Quantifying Socio-economic Context from Overhead Imagery

Abstract

Discerning regional political volatility is valuable for successful policy development by government and commercial entities, and necessitates having an understanding of the underlying economic, social, and political environment. Some methods of obtaining the environment information, such as global public opinion surveys, are expensive and slow to complete. We explore the feasibility of gleaning comparable information through automated image processing with a premium on freely available commercial satellite imagery. Previous work demonstrated success in predicting survey responses related to wealth, poverty, and crime in rural Afghanistan and Botswana, by utilizing spatially coinciding high resolution satellite images to develop models. We extend these findings by using similar image features to predict survey responses regarding political and economic sentiment. We also explore the feasibility of predicting survey responses with models built from Sentinel 2 satellite imagery, which is coarser-resolution, but freely available. Our fidings reiterate the potential for cheaply and quickly discerning the socio-politico-economic context of a region solely through satellite image features. We show a number of models and their cross-validated performance in predicting survey responses, and conclude with comments and recommendations for future work.