A method to characterize the social cascading damage processes of disasters using media information

Hiroki Noguchi (), Takuma Nishizawa () and Masaaki Fuse ()
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Hiroki Noguchi: Graduate School of Advanced Science and Engineering
Takuma Nishizawa: Graduate School of Advanced Science and Engineering
Masaaki Fuse: Graduate School of Advanced Science and Engineering

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 1, No 11, 247 pages

Abstract: Abstract Constantly advancing media information is a key data source to characterize the social cascading damage processes following natural hazards. However, media information tends to include a large sample size but low information density. In consideration of these properties, the aim of this study is to develop a new method for media-based information characterizing social cascading damage processes. In developing the method, a network theory framework was constructed to systematically integrate media information and its characterization. The method has two analytical components: a disaster damage network systematically inputting media information and network analysis using the concept of degree centrality. The developed method was applied to the record-breaking 2018 heavy rain disaster in western Japan, employing newspaper articles as media information sources. The study identified the critical disaster events and their relationships. This case study demonstrates that our method will benefit policymakers by providing them with potential fundamental information to support disaster management.

Keywords: Network theory; Social cascading damage process; Disaster event; Disaster damage network; Degree centrality; 2018 Heavy rain disaster in western Japan (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04581-4

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