The spatial distribution of population in 2020, throughout 47 African countries.

GISRede OpenData support

The geospatial experts at GISReded have developed a Python workflow to automate the production of population grids using the method developed by Stevens et al. (2015) . This method models population total per grid cell based on the relationship between population density in census blocks and various environmental predictor variables (e.g. proximity to road network, brightness of nightlights) as a proxy for the presence/absence of population, as well as intensity. In this iteration, we predict population counts for every 1km grid cell in 47 African countries. To enhance this prediction, we only predict population in grid cells in which buildings are present, as extracted from high-resolution satellite imagery by Google. This leaves grid cells with an absence of buildings as unpopulated, rather than spreading a tiny proportion of the population over clearly uninhabited areas. Please see our building patterns page to download these patterns and for more information on this data. Census tables for 2020 were downloaded from the WorldPop project’s website. For this iteration, we have used all building footprints in the Google extraction, including those with lower prediction accuracies (see for further details on Google’s methodology). If you would like this data at higher resolutions, please contact one of our experts to discuss your options.

The spatial distribution of population in 2020, throughout 47 African countries.

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GISREDE allows solving a wide range of applied problems based on the analysis of the location, the characteristics of a particular territory and the relationship between geographical, social and economic factors.