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Using Geographically Weighted Regression to Explore Local Crime Patterns
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Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems
Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems
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Explaining and modelling crime patterns is an exercise that has taxed policy-makers, criminologists, social reformers and the police ever since the first crime patterns were recorded. Crime is a particularly difficult phenomenon to model because of its inherent complexity; crime patterns are built up from a multitude of human-human and human-environment micro-interactions that ultimately lead to individual crime events. Commonly used modelling techniques, such as regression, struggle to fully account for the dynamics of the crime system. They work at aggregate scales thereby disregarding important individual-level variation and also struggle to account for the effects of different types of human behaviour. Furthermore, important concepts from environmental criminology - such as individual offender awareness spaces or heterogeneity in offender decision-making - cannot be included directly when working at a resolution above that of the individual.This research addresses the drawbacks associated with traditional mathematical crime models by building an agent-based simulation with a unique offender behavioural model. Through use of the PECS framework for modelling human behaviour, agents are endowed with needs and motives that drive their behaviour and ultimately lead to the commission of crime. As the model uses real-world environmental data, it can be used to make predictions in existing cities. The paper demonstrates that use of this framework, in combination with an agent-based model, can replicate patterns and trends that are supported by the current theoretical understanding of offending behaviour.