  Access to accurate, granular, and up-to-date poverty data is essential for
humanitarian organizations to identify vulnerable areas for poverty alleviation
efforts. Recent works have shown success in combining computer vision and
satellite imagery for poverty estimation; however, the cost of acquiring
high-resolution images coupled with black box models can be a barrier to
adoption for many development organizations. In this study, we present a
interpretable and cost-efficient approach to poverty estimation using machine
learning and readily accessible data sources including social media data,
low-resolution satellite images, and volunteered geographic information. Using
our method, we achieve an $R^2$ of 0.66 for wealth estimation in the
Philippines, compared to 0.63 using satellite imagery. Finally, we use feature
importance analysis to identify the highest contributing features both globally
and locally to help decision makers gain deeper insights into poverty.
