Vomfell, Lara Härdle, Wolfgang Karl Lessmann, Stefan
Year of Publication:
IRTG 1792 Discussion Paper No. 2018-013
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
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.
Predictive Policing Crime Forecasting Social Media Data Spatial Econometrics