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Abstract
The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform the spatial and temporal patterning of crime. Being able to capture citizen mobility and delineate a crime-specific population denominator is a vital prerequisite of the endeavour to both explain and address crime. This paper introduces the concept of an exposed population-at-risk, defined as the mix of residents and non-residents who may play an active role as an offender, victim or guardian in a specific crime type, present in a spatial unit at a given time. This definition is deployed to determine the exposed population-at-risk for violent crime, associated with the night-time economy, in public spaces. Through integrating census data with mobile phone data and utilising fine-grained temporal and spatial violent crime data, the paper demonstrates the value of deploying an exposed (over an ambient) population-at-risk denominator to determine violent crime in public space hotspots on Saturday nights in Greater Manchester (UK). In doing so, the paper illuminates that as violent crime in public space rises, over the course of a Saturday evening, the exposed population-at-risk falls, implying a shifting propensity of the exposed population-at-risk to perform active roles as offenders, victims and/or guardians. The paper concludes with a discussion of the theoretical and policy relevance of these findings.
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1 Manchester Metropolitan University, Crime and Well-being Big Data Centre, Manchester, UK (GRID:grid.25627.34) (ISNI:0000 0001 0790 5329)
2 Manchester Metropolitan University, Crime and Well-being Big Data Centre, Manchester, UK (GRID:grid.25627.34) (ISNI:0000 0001 0790 5329); University of Oxford, Transport Studies Unit, School of Geography and the Environment, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948)