IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v36y2009i6p1103-1123.html
   My bibliography  Save this article

An Agent-Based Model of Burglary

Author

Listed:
  • Nick Malleson
  • Andrew Evans
  • Tony Jenkins

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

Abstract

Occurrences of crime are complex phenomena. They are the result of a large number of interrelated elements which can include environmental factors as well as complex human behaviours. Traditionally, crime occurrences have been modelled using statistical techniques, and although such approaches are useful, they face difficulties in providing predictive analyses and with the integration of behavioural information. Also, it is particularly difficult to account for the strongly influential effect of local urban form. Agent-based modelling is a relatively new modelling paradigm that has generated a considerable amount of interest. An agent is an independent component of a system which interacts with other agents and its environment to achieve goals. In this manner, large systems of agents can be created to mimic real scenarios. Most importantly, the agents can incorporate behavioural information to determine how they should achieve their goals, and models can include a highly detailed environment. This paper presents an agent-based model used to predict burglary rates, which, despite its simplicity, yields interesting results. We apply the model to the city of Leeds, UK. The model indicates that the urban configuration in Leeds is a major element in determining the level of crime across the city. It also demonstrates that agent-based modelling is an excellent tool for these types of analyses with much potential.

Suggested Citation

  • Nick Malleson & Andrew Evans & Tony Jenkins, 2009. "An Agent-Based Model of Burglary," Environment and Planning B, , vol. 36(6), pages 1103-1123, December.
    • Handle: RePEc:sae:envirb:v:36:y:2009:i:6:p:1103-1123
    DOI: 10.1068/b35071
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b35071
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b35071?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. Michael Batty, 2005. "Agents, Cells, and Cities: New Representational Models for Simulating Multiscale Urban Dynamics," Environment and Planning A, , vol. 37(8), pages 1373-1394, August.
    2. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    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. Verda Kocabas & Suzana Dragicevic, 2013. "Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model," Journal of Geographical Systems, Springer, vol. 15(4), pages 403-426, October.
    2. D'Acci, Luca, 2013. "A Modern Postmodern Urbanism The Systemic Retroactive game (SyR) between Bottom-up and Top-down," MPRA Paper 48991, University Library of Munich, Germany.
    3. Matteo Richiardi, 2003. "The Promises and Perils of Agent-Based Computational Economics," LABORatorio R. Revelli Working Papers Series 29, LABORatorio R. Revelli, Centre for Employment Studies.
    4. Johannes Dahlke & Kristina Bogner & Matthias Mueller & Thomas Berger & Andreas Pyka & Bernd Ebersberger, 2020. "Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)," Papers 2003.11985, arXiv.org.
    5. Mehdi Sheikh Goodarzi & Yousef Sakieh & Shabnam Navardi, 2017. "Scenario-based urban growth allocation in a rapidly developing area: a modeling approach for sustainability analysis of an urban-coastal coupled system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(3), pages 1103-1126, June.
    6. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    7. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    8. Ugo Merlone & Daren Sandbank & Ferenc Szidarovszky, 2013. "Equilibria analysis in social dilemma games with Skinnerian agents," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 12(2), pages 219-233, November.
    9. Andrew Hoegh & Frank T. Manen & Mark Haroldson, 2021. "Agent-Based Models for Collective Animal Movement: Proximity-Induced State Switching," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 560-579, December.
    10. Zhai, Xueting & Zhong, Dixi & Luo, Qiuju, 2019. "Turn it around in crisis communication: An ABM approach," Annals of Tourism Research, Elsevier, vol. 79(C).
    11. Giovanni Dosi & Andrea Roventini, 2017. "Agent-Based Macroeconomics and Classical Political Economy: Some Italian Roots," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 261-283, November.
    12. Lamieri, Marco & Bertacchini, Enrico, 2006. "What if Hayek goes shopping in the bazaar?," MPRA Paper 367, University Library of Munich, Germany, revised 21 Jun 2006.
    13. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    14. Tao Zhang & William J. Nuttall, 2007. "An Agent Based Simulation of Smart Metering Technology Adoption," Working Papers EPRG 0727, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    15. Aringhieri, Roberto & Duma, Davide & Fragnelli, Vito, 2018. "Modeling the rational behavior of individuals on an e-commerce system," Operations Research Perspectives, Elsevier, vol. 5(C), pages 22-31.
    16. Gomes, Sharlene L. & Hermans, Leon M. & Thissen, Wil A.H., 2018. "Extending community operational research to address institutional aspects of societal problems: Experiences from peri-urban Bangladesh," European Journal of Operational Research, Elsevier, vol. 268(3), pages 904-917.
    17. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    18. Pietro Terna, 2000. "Sum: A Surprising (Un)Realistic Market - Building A Simple Stock Market Structure With Swarm," Computing in Economics and Finance 2000 173, Society for Computational Economics.
    19. Ali Naqvi & Miriam Rehm, 2014. "A multi-agent model of a low income economy: simulating the distributional effects of natural disasters," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 275-309, October.
    20. Dimitris Ballas & Richard Kingston & John Stillwell & Jianhui Jin, 2007. "Building a Spatial Microsimulation-Based Planning Support System for Local Policy Making," Environment and Planning A, , vol. 39(10), pages 2482-2499, October.

    More about this item

    Statistics

    Access and download statistics

    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:36:y:2009:i:6:p:1103-1123. 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.