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Beschreibung:
Crime exacts high financial, physical, and emotional costs from individuals and societies. Therefore, there is a great deal of interest in reducing it. Criminology and other disciplines have devoted efforts to developing tools supporting crime reduction and prevention strategies. In the same vein, researchers and practitioners have shown that it is possible to curtail criminality by reducing the availability of crime opportunities. This approach has proven more effective than other measures such as, for example, imposing harsh sentences on convicted criminals with the aim of setting an example that discourages other potential lawbreakers from committing crimes. Crime is understood as an interaction between an offender and their environment, and criminal offenders are seen as rather rational decision-makers acting upon available opportunities to maximize their own gains. Building upon these ideas, numerous strategies have emerged to identify locations that present a greater number of opportunities for criminal behavior. For these strategies to be effective, they need to be targeted at areas with a higher risk of crime. These locations are often characterized by specific features, which can be captured by data. Thus, modern approaches model crime by integrating features of the environment with historic crime data. This leads to rather accurate crime prediction models, allowing practitioners to reduce crime by planning interventions at the right times and locations. Crime prediction models have long been built using statistical techniques. The potential of these techniques is that they can integrate a large variety of spatio-temporal features to build accurate predictive models. On the downside, they cannot account for interactions between the elements of the model on an individual level, which are very characteristic of social phenomena. Simulation techniques can overcome this shortcoming. Indeed, agent-based modeling allows one to represent dynamic individual agent behavior in its context, including interactions ...