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Making realistic predictions about the occurrence of crime is a challenging research area. City-wide crime patterns depend on the behaviour and interactions of a huge number of people (including victims, offenders, and passers-by) as well as a multitude of environmental factors. Modern criminology theory has highlighted the individual-level nature of crime—whereby overall crime rates emerge from individual crimes that are committed by individual people in individual places—but traditional modelling methodologies struggle to capture the complex dynamics of the system. The decision whether or not to commit a burglary, for example, is based on a person’s unique behavioural circumstances and the immediate surrounding environment. To address these problems, individual-level simulation techniques such as agent-based modelling have begun to spread to the field of criminology. These models simulate the behaviour of individual people and objects directly; virtual ‘agents’ are placed in an environment that allows them to travel through space and time, behaving as they would do in the real world. We outline an advanced agent-based model that can be used to simulate occurrences of residential burglary at an individual level. The behaviour within the model closely represents criminology theory and uses real-world data from the city of Leeds, UK as an input. We demonstrate the use of the model to predict the effects of a real urban regeneration scheme on local households. Keywords: crime simulation, burglary, agent-based modelling, offender behaviour

Making realistic predictions about the occurrence of crime is a challenging research area. City-wide crime patterns depend on the behaviour and interactions of a huge number of people (including victims, offenders, and passers-by) as well as a multitude of environmental factors. Modern criminology theory has highlighted the individual-level nature of crime—whereby overall crime rates emerge from individual crimes that are committed by individual people in individual places—but traditional modelling methodologies struggle to capture the complex dynamics of the system. The decision whether or not to commit a burglary, for example, is based on a person's unique behavioural circumstances and the immediate surrounding environment. To address these problems, individual-level simulation techniques such as agent-based modelling have begun to spread to the field of criminology. These models simulate the behaviour of individual people and objects directly; virtual ‘agents' are placed in an environment that allows them to travel through space and time, behaving as they would do in the real world. We outline an advanced agent-based model that can be used to simulate occurrences of residential burglary at an individual level. The behaviour within the model closely represents criminology theory and uses real-world data from the city of Leeds, UK as an input. We demonstrate the use of the model to predict the effects of a real urban regeneration scheme on local households.

Occurrences of crime are complex phenomena. They are the result of a large number of interrelated elements which can include environmental factors as well as complex human behaviours. Traditionally, crime occurrences have been modelled using statistical techniques, and although such approaches are useful, they face difficulties in providing predictive analyses and with the integration of behavioural information. Also, it is particularly difficult to account for the strongly influential effect of local urban form. Agent-based modelling is a relatively new modelling paradigm that has generated a considerable amount of interest. An agent is an independent component of a system which interacts with other agents and its environment to achieve goals. In this manner, large systems of agents can be created to mimic real scenarios. Most importantly, the agents can incorporate behavioural information to determine how they should achieve their goals, and models can include a highly detailed environment. This paper presents an agent-based model used to predict burglary rates, which, despite its simplicity, yields interesting results. We apply the model to the city of Leeds, UK. The model indicates that the urban configuration in Leeds is a major element in determining the level of crime across the city. It also demonstrates that agent-based modelling is an excellent tool for these types of analyses with much potential.