Combining remote sensing and social media data to predict indicators of political and economic instability

Abstract

Understanding a region’s socio-economic conditions can inform the development of policies in both the public and private sectors. Commercial satellite imagery provides socio-economic context. By combining commercial imagery with geospatially enhanced social media, we generate local measures of political and economic instability risks at a regional and national scale. We present models that generate instability estimates by fusing socio-economic contextual data from commercial imagery with high-tempo social media data. To assess model performance, we predict annual indicators of conditions for a country as assessed by the World Bank. The models relate model-derived features to indicators of political stability, control of corruption, rule of law, government effectiveness, voice and accountability, and gross domestic product using data from multiple countries. Comparison of our methods to the World Bank data demonstrate the strengths of our approach.