Learning, Predicting and Planning against Crime: Demonstration Based on Real Urban Crime Data (Demonstration)
نویسندگان
چکیده
Figure 1: DBN Crime in urban areas plagues every city in all countries. This demonstration will show a novel approach for learning and predicting crime patterns and planning against such crimes using real urban crime data. A notable characteristic of urban crime, distinct from organized terrorist attacks, is that most urban crimes are opportunistic in nature, i.e., criminals do not plan their attacks in detail, rather they seek opportunities for committing crime and are agile in their execution of the crime [1, 2]. Police officers conduct patrols with the aim of preventing crime. However, criminals can adapt their strategy in response of police deployment by seeking crime opportunity in less effectively patrolled location. The problem of where and how much to patrol is therefore important. In this presentation we propose a novel approach to learn and update the criminal behavior from real data [3]. We model the interaction between criminals and patrol officers as a Dynamic Bayesian Network (DBN). Figure 1 shows an example of such DBN. Next, we apply a dynamic programming algorithm to generate optimal patrol strategy against the learned criminal model. By iteratively updating the criminals’ model and computing patrol strategy against them, we help patrol officers keep up with criminals’ adaptive be-
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