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

Mark Ellison, Jon Bannister, Won Do Lee and Muhammad Salman Haleem
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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

Urban Studies, 2021, vol. 58, issue 15, 3157-3175

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.

Keywords: anti-social behaviour; big data; crime; policing; public welfare and safety; å 社会行为; å¤§æ•°æ ®; 犯罪; 警务; å…¬å…±ç¦ åˆ©å’Œå®‰å…¨ (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:58:y:2021:i:15:p:3157-3175

DOI: 10.1177/0042098020981007

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