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A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

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  • Lianfa Li
  • Jinfeng Wang
  • Hareton Leung
  • Sisi Zhao

Abstract

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.

Suggested Citation

  • Lianfa Li & Jinfeng Wang & Hareton Leung & Sisi Zhao, 2012. "A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk," Risk Analysis, John Wiley & Sons, vol. 32(6), pages 1072-1092, June.
  • Handle: RePEc:wly:riskan:v:32:y:2012:i:6:p:1072-1092
    DOI: 10.1111/j.1539-6924.2012.01790.x
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    References listed on IDEAS

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    1. A. Amendola & Y. Ermoliev & T. Ermolieva & V. Gitis & G. Koff & J. Linnerooth-Bayer, 2000. "A Systems Approach to Modeling Catastrophic Risk and Insurability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 21(2), pages 381-393, May.
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    1. Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    2. Bruce Tonn & Dorian Stiefel, 2013. "Evaluating Methods for Estimating Existential Risks," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1772-1787, October.
    3. Abroon Qazi & Mecit Can Emre Simsekler & Steven Formaneck, 2023. "Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation," Annals of Operations Research, Springer, vol. 322(1), pages 241-272, March.
    4. Rui Liu & Yun Chen & Jianping Wu & Lei Gao & Damian Barrett & Tingbao Xu & Xiaojuan Li & Linyi Li & Chang Huang & Jia Yu, 2017. "Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 756-773, April.

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