First release of the code used to calculate accessibility to health services in sub-Saharan Africa, while evaluating the impact of using six different gridded population datasets. In this project we compared the difference in accessibility coverage estimates for six gridded population datasets: 1) WorldPop top-down constrained, 2) WorldPop top-down unconstrained, 3) HRSL, 4) GPWv4, 5) Landscan, and 6) Global Human Settlement Population (GHS-POP). The R scripts are made to clip, project and prepare all input data for a geographic accessibility analysis. In addition it uses code to extract the most recent OpenStreetMap layers. The data preparation includes: Landcover: clipping and projection (R-script, 01_data_prep_landcover.R) Digital Elevation Model: data fetching, clipping, and projecting (R-script, 02_data_prep_dem_download.R & 02_data_prep_dem_process.R) Roads: data fetching and projecting (R-script, 03_data_prep_roads.R) Hydrography: data fetching of line and polygon features (R-script, 04_data_prep_hydro_lines.R & 05_data_prep_hydro_poly.R) Landcover merge: combining all input data in a merged land cover to which a travel scenario can be applied (R-script, 06_data_prep_merge_landcover.R) Friction layer: the transformation of a land cover merge to a friction layer that presents the cost of traversing a cell (R-script, 07_friction_layer.R) Health facility location: clipping point features to countries and projecting (R-script, 08_health_facilities.R) Accessibility analyis: cost-distance algorithm in arcpy that appliesa the eight directional least-cost path to the friction layer overlaid with health facility location (09_accessibility_analysis.py). Gridded population data: the fetching of some and preparation of several gridded population datasets (R-scripts, 10_download_population_worldpop.R & 11_clip_population.R) Accessibility coverage statistics: calculation of the population covered in several travel time catchments (i.e., 30, 60, 120, 150, and 180 minutes) (R-scripts, 13_extract_coverage_X.R)