What we study
Our work promotes interdisciplinary science and innovation for economic, social, and environmental sustainability, integrating development economics, spatial data science, machine learning, and satellite remote sensing to understand and inform sustainable development across regions and countries.
Integrate satellite remote sensing, spatial econometrics, and machine learning to evaluate local progress toward the Sustainable Development Goals (SDGs), quantify environmental trade-offs, and model regional resilience.
Harness Earth observation data to map subnational wealth and health globally. This approach proxies economic activity, tracks poverty dynamics, and reveals spatial inequality in data-scarce regions.
Apply econometrics and machine learning to analyze regional income convergence, identify persistent spatial poverty traps, and measure the impact of economic spillovers across national and subnational borders.
Model how localized sectoral shifts and labor productivity improvements drive regional development. This includes detecting economic clusters and mapping their evolutionary dynamics.
Quantify the spatial disparities of energy poverty and electricity access while mapping the localized socio-economic impacts of the transition to a low-carbon economy.