Keeping pace with criminals: Learning, Predicting and Planning against Crime: Demonstration Based on Real Urban Crime Data (Demonstration)


Chao Zhang, Manish Jain, Ripple Goyal, Arunesh Sinha, and Milind Tambe. 2015. “Keeping pace with criminals: Learning, Predicting and Planning against Crime: Demonstration Based on Real Urban Crime Data (Demonstration) .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).


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 [6, 7, 1, 4]. 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. There are two approaches to solve this problem. The first approach is to schedule patrols manually by human planners, which is followed in various police departments. However, it has been demonstrated that manual planning of patrols is not only time-consuming but also highly ineffective in related scenarios of protecting airport terminals [3] and ships in ports [5]. The second approach is to use automated planners to plan patrols against urban crime. This approach has either focused on modeling the criminal explicitly [7,6] (rational, bounded rational, etc.) in a game model or to learn the adversary behavior using machine learning [2]. However, the proposed mathematical models of criminal behavior have not been validated with real data. Also, prior machine learning approaches have either only focused on the adversary actions ignoring their adaptation to the defenders’ actions [2]. Hence, in this presentation we propose a novel approach to learn and update the criminal behavior from real data [8]. 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 behavior and execute effective patrols. This process is shown as a flow chart in Figure 2. With this context, the demonstration presented in this paper introduces a web-based software with two contributions. First, our system collects and analyzes crime reports and resources (security camera, emergency supplies, etc.) data, presenting them in various forms. Second, our patrol scheduler incorporates the algorithm in [8] in a scheduling recommendation system. The demonstration will engage audience members by having them participate as patrol officers and using the software to ‘patrol’ the University of Southern California (USC) campus in USA.
See also: 2015