Crime Location Prediction

enforcement to face inevitable increases in urban crime rates as a side ef1'ect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concen­ trates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionall y higher crime density. In this paper we present CRIMETRACER, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behavior of known ofl'enders within their activity space. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes and serial violent crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our on a large real-world crime dataset show that CRIMETRACER outperforms all other methods used for location recommendation we evaluate here.

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