Mapping multidimensional poverty in Cambodia: Integrating big data, socioeconomic surveys, and machine learning

Abstract

Cambodia, while experiencing rapid economic growth, faces persistent poverty challenges, making it one of Southeast Asia’s economically vulnerable nations. Addressing this requires comprehensive insights into poverty’s diverse dimensions. This study combines big data, machine learning, and the Cambodia Socio-Economic Survey to analyze poverty across education, health, and living standards. It calculates deprivation probabilities across a geospatial grid. A random forest algorithm yields high predictive accuracy and identifies key predictors of poverty vulnerability. Overall, this study underscores the potential of big-earth observation data and machine learning in complementing surveys to map poverty vulnerabilities at various scales.

Carlos Mendez

Associate Professor of Development Economics

My research interests focus on the integration of development economics, spatial data science, and econometrics to understand and inform the process of sustainable development across regions.