Geographical Analysis

Crime Exposure along My Way Home: Estimating Crime Risk along Personal Trajectory by Visual Analytics

Early View

Crime has been one of the notorious public threats in cities. Fortunately, the increasing digital crime data provide great opportunities to analyze and control crime incidents. However, studies that predict the risk of crime exposure for an individual's spatiotemporal paths based on historical crime big data are still limited. In this study, we have proposed the crime risk index (CRI) for spatiotemporal trajectory and built a model to estimate the CRI. Furthermore, an online crime risk analysis platform has been developed based on the model. First, we proposed a multi‐scale tile system and a novel algorithm to estimate trajectory‐based CRI using big historical crime data and entropy‐based weighting. Second, we created a web‐based platform that allows users to provide a spatiotemporal trajectory and estimate the crime risk for such trajectory. We conducted several experiments based on the crime data in Detroit. Results demonstrate the practicability and generalizability of our platform. The proposed model and platform can be applied to multiple cities, providing useful references for crime information and public safety.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.