Estimating the Texas Windstorm Insurance Association claim payout of commercial buildings from Hurricane Ike

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• 作者：J. M. Kim ; P. K. Woods ; Y. J. Park ; K. Son
• 关键词：Hurricanes ; Damage ; Natural disasters ; Insurance ; Texas
• 刊名：Natural Hazards
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：84
• 期：1
• 页码：405-424
• 全文大小：1,083 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：84

文摘

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there is currently a shortage of comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and insured losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hazard indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple regression method has been applied to develop hurricane damage prediction models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage. Exposures, built environment vulnerability indicators, and hazard indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies and emergency planners to establish the hurricane damage mitigation strategies. In addition, insurance companies could utilize the model to predict hurricane damage.