Early Estimation of Heavy Rain Damage at the Municipal Level Based on Time-Series Analysis of SNS Information
Qinglin Cui, Kikuko Shoyama, Makoto Hanashima, and Yuichiro Usuda
National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan
To carry out natural disaster response, restoration, and reconstruction, it is important to efficiently and quickly assess the damage caused by the natural disaster. The existing evidence demonstrates that when a natural disaster occurs, social networking services (SNS) information is amplified significantly, compared to normal times. Specifically, the damage caused by a natural disaster tends to cover a wide area and have a large scale. Additionally, it may vary considerably depending on the municipality. Thus, this study investigates whether the utilization of this amplified SNS information can offer an effective approach for real-time evaluation and monitoring of the damage caused by a natural disaster in municipal units. To this end, focusing on time-series changes in SNS information, we propose a general-purpose analysis method of SNS information for evaluating the damage caused by a natural disaster in real time in municipal units. Using real-world data twitter data, we investigate the case of Kumamoto Prefecture, which experienced heavy rain in July 2020 and July 2021, to verify the proposed analysis method.
-  Ministry of Internal Affairs and Communications (MIC), “The Present and Future of the Smartphone Economy,” MIC, “White Paper Information and Communications in Japan,” pp. 2-13, 2017, https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h29/pdf/n1100000.pdf (in Japanese) [accessed March 31, 2022]
-  Ministry of Internal Affairs and Communications (MIC), “White Paper Information and Communications in Japan,” 2011, http://www.soumu.go.jp/johotsusintokei/whitepaper/h23.html (in Japanese) [accessed March 31, 2022]
-  Twitter, https://twitter.com/ [accessed March 31, 2022]
-  S. Kitamura, Y. Sasaki, and D. Kawai, “The Psychology of Twitter: Information Environment and User Behavior,” Seishin Shobo, 266pp., 2016 (in Japanese).
-  S. Taniguchi, “About the Usefulness of Twitter at the Time of the Disaster – A Heavy Rain Disaster by Typhoon 12 of September, 2011 for an Example,” J. of Disaster Information Studies, No.10, pp. 56-67, 2012 (in Japanese).
-  S. Sato and F. Imamura, “An Analysis of Tweet Data Tagged with ‘#Rescue’ in the 2017 North Kyushu Heavy Rain Disaster,” J. of Japan Society for Natural Disaster Science, Vol.37, No.1, pp. 93-102, 2018 (in Japanese with English abstract).
-  Q. Cui et al., “Development of Analysis Technology for SNS Information and Application to the Western Japan Heavy Rain in 2018,” Proc. of Japan Society for Natural Disaster Science, No.38, pp. 35-36, 2019 (in Japanese).
-  Q. Cui et al., “An Attempt to Grasp the Disaster Situation of ‘The 2018 Hokkaido Eastern Iburi Earthquake’ Using SNS Information,” J. Disaster Res., Vol.14, No.9, pp. 1170-1184, 2019.
-  Q. Cui et al., “Grasping Power Outage Situation of Tokyo Metropolitan Area Using SNS Information in the 2019 Boso Peninsula Typhoon,” J. of Social Safety Science, Vol.38, pp. 47-57, 2021 (in Japanese with English abstract).
-  K. Shoyama et al., “Emergency Flood Detection Using Multiple Information Sources: Integrated Analysis of Natural Hazard Monitoring and Social Media Data,” Science of the Total Environment, Vol.767, Article No.144371, 2021.
-  Y. Usuda, “Decision Support System and New Technologies,” M. Sakurai and R. Shaw (Eds.), “Emerging Technologies for Disaster Resilience: Practical Cases and Theories,” pp. 241-260, Springer, 2021.
-  S. Sato et al., “Grasp of Disaster Situation and Support Need Inside Affected Area with Social Sensing – An Analysis of Twitter Data Before and After the 2011 Great East Japan Earthquake Disaster Occurring –,” J. Disaster Res., Vol.11, No.2, pp. 198-206, 2016.
-  Q. Zhao et al., “Event Detection from Evolution of Click-Through Data,” Proc. of the 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’06), pp. 484-493, 2006.
-  T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors,” Proc. of the 19th Int. Conf. on World Wide Web (WWW’10), pp. 851-860, 2010.
-  A. Miura et al., “Expression of Negative Emotional Responses to the 2011 Great East Japan Earthquake: Analysis of Big Data from Social Media,” The Japanese J. of Psychology, Vol.86, No.2, pp. 102-111, 2015 (in Japanese with English abstract).
-  H. Yamada et al., “Effects and Issues of the Disaster Information Tweeting and Mapping System for Residents,” Proc. of the 2020 5th Int. Conf. on Computer and Communication Systems (ICCCS), pp. 442-446, 2020.
-  National Institute of Information and Communications Technology (NICT), “DISAANA web,” https://disaana.jp/rtime/search4pc.jsp (in Japanese) [accessed March 31, 2022]
-  National Institute of Information and Communications Technology (NICT), “D-SUMM web,” https://disaana.jp/d-summ/ (in Japanese) [accessed March 31, 2022]
-  Q. Cui, M. Hanashima, and Y. Usuda, “Time Series Analysis on the Damage Report of the Northern Kyushu Heavy Rainfall in July 2017,” J. Disaster Res., Vol.15, No.6, pp. 698-711, 2020.
-  Kumamoto Prefecture, “Damage Situation Related to Heavy Rain in July 2020 (Damage Report),” https://www.pref.kumamoto.jp/soshiki/4/74612.html (in Japanese) [accessed March 31, 2022]
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