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In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

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In modern societies where fighting crime has a long history, establishing effective methods for crime prevention is of high significance. For this purpose, police departments worldwide undertake efforts to analyze past crime data. They aim to detect the most prolific crime areas and predict their development, in order to direct their prevention efforts. In parallel, scholars investigate possibilities to build crime prediction models, by applying various techniques, from simple regression to data mining. Acknowledging the latest advances in this field, which suggest that Agent-Based Modeling (ABM) is a promising method, in this paper we present the design of an ABM capable of predicting where and when future crimes will most probably happen. We extend the previous work by accounting for offender behavior and integrating a realistic representation of the environment. In contrast to existing models, past crime data will be included to achieve automatic calibration. Furthermore, we will assess how crime data can be used to model agent's behavior and we will include environmental data stepwise, in order to achieve the optimal balance between prediction accuracy and complexity. The resulting ABM will be developed as a crime prediction tool, and as an experimental environment to test prevention strategies.