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Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method

Marchment, Z; Gill, P; (2021) Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method. Crime Science , 10 , Article 12. 10.1186/s40163-021-00149-6. Green open access

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Abstract

Background: Several studies have tested the reliability of Risk Terrain Modelling (RTM) by focusing on different geographical contexts and types of crime or events. However, to date, there has been no attempt to systematically review the evidence on whether RTM is effective at predicting areas at high risk of events. This paper reviews RTM’s efficacy as a spatial forecasting method. / Methods: We conducted a systematic review and meta-analysis of the RTM literature. We aggregated the available data from a sample of studies that measure predictive accuracy and conducted a proportion meta-analysis on studies with appropriate data. / Results: In total, we found 25 studies meeting the inclusion criteria. The systematic review demonstrated that RTM has been successful in identifying at risk places for acquisitive crimes, violent crimes, child maltreatment, terrorism, drug related crimes and driving while intoxicated (DWI). The proportion meta-analysis indicated that almost half of future cases in the studies analysed were captured in the top ten per cent of risk cells. This typically covers a very small portion of the full study area. / Conclusions: The study demonstrates that RTM is an effective forecasting method that can be applied to identify places at greatest risk of an event and can be a useful tool in guiding targeted responses to crime problems.

Type: Article
Title: Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s40163-021-00149-6
Publisher version: https://doi.org/10.1186/s40163-021-00149-6
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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/10130422
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