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Understanding policing demand and deployment through the lens of the city and with the application of big data

Author

Listed:
  • Mark Ellison

    (Manchester Metropolitan University, UK)

  • Jon Bannister

    (Manchester Metropolitan University, UK)

  • Won Do Lee

    (Oxford University, UK)

  • Muhammad Salman Haleem

    (University of Warwick, UK)

Abstract

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

Suggested Citation

  • Mark Ellison & Jon Bannister & Won Do Lee & Muhammad Salman Haleem, 2021. "Understanding policing demand and deployment through the lens of the city and with the application of big data," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3157-3175, November.
    • Handle: RePEc:sae:urbstu:v:58:y:2021:i:15:p:3157-3175
    DOI: 10.1177/0042098020981007
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    References listed on IDEAS

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    Cited by:

    1. Jon Bannister & Anthony O’Sullivan, 2021. "Big Data in the city," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3061-3070, November.

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