Spatially interpolated map surface datasets for key development indicators are being produced and publicly shared using population-based surveys from the USAID-funded Demographic and Health Survey (DHS) Program. Each modeled surface is produced with standardized geostatistical modeling methods. For each indicator, a package is available that includes spatial raster grids of 5 × 5 km pixels for the point estimate surface and an uncertainty surface, along with validation statistics and other model diagnostic data. The maps are publicly available for download on the DHS Program Spatial Data Repository at http://spatialdata.dhsprogram.com/. The modeled surfaces are produced with publicly available geo-referenced data on each indicator as collected by the DHS Program, augmented with other relevant spatial data sources that act as covariates. A Bayesian model−based geostatistical (MBG) approach is used to generate the modeled surfaces. Spatially modeled surfaces can be used to support and improve decision-making at multiple levels within many development programs including health, population, family planning, nutrition, and water and sanitation. The modeled surfaces can be used in their original 5 × 5 km pixel format, operationalized to other geographic areas as relevant for the program, or linked to DHS or other survey data for additional analysis.
Published in a peer-reviewed journal of the Population Council. Clara R. Burgert-Brucker is Senior Geospatial Advisor, and Trinadh Dontamsetti is Health Geographic Analyst, The DHS Program, ICF International, Rockville, MD. Peter W. Gething is Professor, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, UK.