This article seeks to address some of the challenges presented in the development of crime prediction models, including realistic crime-related data and currency of criminological theories that impact predictions.
It requires innovations to accurately predict crimes and offending risks due to the complex nature of human behaviors within different social contexts. In recent years, theory-based and data-driven agent-based models have become useful tools in computational criminology with the abundance of public data, the rise of computing powers, and extensive explorations of related theories and modeling tools. In this paper, the authors present CARESim, an integrated simulation environment to predict the risks of high-risk individuals committing street violent crimes. The simulation environment combines agent-based modeling with a real geographical information system and incorporates public data (e.g., environmental data, crime data, census data, and transportation data) to reproduce authentic human mobility. This simulation environment is capable of replicating crime patterns and evaluating policies. The authors demonstrate the use of this environment and illustrate how key factors, such as social networks, the neighborhood, the weather, police patrol strategies, the population, and citizens’ behaviors, impact the risk of street-level violent offending. Furthermore, the environment can support the optimization of patrol strategies and offer evaluations of different social setups that could be unfeasible or expensive to test in real life. Publisher Abstract Provided
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