IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v40y2013i3p405-426.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b38057
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b38057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bardsley, Nicholas & Büchs, Milena & James, Patrick & Papafragkou, Anastasios & Rushby, Thomas & Saunders, Clare & Smith, Graham & Wallbridge, Rebecca & Woodman, Nicholas, 2019. "Domestic thermal upgrades, community action and energy saving: A three-year experimental study of prosperous households," Energy Policy, Elsevier, vol. 127(C), pages 475-485.
    2. Johannes Gräb & Michael Grimm, 2008. "Spatial inequalities explained - Evidence from Burkina Faso," Ibero America Institute for Econ. Research (IAI) Discussion Papers 173, Ibero-America Institute for Economic Research.
    3. Federico Benassi & Marica D'Elia & Francesca Petrei, 2021. "The “meso” dimension of territorial capital: Evidence from Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 159-175, February.
    4. Richard Harris & Yingyu Feng, 2016. "Putting the geography into geodemographics: Using multilevel modelling to improve neighbourhood targeting – a case study of Asian pupils in London," Journal of Marketing Analytics, Palgrave Macmillan, vol. 4(2), pages 93-107, July.
    5. Alex Fenton, 2013. "Small-area measures of income poverty," CASE Papers case173, Centre for Analysis of Social Exclusion, LSE.
    6. repec:cep:sticas:/173 is not listed on IDEAS
    7. Holt, Alison R. & Mears, Meghann & Maltby, Lorraine & Warren, Philip, 2015. "Understanding spatial patterns in the production of multiple urban ecosystem services," Ecosystem Services, Elsevier, vol. 16(C), pages 33-46.
    8. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    9. Mordechai Haklay & David O'Sullivan & Mark Thurstain-Goodwin & Thorsten Schelhorn, 2001. "“So Go Downtown†: Simulating Pedestrian Movement in Town Centres," Environment and Planning B, , vol. 28(3), pages 343-359, June.
    10. Burton Lucy & Johnson Shane D. & Braithwaite Alex, 2017. "Potential uses of Numerical Simulation for the Modelling of Civil Conflict," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 23(1), pages 1-39, January.
    11. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    12. Brian Robson & Kitty Lymperopoulou & Alasdair Rae, 2008. "People on the Move: Exploring the Functional Roles of Deprived Neighbourhoods," Environment and Planning A, , vol. 40(11), pages 2693-2714, November.
    13. Mohamed Amara, 2015. "Multilevel Modelling of Individual Fertility Decisions in Tunisia: Household and Regional Contextual Effects," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 477-499, November.
    14. Rosaria Conte & Bruce Edmonds & Scott Moss & R. Keith Sawyer, 2001. "Sociology and Social Theory in Agent Based Social Simulation: A Symposium," Computational and Mathematical Organization Theory, Springer, vol. 7(3), pages 183-205, October.
    15. Demetris Lamnisos & Nicos Middleton & Nikoletta Kyprianou & Michael A. Talias, 2019. "Geodemographic Area Classification and Association with Mortality: An Ecological Study of Small Areas of Cyprus," IJERPH, MDPI, vol. 16(16), pages 1-13, August.
    16. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.
    17. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
    18. Jonathan Reades & Jordan De Souza & Phil Hubbard, 2019. "Understanding urban gentrification through machine learning," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 922-942, April.
    19. Helen Briassoulis, 2008. "Land-Use Policy and Planning, Theorizing, and Modeling: Lost in Translation, Found in Complexity?," Environment and Planning B, , vol. 35(1), pages 16-33, February.
    20. Alexander D. Singleton & Paul A. Longley, 2009. "Creating open source geodemographics: Refining a national classification of census output areas for applications in higher education," Papers in Regional Science, Wiley Blackwell, vol. 88(3), pages 643-666, August.
    21. Cormac O'Dea & Ian Preston, 2012. "The distributional impact of public spending in the UK," IFS Working Papers W12/06, Institute for Fiscal Studies.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:40:y:2013:i:3:p:405-426. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.