A Model-Based Reasoning Approach to Prevent Crime

Within the field of criminology, one of the main research interests is the analysis of the displacement of crime. Typical questions that are important in understanding the displacement of crime are: When do hot spots of high crime rates emerge? Where do they emerge? And, perhaps most importantly, how can they be prevented? In this paper, an agent-based simulation model of crime displacement is presented, which can be used not only to simulate the spatio-temporal dynamics of crime, but also to analyze and control those dynamics. To this end, methods from Artificial Intelligence and Ambience Intelligence are used, which are aimed at developing intelligent systems that monitor human-related processes, and provide appropriate support. More specifically, an explicit domain model of crime displacement has been developed, and, on top of that, model-based reasoning techniques are applied to the domain model, in order to analyze which environmental circumstances result in which crime rates, and to determine which support measures are most appropriate. The model can be used as an analytical tool for researchers and policy makers to perform thought experiments, i.e., to shed more light on the process under investigation, and possibly improve existing policies (e.g., for surveillance). The basic concepts of the model are defined in such a way that it can be directly connected to empirical information.