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Estimation of the Risk Management Index (RMI) using statistical analysis

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  • David Novelo-Casanova
  • Gerardo Suárez

Abstract

Based on a statistical analysis, we developed a methodology to determine the Risk Management Index (RMI) at the local level. The algorithm is transparent, relatively easy to update periodically by the affected communities themselves, and the results are easy to understand by public policymakers. The main characteristics of this tool are: (1) It considers disaster management issues at the local level; (2) RMI values are obtained using a statistical analysis; (3) levels of performance are classified in a scale of numbers ranging from 0 to 5, where 0 = nonexistent, 1 = low, 2 = incipient, 3 = significant, 4 = outstanding, and 5 = optimal; (4) the weight of the indicators is determined using the analytic hierarchy process. As case studies we applied this methodology to the districts of Iztapalapa and Xochimilco in Mexico City, Mexico. Our results indicate that, to date, the Xochimilco District has not implemented any actions designed to reduce risk or to provide financial protection. Low performance was measured also in risk identification and disaster management. The Iztapalapa District has an outstanding level of performance in risk identification. However, its score is low in activities related to risk reduction, disaster management, and financial protection. The RMIs obtained in both communities highlight the need for developing permanent programs for disaster prevention, mitigation, and response. The methodology used here is designed to aid in evaluating and understanding existing disaster management problems in a community and in guiding the decision-making processes to reduce the hazard and to conduct remedial actions at the local level. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • David Novelo-Casanova & Gerardo Suárez, 2015. "Estimation of the Risk Management Index (RMI) using statistical analysis," 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. 77(3), pages 1501-1514, July.
  • Handle: RePEc:spr:nathaz:v:77:y:2015:i:3:p:1501-1514
    DOI: 10.1007/s11069-015-1663-4
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    References listed on IDEAS

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    1. Maxx Dilley & Robert S. Chen & Uwe Deichmann & Arthur L. Lerner-Lam & Margaret Arnold, 2005. "Natural Disaster Hotspots: A Global Risk Analysis," World Bank Publications - Books, The World Bank Group, number 7376, December.
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    1. L. P. Zhang & P. Zhou, 2019. "Reassessment of global climate risk: non-compensatory or compensatory?," 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. 95(1), pages 271-287, January.
    2. Matteo Spada & Peter Burgherr, 2020. "Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging," Energies, MDPI, vol. 13(2), pages 1-21, January.
    3. Sonia Morán-Rodríguez & David A. Novelo-Casanova, 2018. "A methodology to estimate seismic vulnerability of health facilities. Case study: Mexico City, Mexico," 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. 90(3), pages 1349-1375, February.

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