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Access to accurate, granular, and up-to-date poverty data is essential forhumanitarian organizations to identify vulnerable areas for poverty alleviationefforts. Recent works have shown success in combining computer vision andsatellite imagery for poverty estimation; however, the cost of acquiringhigh-resolution images coupled with black box models can be a barrier toadoption for many development organizations. In this study, we present ainterpretable and cost-efficient approach to poverty estimation using machinelearning and readily accessible data sources including social media data,low-resolution satellite images, and volunteered geographic information. Usingour method, we achieve an $R^2$ of 0.66 for wealth estimation in thePhilippines, compared to 0.63 using satellite imagery. Finally, we use featureimportance analysis to identify the highest contributing features both globallyand locally to help decision makers gain deeper insights into poverty.

Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information