Modelling residential fire incident response times: A spatial analytic approach |
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| Affiliation: | 1. Biochemistry, Microbiology, and Molecular Biology Department, The Pennsylvania State University, 107 Whitmore Laboratory, University Park, PA 16802, USA;2. Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, Canada M5S 3H6;3. Ontario Ministry of the Environment, 125 Resources Road, Toronto, ON, Canada M9P 3V6;4. Restek Corporation, 110 Benner Circle, Bellefonte, PA 16823, USA;5. Fire and Emergency Services Training Institute, P.O. Box 6031, 2025 Courtneypark Drive East, Toronto, ON, Canada L5P 1B2;6. Department of Chemistry, McMaster University, 1280 Main Street West, Hamilton, ON, Canada L8S 4M1;1. Department of Forensic Sciences, Akita University Graduate School of Medicine, Hondo 1-1-1, Akita 010-8543, Japan;2. Department of Forensic Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto 860-8556, Japan;1. School of Geography, Planning and Environmental Management, The University of Queensland, 4072 Australia;2. Faculty of Computing, Engineering & Science,University of South Wales,Pontypridd CF37 1DL, United Kingdom;1. Centre for Environmental Safety and Risk Engineering, College of Engineering & Science, Level 2, Building 4, Werribee campus, Victoria University, Melbourne, VIC 3030, Australia;2. Discipline of Psychology, College of Arts, Room 217, Building E, Footscray Park Campus, Victoria University, Melbourne, VIC 3011, Australia |
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| Abstract: | Rapid response to fire incidents is critical as delays in the departure and arrival at the scene can have significant consequences in terms of damage, injury and death. Research on the dynamics of residential fire incident response times has barely begun, a situation arguably underpinned by limited access to disaggregate command and control data. In this paper we draw on unit record data and employ quantile regression to examine the role that socio-demographic, infrastructure characteristics and temporal factors play on response times. Results reveal that response times are slower during the winter, in locales with larger numbers of children (aged 14 years and below) and low socioeconomic households, and in areas that have more complex street layouts. We conclude through emphasising the importance of these findings in their capacity to contribute to a new evidence base to inform policy decisions from a resource allocation perspective through the spatial allocation of finite fire resources. |
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| Keywords: | Residential fire Response time Spatial pattern Quantile regression modelling |
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