Modelling Residential Fire Vulnerability of Denmark

Abstract This study analyzes socio-economic demographics (including Geomatic conzoom® segmented demographic variables) as well as building and property registration information as risk factors in relation to the prevalence of residential building fires within 100 m × 100 m cells. The logistic regression model achieved a receiver operating curve (ROC) of 0.74 and a precision-recall curve of 0.12 on the testing dataset. The model identifies 19 significant variables related to the risk of residential fire. The top 5 highest performing variables in our model and their odds ratios are the following: number of people (OR 1.25), Multi/family residence-building type (OR 1.20), number of buildings (OR 1.18), conzoom® Type C—Country/Rural Communities (OR 0.85), construction year (OR 0.87). These results indicate that socio-economic factors play a large role in influencing fire vulnerability within residential areas and can help prioritize resource allocation to reduce fire vulnerability in the identified risk factor groups.

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Publication Details

Journal
Fire Technology
Published
2024-09-10
DOI
https://doi.org/10.1007/s10694-024-01633-6
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Modelling Residential Fire Vulnerability of Denmark

Hans Skov-Petersen, Stefan Oehmcke, Bo Markussen, Christian Igel et al.
Fire Technology
Flood Risk Assessment and Management
article

Modelling Residential Fire Vulnerability of Denmark

Hans Skov-Petersen, Stefan Oehmcke, Bo Markussen, Christian Igel, Patrik Karlsson Nyed, Naomi Crump
article en

Abstract

Abstract This study analyzes socio-economic demographics (including Geomatic conzoom® segmented demographic variables) as well as building and property registration information as risk factors in relation to the prevalence of residential building fires within 100 m × 100 m cells. The logistic regression model achieved a receiver operating curve (ROC) of 0.74 and a precision-recall curve of 0.12 on the testing dataset. The model identifies 19 significant variables related to the risk of residential fire. The top 5 highest performing variables in our model and their odds ratios are the following: number of people (OR 1.25), Multi/family residence-building type (OR 1.20), number of buildings (OR 1.18), conzoom® Type C—Country/Rural Communities (OR 0.85), construction year (OR 0.87). These results indicate that socio-economic factors play a large role in influencing fire vulnerability within residential areas and can help prioritize resource allocation to reduce fire vulnerability in the identified risk factor groups.

Fire TechnologyVol. 61(2)
University of Copenhagen (DK), Danish Emergency Management Agency (DK), Department of Mathematical Sciences (RU)
Københavns Universitet
Openalex Percentile: Top 86%
Flood Risk Assessment and Management
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Modelling Residential Fire Vulnerability of Denmark — Hans Skov-Petersen, Stefan Oehmcke, et al. · Fire Technology (2024) | TGRS Research Map | TGRS