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Spatial distribution patterns of global natural disasters based on biclustering

Author

Listed:
  • Shi Shen

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Changxiu Cheng

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Changqing Song

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Jing Yang

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Shanli Yang

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Kai Su

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Lihua Yuan

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

  • Xiaoqiang Chen

    (Beijing Normal University
    Beijing Normal University
    Beijing Normal University)

Abstract

Understanding the spatial distribution patterns (SDPs) of natural disasters plays an essential role in reducing and minimizing natural disaster risks. An integrated discussion on the SDPs of multiple global disasters is still lacking. In addition, due to their high quantity and complexity, natural disasters constitute high-dimensional data that represent a challenge for an analysis of SDPs. This paper analyzed the SDPs of global disasters from 1980 to 2016 through biclustering. The results indicate that the SDPs of fatality rates are more uneven than those of occurrence rates. Based on the occurrence rates, the selected countries were clustered into four classes. (1) The major disasters along the northern Pacific and in the Caribbean Sea and Madagascar are storms, followed by floods. (2) Most of Africa is mainly affected by floods, epidemics, and droughts. (3) The primary disaster types in the Alpine-Himalayan belt and the western Andes are floods and earthquakes. (4) Europe, America, Oceania, and South and Southeast Asia are predominantly influenced by floods. In addition, according to the fatality rates, the selected countries were clustered into eight classes. (1) Extreme high temperatures mostly result in high fatality rates (HFRs) in developed countries. (2) Epidemics lead to HFRs in parts of Africa. (3) Droughts produce HFRs in East Africa. (4) Earthquakes result in HFRs along the eastern Pacific coastline and the Alpine-Himalayan belt. (5) Tsunamis mainly cause HFRs in Thailand, Indonesia, and Japan. (6) Storms result in scattered but distinct HFRs along the coastal regions of the Pacific and Indian Oceans. (7) Floods cause concentrated HFRs in South Asia and northeastern South America. (8) Finally, volcanoes cause HFRs in Colombia, while extreme low temperatures cause HFRs in Ukraine and Poland.

Suggested Citation

  • Shi Shen & Changxiu Cheng & Changqing Song & Jing Yang & Shanli Yang & Kai Su & Lihua Yuan & Xiaoqiang Chen, 2018. "Spatial distribution patterns of global natural disasters based on biclustering," 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. 92(3), pages 1809-1820, July.
  • Handle: RePEc:spr:nathaz:v:92:y:2018:i:3:d:10.1007_s11069-018-3279-y
    DOI: 10.1007/s11069-018-3279-y
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    References listed on IDEAS

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    1. Yu Peng & Jingyi Song & Tiantian Cui & Xiang Cheng, 2017. "Temporal–spatial variability of atmospheric and hydrological natural disasters during recent 500 years in Inner Mongolia, China," 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. 89(1), pages 441-456, October.
    2. Ferraty, F., 2010. "High-dimensional data: a fascinating statistical challenge," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 305-306, February.
    3. Chan-juan Li & Yuan-qing Chai & Lin-sheng Yang & Hai-rong Li, 2016. "Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa," 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. 82(1), pages 721-731, May.
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    Cited by:

    1. Wei Wang & Chenhong Xia & Chaofeng Liu & Ziyi Wang, 2020. "Study of double combination evaluation of urban comprehensive disaster risk," 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. 104(2), pages 1181-1209, November.
    2. Shi Shen & Ke Shi & Junwang Huang & Changxiu Cheng & Min Zhao, 2023. "Global online social response to a natural disaster and its influencing factors: a case study of Typhoon Haiyan," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.

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