Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk

Abstract

Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters.

Keywords:

• spatial analysis
• Bayesian network
• insurance pricing
• vulnerability
• data mining

Acknowledgements

The authors appreciate the constructive comments from the anonymous referees. They are also thankful to the support from the NSFC grant (40601077/D0120 and 40471111/D0120), MOST grants (2007AA12Z233 and O88RA204SA), and PolyU grant (H-ZG20).