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Using an Agent-Based Crime Simulation to Predict the Effects of Urban Regeneration on Individual Household Burglary Risk

Author

Listed:
  • Nick Malleson
  • Alison Heppenstall
  • Linda See

    (International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria)

  • Andrew Evans

    (School of Geography, University of Leeds, Leeds LS2 9JT, England)

Abstract

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.

Suggested Citation

  • Nick Malleson & Alison Heppenstall & Linda See & Andrew Evans, 2013. "Using an Agent-Based Crime Simulation to Predict the Effects of Urban Regeneration on Individual Household Burglary Risk," Environment and Planning B, , vol. 40(3), pages 405-426, June.
    • Handle: RePEc:sae:envirb:v:40:y:2013:i:3:p:405-426
    DOI: 10.1068/b38057
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    References listed on IDEAS

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    1. Dan Vickers & Phil Rees, 2007. "Creating the UK National Statistics 2001 output area classification," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 379-403, March.
    2. Andromachi Tseloni, 2006. "Multilevel modelling of the number of property crimes: household and area effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 205-233, March.
    3. David O'Sullivan & Mordechai Haklay, 2000. "Agent-Based Models and Individualism: Is the World Agent-Based?," Environment and Planning A, , vol. 32(8), pages 1409-1425, August.
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