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Community fire prevention via population segmentation modelling

Taylor, MJ, Higgins, E, Lisboa, P, Jarman, I and Hussain, A (2015) Community fire prevention via population segmentation modelling. Community Development Journal. ISSN 1468-2656

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In this paper we examine the use of population segmentation modelling for targeting fire prevention to the needs of the community. A population segmentation approach based upon socio-economic characteristics data was developed to provide a deeper understanding of the fire risks associated with different social groups by a partnership consisting of a UK fire and rescue service, a National Health Service trust, a local council, and a police force. This approach supported more targeted and co-ordinated community fire prevention measures by the agencies involved. This approach was used to target those most at risk, and improve intra-agency co-ordination and collaboration between the agencies involved. The modelling enabled differences in terms of the risk of fire related injuries and fatalities between the population segments to be examined. Overall, the research examines how and why population segmentation was undertaken by the fire and rescue service studied, and how this was implemented and used operationally to support fire prevention activities. The project was funded by the UK Department of Communities and Local Government.

Item Type: Article
Additional Information: This is a pre-copyedited, author-produced PDF of an article accepted for publication in Community Development Journal following peer review. The version of record Community Dev J (2015)doi: 10.1093/cdj/bsv006 is available online at: http://dx.doi.org/10.1093/cdj/bsv006
Uncontrolled Keywords: 1604 Human Geography, 1608 Sociology, 1605 Policy And Administration
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher: Oxford University Press
Date Deposited: 21 Mar 2016 14:07
Last Modified: 27 Apr 2022 13:58
DOI or ID number: 10.1093/cdj/bsv006
URI: https://researchonline.ljmu.ac.uk/id/eprint/3159

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