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Agent-Based Models of Geographical Systems pp 411–434Cite as

Using Agent-Based Models to Simulate Crime

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Abstract

Due to the complexity of human behaviour and the intricacies of the urban environment, it is extremely difficult to understanding and model crime patterns. Nevertheless, a greater understanding of the processes and drivers behind crime is essential for researchers to be able to properly model crime and for policy-makers to be able to predict the potential effects of their interventions. Traditional mathematical models that use spatially aggregated data struggle to capture the low-level dynamics of the crime system – such as an individual person’s behaviour – and hence fail to encapsulate the factors that characterise the system and lead to the emergence of city-wide crime rates.

This chapter will outline a realistic agent-based model that can be used to simulate, at the level of individual houses and offenders, occurrences of crime in a real city. In particular, the research focuses on the crime of residential burglary in the city of Leeds, UK. The model is able to predict which places might have a heightened burglary risk as a direct result of a real urban regeneration scheme in the local area.

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Notes

  1. 1.

    Census data is published through CASWEB (Mimas 2010), For more information about the census see Rees et al. (2002a, 2002b)

  2. 2.

    For more information about space syntax techniques, refer to Hiller and Hanson (1984), Bafna (2003) or Park (2005).

  3. 3.

    Legitimate employment (whether full-time or temporary) is also common and has been included in the model, but is not a feature that is used in the later case studies.

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  1. School of Geography, University of Leeds, Leeds, UK

    Nicolas Malleson

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Editors and Affiliations

  1. School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, United Kingdom

    Alison J. Heppenstall

  2. , Department of Computational Social Scien, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    Andrew T. Crooks

  3. International Institute of Applied Syste, Schlossplatz 1, Laxenburg, 2361, Austria

    Linda M. See

  4. Ctr. Advanced Spatial Analysis, University College London, Torrington Place 1-19, London, WC1E 6BT, United Kingdom

    Michael Batty

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Malleson, N. (2012). Using Agent-Based Models to Simulate Crime. In: Heppenstall, A., Crooks, A., See, L., Batty, M. (eds) Agent-Based Models of Geographical Systems. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8927-4_19

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