Improving Crime Count Forecasts Using Twitter and Taxi Data

Data from social media has created opportunities to understand how and why people move through their urban environment and how this relates to criminal activity. To aid resource allocation decisions in the scope of predictive policing, the paper proposes an approach to predict weekly crime counts. The novel approach captures spatial dependency of criminal activity through approximating human dynamics. It integrates point of interest data in the form of Foursquare venues with Twitter activity and taxi trip data, and introduces a set of approaches to create features from these data sources. Empirical results demonstrate the explanatory and predictive power of the novel features. Analysis of a six-month period of real-world crime data for the city of New York evidences that both temporal and static features are necessary to eectively account for human dynamics and predict crime counts accurately. Furthermore, results provide new evidence into the underlying mechanisms of crime and give implications for crime analysis and intervention.