Buil-Gil, D, Brunton-Smith, I, Pina Sanchez, J orcid.org/0000-0002-9416-6022 et al. (1 more author) (2022) Comparing measurements of violent crime in local communities: a case study in Islington, London. Police Practice and Research, 23 (4). pp. 489-506. ISSN 1561-4263
Abstract
Police-recorded crime data are prone to measurement error, affecting our understanding of the nature of crime. Research has responded to this problem using data from surveys and emergency services. These data sources are not error-free, and data from different sources are not always easily comparable. This study compares violent crime data recorded by police, ambulance services, two surveys and computer simulations in Islington, London. Different data sources show remarkably different results. However, crime estimates become more similar, but still show different distributions, when crime rates are calculated using workday population as the denominator and log-transformed. Normalising crime rates by workday population controls for the fact that some data sources reflect offences’ location while others refer to victims’ residence, and log-transforming rates mitigates the biasing effect associated with some multiplicative forms of measurement error. Comparing multiple data sources allows for more accurate descriptions of the prevalence and distribution of crime.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of an article published in Police Practice and Research. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Police data; crime surveys; crime mapping; measurement error; official statistics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/T015667/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Mar 2022 14:51 |
Last Modified: | 16 Sep 2023 00:13 |
Status: | Published |
Publisher: | Routledge |
Identification Number: | 10.1080/15614263.2022.2047047 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184810 |