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Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

Borrion, H; Kurland, J; Tilley, N; Chen, P; (2020) Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China. PLoS One , 15 (10) , Article e0240077. 10.1371/journal.pone.0240077. Green open access

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Abstract

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

Type: Article
Title: Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0240077
Publisher version: https://doi.org/10.1371/journal.pone.0240077
Language: English
Additional information: © 2020 Borrion et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10110534
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