Developing a contextual model of poverty prediction using data science and analytics – The case of Shelby County

This study builds on the existing poverty literature and leverages data from disparate sources including both big data sources such as satellite images and traditional data sources such as the federal, state, and local agencies to develop a context-specific poverty prediction model using design science. We examine whether and to what extent infrastructure development as measured from the satellite images as well as spatial spillovers helps predict the poverty rate of a given census tract. We also develop and implement a Vector Autoregression (VAR) based ensemble model that combines predictions from daytime and nighttime imaging with adjacent tracts' poverty rates and other economic and demographic factors identified in prior literature. Our results show that daytime imaging and spatial network features have significant predictive value and that a combination of these features gives the best predictive power. In addition, we find that the skewness of poverty rates among adjacent census tracts, not the average, is a significant predictor showing the importance of distribution of poverty around a region. Our work has major implications for researchers using deep learning and network analysis for policy development and decision making. •Method for combining CNN and RNN for Day Time Satellite Image Data.•An Ensemble Method for Time Series Data using VAR.•Developed a framework for combining traditional and big data for poverty prediction.•Day Time Satellite Images and Network Features have significant predictive value.•Skewness of Poverty rates among census tracts is a significant predictor.