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Typhoon insurance pricing with spatial decision support tools

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Abstract:

In disaster insurance and reinsurance, GIS has been used to visualize and manage geospatial data and to help vulnerability and risk analysis for years. However, hazard insurance is a multidisciplinary issue that involves complex factors and uncertainty. GIS, if used alone, has limited functionality due to poor incorporation of intelligence and spatial statistics. The Spatial Decision Support System (SDSS) presented in this paper, addresses some of the deficiencies of traditional GIS, by providing powerful tools to support disaster insurance pricing that involves procedural and declarative knowledge. In the SDSS, the knowledge-based system shell, using the open-source CLIPS and supporting fuzziness and uncertainty, can be applied in at least three phases: hazard simulation, fuzzy comprehensive evaluation of risk, and query for insurance pricing. The libraries of statistics and spatial statistics provide a robust support for analysis of spatial factors, including spatial correlation between zones vulnerable to hazard and spatial variation of exposures. The GIS components provide sophisticated visualization and database management support for geospatial data, helping easily locate the insured points and risk zones as well as exploratory analysis of spatial data. Standard database management interfaces are used to manage other aspatial data. COM, an industry-wide interface protocol, tightly integrates these technologies (the expert shell, GIS, spatial statistics and DBM within an integral system), and can be used to develop mixed complex algorithms in support of other COM objects. An application of typhoon insurance pricing is demonstrated with a case study in Guangdong, China. Developed as a suite of generic tools with abilities to deal with the complex problem of disaster insurance involving spatial factors and field knowledge, this prototype SDSS can also be applied to other disaster insurance and fields that involve similar spatial decision making.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/13658810412331317742

Affiliations: Institute of Remote Sensing Application Chinese Academy of Sciences Beijing 100101 China

Publication date: March 1, 2005

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