Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature

Jennings, Charles R.

doi:10.1016/j.firesaf.2013.07.002

Cited by 112 publications

(99 citation statements)

References 17 publications

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“…Extensive literature has examined the potential contribution of a set of physical environmental and socioeconomic factors to the fire events (Corcoran et al, 2007a;Yassemi et al, 2008;Corcoran et al, 2011b;Jennings, 2013;Špatenková and Virrantaus, 2013), usually by multiple regression analyses. For instance, it has been found that the frequency of wildfire is closely related to forest fuel, local climate (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…elevation and aspect) (Yassemi et al, 2008). Regarding urban fires, socioeconomic variables such as population density, building conditions and household level generally have more influence compared to the physical environment (Corcoran et al, 2007a;Corcoran et al, 2011b;Jennings, 2013;Špatenková and Virrantaus, 2013). In addition, Corcoran et al (2011b) examined the impact of calendar events (e.g.…”

Section: Related Workmentioning

confidence: 99%

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Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

J.¹,

Zhang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safer living environment. Well understanding spatial-temporal dynamics of fire incidents can offer insights for potential determinants of various fire events and enable better fire risk estimation, assisting future allocation of prevention resources and strategic planning of mitigation programs. Using a 12-year (2002-2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatial-temporal dynamics of urban fire incidents. A range of exploratory spatial data analysis (ESDA) approaches and tools, such as spatial kernel density and co-maps, are employed to examine the spatial, temporal and spatial-temporal variations of the fire events. Particular attention has been paid to two types of fire incidents: residential properties and local facilities, due to their relatively higher occurrence frequencies. The results demonstrated that the amount of urban fire has greatly increased in the last decade and spatial-temporal distribution of fire events vary among different incident types, which implies varying impact of potential influencing factors for further investigation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

J.¹,

Zhang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…personal or demographics), and housing characteristics with the residential fire incidence. Socio-economic characteristics are important in predicting the residential fire risk in urban areas [1,2]. Using statistical models, Hasofer and Thomas [3] found that the most significant fire factors that contributed to the risk of casualties and fatalities in apartment fires are the extent of fire damage, area of fire origin, type of material ignited, and ignition factor.…”

Section: Introductionmentioning

confidence: 99%

The analysis of fire losses and characteristics of residential fires based on investigation data in Selangor, 2012-2014

2016

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Abstract. This is a research in progress where authors seek to investigate the factors of residential fires. As part of the research, this paper aims to analyse the fire problems faced by the community of Malaysia. Data regarding residential fires between 2012 and 2014 was collected from fire investigation reports prepared by the Selangor Fire and Rescue Department. Descriptive analysis is conducted to summarize the data collected and describe the common phenomenon of residential fires. The distributions of the fire characteristics suggested that residential fires are commonly occurred during daytime, confined within the room of fire origin, caused by electrical failure and cooking negligence, started from kitchen, and occurred in multi-units housing. Further analysis will be conducted in order to investigate the relationships between the characteristics and residential fires.

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“…In total, 25 explanatory variables were extracted from several databases, including a variety of socioeconomic attributes according to the results in previous studies [21,[23][24][25][26][27]. These variables not only consider the influence of socioeconomic conditions on fire occurrence but also consider the influence of climate and topographic conditions.…”

Section: Explanatory Variables: Selection and Pre-processingmentioning

confidence: 99%

A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

et al. 2017

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Abstract:Fire occurrence, which is examined in terms of fire density (number of fire/km 2 ) in this paper, has a close correlation with multiple spatiotemporal factors that include environmental, physical, and other socioeconomic predictors. Spatial autocorrelation exists widely and should be considered seriously for modeling the occurrence of fire in urban areas. Therefore, spatial econometric models (SE) were employed for modeling fire occurrence accordingly. Moreover, Random Forest (RF), which can manage the nonlinear correlation between predictors and shows steady predictive ability, was adopted. The performance of RF and SE models is discussed. Based on historical fire records of Hefei City as a case study in China, the results indicate that SE models have better predictive ability and among which the spatial autocorrelation model (SAC) is the best. Road density influences fire occurrence the most for SAC, while network distance to fire stations is the most important predictor for RF; they are selected in both models. Semivariograms are employed to explore their abilities to explain the spatial structure of fire occurrence, and the result shows that SAC works much better than RF. We give a further explanation for the generation of residuals between fire density and the common predictors in both models. Therefore, decision makers can make use of our conclusions to manage fire safety at the city scale.

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Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature

Cited by 112 publications

References 17 publications

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

The analysis of fire losses and characteristics of residential fires based on investigation data in Selangor, 2012-2014

A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

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