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 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.

Average time spent by households on fuel collection daily. The higher the number of daily hours that households spend in collecting fuel, the higher the improvements in the quality of life of communities due to electricity access.

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 Fire Information for Resource Management System (FIRMS) of the National Aeronautics and Space Administration (NASA) uses satellite observations to detect active fires and thermal anomalies. They deliver this information to decision makers in near real-time (within 3 hours of satellite observation). This dataset includes active fires of the last 24h. Each point represents the centre of a 375 m resolution pixel where a fire was detected. It is updated twice daily. Compared to other coarser resolution (≥1km) satellite fire detection products, it provides improved response for smaller fires, improved mapping of large fire perimeters, and better detection at night, when fire activities usually occur. Consequently, the data are well suited for use in support of fire tracking and management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.

The Fire Information for Resource Management System (FIRMS) of the National Aeronautics and Space Administration (NASA) uses satellite observations to detect active fires and thermal anomalies. They deliver this information to decision makers in near real-time (within 3 hours of satellite observation). This dataset includes active fires of the last 48h. Each point represents the centre of a 375 m resolution pixel where a fire was detected. It is updated twice daily. Compared to other coarser resolution (≥1km) satellite fire detection products, it provides improved response for smaller fires, improved mapping of large fire perimeters, and better detection at night, when fire activities usually occur. Consequently, the data are well suited for use in support of fire tracking and management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.

The Fire Information for Resource Management System (FIRMS) of the National Aeronautics and Space Administration (NASA) uses satellite observations to detect active fires and thermal anomalies. They deliver this information to decision makers in near real-time (within 3 hours of satellite observation). This dataset includes active fires of the last 72h. Each point represents the centre of a 375 m resolution pixel where a fire was detected. It is updated twice daily. Compared to other coarser resolution (≥1km) satellite fire detection products, it provides improved response for smaller fires, improved mapping of large fire perimeters, and better detection at night, when fire activities usually occur. Consequently, the data are well suited for use in support of fire tracking and management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.