Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study

Corcoran, Jonathan; Higgs, Gary; Rohde, David; Chhetri, Prem

doi:10.1007/s10109-009-0102-z

Cited by 50 publications

(33 citation statements)

References 33 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%

“…Comparatively, less attention has been paid to urban fires. A few exceptions include recent work by Corcoran et al (2007aCorcoran et al ( , 2007bCorcoran et al ( , 2011aCorcoran et al ( , 2011b, Asgary et al (2010) and Špatenková and Virrantaus (2013). Compared to wildfire, urban fire is virtually a both physical and social process (Jennings, 2013), in the sense that it occurs in built environment and affects individuals and socioeconomic activities in the surrounding neighborhood or communities, usually involving both individual causalities and economic losses.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to wildfire, urban fire is virtually a both physical and social process (Jennings, 2013), in the sense that it occurs in built environment and affects individuals and socioeconomic activities in the surrounding neighborhood or communities, usually involving both individual causalities and economic losses. Meanwhile, most of the existing work on urban fires has focused on the cases in developed countries, such as the US (Gaither et al, 2011), the UK (Corcoran et al, 2007a(Corcoran et al, , 2007b(Corcoran et al, , 2011a(Corcoran et al, , 2011bHiggins et al, 2013), Canada (Asgary et al, 2010) and Australia (Corcoran et al, 2011a). So far, few studies have been carried out in developing countries and regions.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In the Western Amazon, drought intensity was the most important predictor of fire occurrence in local scale [24]. In Queensland, Australia, a high fire occurrence rate is related to high temperature [28]. Gunther found that, because there are few fixed heating systems in the rural parts of South America, the death rate from fires increased on cold days owing to the risks from using other types of heating [29].…”

Section: Introductionmentioning

confidence: 99%

“…He argued that both the population and buildings are distributed along a certain density gradient; therefore, the rate of fire accidents is unevenly distributed [36]. Corcoran et al analyzed the temporal and spatial change based on temporal and spatial exploratory analysis by using Queensland as an example [28]. Chang adopted the Spatial Lag Model (SLM) and the Spatial Error Correction Model (SEM) to analyze the relationship between the fire incidence rate and spatial factors, using Tainan City as an example [37].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity

Wang

Zhang

2015

Sustainability

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Frequent fire accidents pose a serious threat to human life and property. The spatio-temporal features of China's urban fires, and their drivers should be investigated. Based on the Spatio-temporal Dynamic panel data Model (SDM), and using fire data gathered from 337 Chinese cities in 2000 to 2009, the influence of spatio-temporal factors on the frequency of urban fires was analyzed. The results show that (1) the overall fire incidence of China increased annually before 2002 and reduced significantly after 2003, and then high fire incidence increased in western China; (2) Spatio-temporal factors play a significant role in the frequency of Chinese urban fires; specifically, the fire assimilation effect, fire inertia effect and fire caution effect. The ratio of fire incidence of China has reduced significantly, and the focus of fire incidence moved towards the western region of China. GDP and humidity have a significant effect on urban fire situation change in China, and these effects may be referred to as "fire assimilation effects", "fire inertia effects" and "fire caution effects". OPEN ACCESSSustainability 2015, 7 9735 Keywords: urban fire; spatio-temporal features; SDM, humidity; GDP; the fire assimilation effect; fire inertia effect; fire caution effect

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A method to improve the determination of ignition probability in buildings based on Bayesian network

Shu

Shen

et al. 2021

Fire and Materials

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Summary The traditional research of building fire probability analysis is from statistics or fire science. This paper combines the two methods and aims to improve the statistical method of building ignition probability determination according to the research conclusion of fire science. The specific factors that affect the ignition probability are divided into three aspects: humans, ignition sources and combustibles and environments. On this basis, the Bayesian network of building ignition probability is constructed, the nodes and conditional probability table in the Bayesian network are introduced in detail, according to which the ignition probability of building can be calculated quantitatively and objectively. Then some typical buildings are chosen as examples for the application of the method, the posterior probability value is calculated by obtaining the relevant building information and substituting them into the Bayesian network. The ignition probability is dynamic, and the comparison with the statistical data of building fire also proves its rationality.

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Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study

Cited by 50 publications

References 33 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

Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity

A method to improve the determination of ignition probability in buildings based on Bayesian network

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