Using machine learning to track human development at global scale and high spatial resolution

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Using machine learning to track human development at global scale and high spatial resolution

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    The Human Development Index (HDI) is widely used by policymakers and academics to summarize three key dimensions of wellbeing: the population’s health, human capital, and standard of living. A more comprehensive measure of wellbeing than income or wealth alone, HDI is used to categorize countries by their level of human development, which, in turn, can determine allocations of global resources.  However, the United Nations releases official global estimates of HDI annually only at the highly aggregated national level, preventing their use for many policy and aid applications. This project builds 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) and for a global ~10km × 10km grid. 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. 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. 

     

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