This project from the Becker Friedman Institute for Economics combines high-resolution spatial data and machine learning, using Random Forest models trained on diverse predictors like population, nighttime lights, land use, CO2 emissions, and vegetation indices, to estimate local GDP shares globally while addressing data scarcity and cross-regional variability.
