Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors.

Xiangxue Zhang, Juan Nie, Changxiu Cheng, Chengdong Xu, Xiaojun Xu, Bin Yan
Author Information
  1. Xiangxue Zhang: Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing, 100875, China.
  2. Juan Nie: National Disaster Reduction Center of China, Ministry of Emergency Management, Beijing, 100124, China.
  3. Changxiu Cheng: Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing, 100875, China. chengcx@bnu.edu.cn.
  4. Chengdong Xu: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. xucd@lreis.ac.cn.
  5. Xiaojun Xu: College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
  6. Bin Yan: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.

Abstract

BACKGROUND: Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons.
METHODS: In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors.
RESULTS: Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces.
CONCLUSIONS: These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons.

Keywords

References

  1. Nature. 2013 Dec 5;504(7478):44-52 [PMID: 24305147]
  2. Sci Total Environ. 2018 Dec 1;643:171-182 [PMID: 29936160]
  3. Lancet. 2012 Mar 3;379(9818):843-52 [PMID: 22386037]
  4. BMC Infect Dis. 2021 Mar 5;21(1):242 [PMID: 33673819]
  5. Sci Rep. 2013;3:1522 [PMID: 23519311]
  6. Sci Adv. 2015 May 29;1(4):e1500014 [PMID: 26601179]
  7. Int J Environ Res Public Health. 2013 Dec 20;11(1):173-86 [PMID: 24362546]
  8. Nat Commun. 2015 Mar 12;6:6591 [PMID: 25761457]
  9. Sensors (Basel). 2008 Mar 28;8(4):2223-2239 [PMID: 27879819]
  10. Epidemiology. 2009 Nov;20(6):892-5 [PMID: 19797965]
  11. Sci Total Environ. 2020 Apr 15;713:136623 [PMID: 31954246]
  12. PLoS One. 2011;6(6):e21427 [PMID: 21738660]

Grants

  1. 2019YFA0606901/National Key Research and Development Plan of China

MeSH Term

China
Cyclonic Storms
Humans
Socioeconomic Factors
Wind

Word Cloud

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