inclusive, productive and sustainable. This map of urban population covers 7 500 agglomerations in 50 countries for the base year 2015.

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.

The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population change related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation. It reflects the dynamics of increasing number of people in a certain area. This layer is part of the World Altas of Desertification

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.

This dataset provides fixed broadband 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 fixed 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.