Show simple item record

dc.contributor.advisorWoods, Paul
dc.contributor.advisorHill, Rodney
dc.creatorKim, Ji Myong
dc.date.accessioned2013-12-16T20:04:59Z
dc.date.available2015-08-01T05:48:26Z
dc.date.created2013-08
dc.date.issued2013-07-31
dc.date.submittedAugust 2013
dc.identifier.urihttps://hdl.handle.net/1969.1/151152
dc.description.abstractFollowing 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 currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane 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 (GIS) to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage and ratio. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectHurricane Ikeen
dc.subjectDamage predictionen
dc.subjectTexas Windstorm Insurance Associationen
dc.subjectClaim payouten
dc.subjectRatioen
dc.titleMultiple Linear Regression Models: Predicting the Texas Windstrom Insurance Association Claim Payout and Ratio Versus the Appraised Value of Commercial Buildings from Hurricae Ikeen
dc.typeThesisen
thesis.degree.departmentArchitectureen
thesis.degree.disciplineArchitectureen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberChoudhury, Ifte
dc.contributor.committeeMemberJun, Mikyoung
dc.type.materialtexten
dc.date.updated2013-12-16T20:04:59Z
local.embargo.terms2015-08-01


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record