The integrity of crime statistics: Assessing the impact of police data bias on crime mapping

David Buil-Gil, Angelo Moretti and Samuel Langton
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David Buil-Gil: University of Manchester

No myfhp, SocArXiv from Center for Open Science

Abstract: Police-recorded crimes are used by police forces to map crime patterns and design spatially-targeted strategies. Nevertheless, maps of crimes known to police are affected by selection biases driven by unequal crime reporting rates across social groups. This paper presents a simulation study to analyse the impact of selection biases on crime maps produced at different spatial scales. Based on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the relative difference between all crimes and police-recorded incidents at the different spatial scales. While the average relative difference between all crimes and those known to police is 62% across all geographical scales, the measures of dispersion of the relative difference are larger when crimes are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating the prevalence of crime in certain places while overestimating it in others. Micro-level crime maps are affected by a larger risk of bias than maps produced at larger scales.

Date: 2020-06-25
New Economics Papers: this item is included in nep-law and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:myfhp

DOI: 10.31219/osf.io/myfhp

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