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Resilience Analysis of Countries under Disasters Based on Multisource Data

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  • Nan Zhang
  • Hong Huang

Abstract

Disasters occur almost daily in the world. Because emergencies frequently have no precedent, are highly uncertain, and can be very destructive, improving a country's resilience is an efficient way to reduce risk. In this article, we collected more than 20,000 historical data points from disasters from 207 countries to enable us to calculate the severity of disasters and the danger they pose to countries. In addition, 6 primary indices (disaster, personal attribute, infrastructure, economics, education, and occupation) including 38 secondary influencing factors are considered in analyzing the resilience of countries. Using these data, we obtained the danger, expected number of deaths, and resilience of all 207 countries. We found that a country covering a large area is more likely to have a low resilience score. Through sensitivity analysis of all secondary indices, we found that population density, frequency of disasters, and GDP are the three most critical factors affecting resilience. Based on broad‐spectrum resilience analysis of the different continents, Oceania and South America have the highest resilience, while Asia has the lowest. Over the past 50 years, the resilience of many countries has been improved sharply, especially in developing countries. Based on our results, we analyze the comprehensive resilience and provide some optimal suggestions to efficiently improve resilience.

Suggested Citation

  • Nan Zhang & Hong Huang, 2018. "Resilience Analysis of Countries under Disasters Based on Multisource Data," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 31-42, January.
    • Handle: RePEc:wly:riskan:v:38:y:2018:i:1:p:31-42
    DOI: 10.1111/risa.12807
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    References listed on IDEAS

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    Cited by:

    1. Vladimir M. Cvetković & Vanja Šišović, 2024. "Understanding the Sustainable Development of Community (Social) Disaster Resilience in Serbia: Demographic and Socio-Economic Impacts," Sustainability, MDPI, vol. 16(7), pages 1-34, March.
    2. Yin, Jiateng & Ren, Xianliang & Liu, Ronghui & Tang, Tao & Su, Shuai, 2022. "Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Huali Pan & Yuxin Yang & Wei Zhang & Mingzhi Xu, 2024. "Research on Coupling Coordination of China’s Urban Resilience and Tourism Economy—Taking Yangtze River Delta City Cluster as an Example," Sustainability, MDPI, vol. 16(3), pages 1-27, February.
    4. Matt Boyd & Nick Wilson, 2020. "The Prioritization of Island Nations as Refuges from Extreme Pandemics," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 227-239, February.

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