Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk

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 persons 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