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.
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