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A Stab in the Dark? Analysing Temporal Trends of Street Robbery

Bowers, KJ; Tompson, L; (2013) A Stab in the Dark? Analysing Temporal Trends of Street Robbery. Journal of Research in Crime and Delinquency , 50 (4) 616-631 . 10.1177/0022427812469114. Green and gold open access

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Abstract

Objectives: Test the influence of darkness in the street robbery crime event alongside temperature. Methods: Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year. Results: Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models. Conclusion: Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data.

Type:Article
Title:A Stab in the Dark? Analysing Temporal Trends of Street Robbery
Open access status:An open access publication. A version is also available from UCL Discovery.
DOI:10.1177/0022427812469114
Publisher version:http://dx.doi.org/10.1177/0022427812469114
Language:English
Additional information:© The Author(s) 2012. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(http://www.uk.sagepub.com/aboutus/openaccess.htm).
Keywords: causes/correlates crime trends crime; routine activity theory; criminological theory; statistical methods; quantitative research; research methods;
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Security and Crime Science

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