Combining Twitter and Earth Observation Data for Local Poverty Mapping

Abstract

Accurate and timely data on economic development is essential for policy-makers in low- and middle-income countries where such data is often unavailable. To fill this gap, existing approaches have used alternative data sources to proxy for levels of local development such as satellite imagery or mobile phone data. In this paper, we underline the power of an underrated data source for poverty mapping: Geolocated tweets. We show that the number of tweets in a region as singular input can already explain 55 % of the variation in local wealth in Sub-Saharan Africa with a simple Random Forest model. When nighttime light and Twitter usage information are combined as inputs to a Random Forest model they already explain 65% of the variation in local wealth which is in the range of state-of-the-art neural network architectures based on satellite images. Our results show that the naive combination of these data sources in a random forest is already competitive in performance and more elaborate fusion approaches are a promising direction to advance the accuracy of poverty mapping.