Using synthetic crime data to understand patterns of police under-counting at the local level

Ian Brunton-Smith, David Buil-Gil, Jose Pina-Sánchez, Alexandru Cernat, Angelo Moretti

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

It is widely known that police recorded crime data provides only a partial picture of the true extent of crimes, with surveys identifying a large number of ‘hidden’ victims. Often referred to as the dark figure of crime, this gap between police records and ‘true’ level of crime has been attributed to a range of influences including an unwillingness for some victims to report their experiences to the police, coupled with selectivity in police recording practices and errors in the translation of police records into official statistics. Studies have also demonstrated the potentially severe implications of failing to account for these hidden crimes for the veracity of models of crime data. Comparing police records against victim surveys presents us with a potential framework to generate corrected model estimates. But to date we know little about the nature of underreporting and recording at the local area level, with victim surveys generally only suitable for regional or broad police force level comparisons. In this chapter we explore a novel solution to this problem, using a synthetic population dataset to examine the extent that police recording practices vary systematically across England and Wales. Designed to match the UK population on basic demographics as measured by the Census, and with each resident given a victimisation profile derived from the Crime Survey for England and Wales, this synthetic population enables an examination of the extent of crime undercounting at a range of spatial scales.
Original languageEnglish
Title of host publicationThe Crime Data Handbook
EditorsLaura Huey, David Buil-Gil
Place of PublicationBristol
PublisherBristol University Press
Publication statusPublished - Apr 2024

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