No abstract

In this paper, we examine the management of unintentional dwelling fire risk through the development of a geographical information system (GIS) for dwelling fire prevention support based upon an 18-month case study in a UK fire and rescue service. Previous research into causal factors in unintentional dwelling fire incidents was used to guide the development of a multiple linear regression risk model for dwelling fire incidents that was the basis of the GIS developed. The GIS provided a more detailed analysis of unintentional dwelling fire risk factors, and enabled more targeted fire prevention activities for the identified atrisk social groups. IntroductionIn this paper, we examine the management of unintentional dwelling fire risk through the development of a geographical information system (GIS) for modelling the risk of dwelling fires within the area covered by a UK fire and rescue service. The purpose of the GIS was to assist in the reduction of unintentional dwelling fires and fatalities within a given geographical area in the UK via more targeted fire prevention initiatives. Fire prevention initiatives (Brussoni, Towner, and Hayes 2006;Rosenberg 1999;Shai 2006;Smith et al. 2007) are increasingly being viewed as an effective means of reducing incidents of unintentional dwelling fires, fire injuries and fire fatalities. In addition, from a resources viewpoint, fire prevention initiatives are increasingly considered to be an effective and efficient utilisation of fire and rescue service personnel.The fire and rescue service studied had previously utilised internal data relating to accidental dwelling fires along with indices of multiple deprivation in the area covered to determine patterns of unintentional dwelling fire incidence. This was used to inform and direct fire prevention activities. However, in order to attempt the management of unintentional dwelling fire risk through improved effectiveness of fire prevention activities, the fire and rescue service studied wished to be able to more specifically target at-risk groups for fire prevention initiatives. Initially, statistical analyses of the fire and rescue service internal accidental dwelling fire data were conducted to determine if such could provide information concerning at-risk groups. However, no significant patterns emerged from the data. The decision was then taken that data from outside organisations would need to be utilised in order to

No abstract

We examine the causal factors involved in unintentional dwelling fire incidents within the Merseyside area of the North West region of the United Kingdom. The approach of all-subsets multiple linear regression was used to develop an unintentional dwelling fire risk model for the region. The risk model was based on data obtained from UK government agencies relating to causal factors iden tified by earlier published studies. In the region studied, mental health problems, disability and residents living alone were the most significant factors associated with unintentional dwelling fire fatalities. However, in a separate model of the incidence of unintentional dwelling fires within the region, binge drinking and smoking were additional statistically significant factors.

In this paper we examine the use of population segmentation modelling for targeting fire prevention to the needs of the community. A population segmentation approach based upon socio-economic characteristics data was developed to provide a deeper understanding of the fire risks associated with different social groups by a partnership consisting of a UK fire and rescue service, a National Health Service trust, a local council, and a police force. This approach supported more targeted and co-ordinated community fire prevention measures by the agencies involved. This approach was used to target those most at risk, and improve intra-agency co-ordination and collaboration between the agencies involved. The modelling enabled differences in terms of the risk of fire related injuries and fatalities between the population segments to be examined. Overall, the research examines how and why population segmentation was undertaken by the fire and rescue service studied, and how this was implemented and used operationally to support fire prevention activities. The project was funded by the UK Department of Communities and Local Government.