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.
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DDN—disaster damage network.
Alexander D (2018) A magnitude scale for cascading disasters. Int J Disast Risk Res 30(B):180–185. https://doi.org/10.1016/j.ijdrr.2018.03.006
Arosio M, Martina MLV, Figueiredo R (2020) The whole is greater than the sum of its parts: a holistic graph-based assessment approach for natural hazard risk of complex systems. Nat Hazards Earth SystSci 20:521–547. https://doi.org/10.5194/nhess-20-521-2020
Berariu R, Fikar C, Gronalt M, Hirsch P (2015) Understanding the impact of cascade effects of natural disasters on disaster relief operations. Int J DisastRisk Res 12:350–356. https://doi.org/10.1016/j.ijdrr.2015.03.005
Boin A, McConnell A (2007) Preparing for critical infrastructure breakdowns: the limits of crisis management and the need for resilience. J Conting Crisis Manag 15(1):50–59. https://doi.org/10.1111/j.1468-5973.2007.00504.x
Cavallo A, Ireland V (2014) Preparing for complex interdependent risks: a system of systems approach to building disaster resilience. Int J DisastRisk Res 9:181–193. https://doi.org/10.1016/j.ijdrr.2014.05.001
Clark-Ginsberg A (2017) Participatory risk network analysis: a tool for disaster reduction practitioners. Int J DisastRisk Res 21:430–437. https://doi.org/10.1016/j.ijdrr.2017.01.006
Clark-Ginsberg A, Abolhassani L, AzamRahmati E (2018) Comparing networked and linear risk assessments: from theory to evidence. Int J Disast Risk Res 30(B):216–224. https://doi.org/10.1016/j.ijdrr.2018.04.031
Helbing D (2013) Globally networked risks and how to respond. Nature 497:51–59
Helbing D, Ammoser H, Kühnert C (2006) Disasters as extreme events and the importance of network interactions for disaster response management. In: Extreme events in nature and society. Springer, Berlin, pp 319–348. https://doi.org/10.1007/3-540-28611-x_15
Institute for the Protection and Security of the Citizen (2006) The vulnerability of interdependent critical infrastructures systems: epistemological and conceptual state-of-the-art. https://doi.org/10.1038/nature12047
Japanese Meteorological Agency (2018) Japan floods (heavy rains, etc., caused by fronts and Typhoon Prapiroon). https://www.data.jma.go.jp/obd/stats/data/bosai/report/2018/20180713/jyun_sokuji20180628-0708.pdf(in Japanese)
Japanese Ministry of Land, Infrastructure, and Transport (2018) Overview and damage characteristics of the July 2018 Japan floods. https://www.mlit.go.jp/river/shinngikai_blog/hazard_risk/dai01kai/dai01kai_siryou2-1.pdf(in Japanese)
Kachali H, Storsjö I, Haavisto I, Kovács G (2018) Inter-sectoral preparedness and mitigation for networked risks and cascading effects. Int J Disast Risk Res 30(B):281–291. https://doi.org/10.1016/j.ijdrr.2018.01.029
Komendantova N, Mrzyglocki R, Mignan A, Khazai B, Wenzel F, Patt A, Fleming K (2014) Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: feedback from civil protection stakeholders. Int J DisastRisk Res 8:50–67. https://doi.org/10.1016/j.ijdrr.2013.12.006
Kumasaki M, King M, Arai M, Yang L (2016) Anatomy of cascading natural disasters in Japan: main modes and linkages. Nat Hazards 80:1425–1441. https://doi.org/10.1007/s11069-015-2028-8
Mainichi Newspapers website. National distribution area and number of issues sold. https://macs.mainichi.co.jp/ad/area.html(in Japanese)
Maisaku web site, Mainichi Newspaper database “Maisaku.” https://mainichi.jp/contents/edu/maisaku/(in Japanese)
McGee S, Frittman J, Ahn S, Murray S (2015) Risk relationships and cascading effects in critical infrastructures: implications for the hyogo framework. Input paper prepared for the global assessment report on disaster risk reduction
Noguchi H, Fuse M (2020) Rethinking critical node problem for railway networks from the perspective of turn-back operation. Physica A 558:124950. https://doi.org/10.1016/j.physa.2020.124950
Pescaroli G, Alexander D (2016) Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. Nat Hazards 82(1):175–192. https://doi.org/10.1007/s11069-016-2186-3
Schauwecker S, Gascón E, Park S, Ruiz-Villanueva V, Schwarb M, Sempere-Torres D, Stoffel M, Vitolo C, Rohrer M (2019) Anticipating cascading effects of extreme precipitation with pathway schemes—three case studies from Europe. Environ Int 127:291–304. https://doi.org/10.1016/j.envint.2019.02.072
Tang P, Xia Q, Wang Y (2019) Addressing cascading effects of earthquakes in urban areas from network perspective to improve disaster mitigation. Int J Disast Risk Res 35:101065. https://doi.org/10.1016/j.ijdrr.2019.101065
United Nations Office for Disaster Risk Reduction (2019) Global Assessment Report on Disaster Risk Reduction, 2019. https://www.unisdr.org/we/inform/publications/65399. https://doi.org/10.18356/f4ae4888-en
van Eeten NA, Luiijf E, Klaver C (2011) The state and the threat of cascading failure across critical infrastructures: the implications of empirical evidence from media incident reports. Public Adm 89:2. https://doi.org/10.1111/j.1467-9299.2011.01926.x
Wang J, Gu X, Huang T (2013) Using Bayesian networks in analyzing powerful earthquake disaster chains. Nat Hazards 68(2):509–527. https://doi.org/10.1007/s11069-013-0631-0
Xu X, Wang C, Cai C, Xue, (2015) Evolution and coping research for flood disaster social stability risk based on the complex network. Nat Hazards 77:1491–1500. https://doi.org/10.1007/s11069-015-1662-5
Yang Y, Xie G, Xie J (2017) Mining important nodes in directed weighted complex networks. Discrete Dyn Nat Soc 9741824
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Noguchi, H., Nishizawa, T. & Fuse, M. A method to characterize the social cascading damage processes of disasters using media information. Nat Hazards 107, 231–247 (2021). https://doi.org/10.1007/s11069-021-04581-4
- Network theory
- Social cascading damage process
- Disaster event
- Disaster damage network
- Degree centrality
- 2018 Heavy rain disaster in western Japan