Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning

The United Nations Human Development Index (HDI) is arguably the most widely used alternative to gross domestic product for measuring national development. This is in large part due to its multidimensional nature, as it incorporates not only income, but also education and health. However, the low country-level resolution of the global HDI data released by the Human Development Report Office of the United Nations Development Programme (N=191 countries) has limited its use at the local level. Recent efforts used labor-intensive survey data to produce HDI estimates for first-level administrative units (e.g., states/provinces). Here, we build on recent advances in machine learning and satellite imagery to develop the first global estimates of HDI for second-level administrative units (e.g., municipalities/counties, N = 61,591) and for a global 0.1 * 0.1 degree grid (N=806,361). To accomplish this we develop and validate a generalizable downscaling technique based on satellite imagery that allows for training and prediction with observations of arbitrary shape and size. This enables us to train a model using provincial administrative data and generate HDI estimates at the municipality and grid levels. Our results indicate that more than half of the global population was previously assigned to the incorrect HDI quintile within each country, due to aggregation bias resulting from lower resolution estimates. We also illustrate how these data can improve decision-making. We make these high resolution HDI estimates publicly available in the hope that they increase understanding of human wellbeing globally and improve the effectiveness of policies supporting sustainable development. We also make available the satellite features and software necessary to increase the spatial resolution of any other global-scale administrative data that is detectable via imagery