Spatial-Temporal-Textual Point Processes with Applications in Crime Linkage Detection

Shixiang Zhu, Yao Xie

Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime events are highly complex. Detecting crime linkage given a set of events is a highly challenging task since we only have limited information, including text descriptions, event times, and locations. In practice, there are very few labels. We propose a new statistical modeling framework for spatio-temporal-textual data and demonstrate its usage on crime linkage detection. We capture linkages of crime incidents via multivariate marked spatio-temporal Hawkes processes and treat embedding vectors of the free-text as marks of incidents. This is inspired by the notion of modus operandi (M.O.) in crime analysis. We also reduce the implicit bias in text documents before embedding to remove any potential discrimination of our algorithm. Numerical results using real data demonstrate the good performance of our method. The proposed method can be widely used in other similar data in social networks, electronic health records, etc.

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