Learning where to inspect: Location learning for crime prediction

摘要: Crime studies conclude that crime does not occur evenly across urban landscapes but concentrates in certain areas. Spatial analysis, primarily focuses on hotspots, areas with disproportionally higher density. Using Crime-Tracer, a personalized random walk based approach to spatial analysis and location prediction outside of we propose here probabilistic model behavior known offenders within their activity space. Pattern Theory states offenders, rather than venture into unknown territory, frequently commit opportunistic crimes by taking advantage opportunities they encounter places are most familiar as part Our experiments large dataset show CrimeTracer outperforms all other methods used for recommendation evaluate here.