Construction and verification of a rainstorm death risk index based on grid data fusion: a case study of the Beijing rainstorm on July 21, 2012

Xianhua Wu (), Jiqiang Zhao (), Yun Kuai (), Ji Guo () and Ge Gao ()
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Xianhua Wu: Shanghai Maritime University
Jiqiang Zhao: Shanghai Maritime University
Yun Kuai: China united network communication Co., LTD.
Ji Guo: Shanghai Maritime University
Ge Gao: Nanjing University of Information Science & Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 3, No 12, 2293-2318

Abstract: Abstract Rainstorm disaster brings serious threat to people's life and property safety. Constructing reasonable rainstorm disaster risk index and drawing rainstorm disaster risk map can help decision-makers to deal with rainstorm disaster effectively and reduce disaster loss. It has important practical significance. This paper, for the first time, proposes a comprehensive risk index for death caused by rainstorm disasters. According to this index, the regional hazard map of Beijing is drawn, so as to directly reflect the damage degree of rainstorm disaster in Beijing. In the process of index construction, the weight is determined by regression coefficient innovatively, and the disadvantage of subjective setting weight is avoided, and the spatial distribution of risk can be more accurately reflected by constructing disaster risk index by using fused grid data. Research shows that the rainstorm death risk index proposed in this paper can well reflect the risk of death caused by rainstorms in various areas of Beijing. Combined with the ArcGIS software, a risk map of death due to rainstorm disasters is drawn. It is found that Fangshan District of Beijing is a major disaster area with the highest risk of death. Finally, the managerial implication included that government administrators should evaluate the risk of rainstorm disasters in a certain area according to the established rainstorm death risk indexes and draw a risk map accordingly.

Keywords: Rainstorm disaster; ArcGIS; Multiple linear regression; Kernel density estimation; Rainstorm death risk index (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-021-04507-0

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