TY - JOUR
T1 - Spatial modelling and mapping of urban fire occurrence in Portugal
AU - Bispo, Regina
AU - Vieira, Francisca G.
AU - Bachir, Nádia
AU - Espadinha-Cruz, Pedro
AU - Lopes, José Pedro
AU - Penha, Alexandre
AU - Marques, Filipe J.
AU - Grilo, António
N1 - info:eu-repo/grantAgreement/FCT/Concurso de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Ciência dos dados e inteligência artificial na Administração Pública - 2019/DSAIPA%2FDS%2F0088%2F2019/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00667%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT#
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/7
Y1 - 2023/7
N2 - Fires in urban areas typically carry severe consequences. High population density together with the complexity of urban network potentially imply significant impacts in property loss, physical damage and life losses. However, despite the impact that fires may have in urban areas, research in urban fire prediction remains limited. In this study, we modelled urban fire occurrences while making a comparative analysis of different strategies to account for spatial autocorrelation. Considering space dependence in addition to a range of social-economic explanatory variables has proven to strengthen the validity of the fitted models. The spatial Durbin error model, including population density, degraded buildings density and buying power, was selected as having the best fit. This model allowed to map the estimated probability of fire occurrence across Portugal, revealing a spatial pattern with clusters centred on the two main Portuguese city districts (Lisboa and Porto). Ultimately, the analysis of the relation between the observed urban fire incidence and the actual number of fire stations in each municipality allowed to underline the need for planning the spatial configuration of fire stations, both in number and location, at a regional scale.
AB - Fires in urban areas typically carry severe consequences. High population density together with the complexity of urban network potentially imply significant impacts in property loss, physical damage and life losses. However, despite the impact that fires may have in urban areas, research in urban fire prediction remains limited. In this study, we modelled urban fire occurrences while making a comparative analysis of different strategies to account for spatial autocorrelation. Considering space dependence in addition to a range of social-economic explanatory variables has proven to strengthen the validity of the fitted models. The spatial Durbin error model, including population density, degraded buildings density and buying power, was selected as having the best fit. This model allowed to map the estimated probability of fire occurrence across Portugal, revealing a spatial pattern with clusters centred on the two main Portuguese city districts (Lisboa and Porto). Ultimately, the analysis of the relation between the observed urban fire incidence and the actual number of fire stations in each municipality allowed to underline the need for planning the spatial configuration of fire stations, both in number and location, at a regional scale.
KW - Hotspot analysis
KW - Spatial autocorrelation
KW - Spatial modelling
KW - Urban fires
UR - http://www.scopus.com/inward/record.url?scp=85159096833&partnerID=8YFLogxK
U2 - 10.1016/j.firesaf.2023.103802
DO - 10.1016/j.firesaf.2023.103802
M3 - Article
AN - SCOPUS:85159096833
SN - 0379-7112
VL - 138
JO - Fire Safety Journal
JF - Fire Safety Journal
M1 - 103802
ER -