An Analysis of Factors Influencing Disaster Mobility Using Location Data from Smartphones: Case Study of Western Japan Flooding
Soohyun Joo*,, Takehiro Kashiyama**, Yoshihide Sekimoto**, and Toshikazu Seto**
*Department of Civil Engineering, The University of Tokyo
4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan
**Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Western Japan was hit by heavy rain from June 8 to July 28, 2018. Record-breaking rain caused nearly all rivers to flood in Hiroshima and other areas. Over 200 people died following this disaster. Authorities attempted to understand why evacuation was not conducted swiftly enough to stop these deaths. They mentioned that normalcy bias and cognitive dissonance are two primary causes of significant damage . Moreover, an effective alert system is necessary to ensure that evacuation behaviors and procedures are incited at the appropriate time. To understand the factors that influence people’s behavior, we estimated the probability of irregular behavior by unit changes in external condition. We chose 500 m mesh as a unit of analysis to consider individual singularity and classified 3 classes of mesh to identify abnormal behavior. We verified that as the number of residents in each mesh increases, the likelihood of a person in that region to exhibit normalcy bias increases as well. Owing to data, the accuracy of this method is somewhat low. However, several implications may still be drawn from our results, such as the demand for an adequate alert system. Using the results of people’s mobility and disaster risk information, approaches to dangerous situations such as the examined case may be improved in the future.
-  “I am OK with myself...Lag behind evacuation behavior ‘Normality bias’,” The Sankei News, 2018.7.20, https://www.sankei.com/west/news/180719/wst1807190107-n1.html (in Japanese) [accessed February 28, 2019]
-  Kyodo, “At least 176 die in west Japan from heavy rain, flooding,” Top News, 2018.7.20, https://alpha.japantimes.co.jp/article/top_news/201807/1429/ (in Japanese) [accessed February 19, 2019]
-  M. C. Gonzáles, C. A. Hidalgo, and A. L. Barabási, “Understanding individual human mobility patterns,” Nature, Vol.453, pp. 779-782, 2008.
-  A. Sevtsuk and C. Ratti, “Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks,” J. of Urban Technology, Vo.17, Issue 1, pp. 41-60, 2010.
-  D. Ashbrook and T. Starner, “Using GPS to learn significant locations and predict movement across multiple users,” Personal and Ubiquitous Computing, Vol.7, Issue 5, pp. 275-286, 2003.
-  F. Calabrease, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating origin-destination flows using mobile phone location data,” IEEE Pervasive Computing, Vol.10, Issue 4, 2011.
-  T. Yabe, K. Tsubouchi, A. Sudo, and Y. Sekimoto, “A framework for evacuation hotspot detection after large scale disasters using location data from smartphones: case study of Kumamoto earthquake,” Proc. of the 24th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, 2016.
-  X. Lu, L. Bengtsson, and P. Holme, “Predictability of population displacement after the 2010 Haiti earthquake,” Proc. National Academy of Sciences, Vol.109, No.29, pp. 11576-11581, 2012.
-  Q. Wang and J. E. Taylor, “Quantifying human mobility perturbation and resilience in Hurricane Sandy,” PLoS ONE, Vol.9, No.11, 2014.
-  X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S. Ueyama, and R. Shibasaki, “Modeling and probabilistic reasoning of population evacuation during large-scale disaster,” Proc. of the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 1231-1239, 2013.
-  T. Adachi, N. Kohashi, T. Saita, K. Kaji, and T. Abe, “Awareness survey on evacuation behavior in Hokusatsu heavy rain disaster,” Proc. J. of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), Vol.72, Issue 4, pp. I_1321-I_1326, 2016 (in Japanese).
-  M. Okumura, M. Tsukai, and T. Shimoaraiso, “Reliance on Disaster Warning and Responses,” Infrastructure Planning Review, Vol.18, pp. 311-316, 2001 (in Japanese).
-  T. Kinoshita, R. Akiyama, Y. Shimizu, and N. Osanai, “Construction of simplified cybernetics model for evacuation behaviors in sediment disaster – The case study of three cities in Nagano prefecture –,” J. of the Japan Society of Erosion Control Engineering, Vol.62, Issue 4, pp. 11-21, 2009 (in Japanese).
-  T. Ohmoto, T. Fujimi, and R. Koba, “Correlation analysis between residents’ evacuation activity and flood damage in river disaster,” Proc. of Hydraulic Engineering, Vol.52, 2008 (in Japanese).
-  J. P. Bagrow, D. Wang, and A. L. Barabási, “Collective response of human population to large-scale emergencies,” PLoS ONE, Vol.6, No.3, 2011.
-  R. Wako, Y. Sekimoto, H. Kanasugi, and R. Shibasaki, “Analysis of people’s route and destination choice in evacuation using GPS log data,” J. of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol.70, Issue 5, pp. I_681-I_688, 2014 (in Japanese).
-  https://www.iza.ne.jp/kiji/events/photos/180709/evt18070908440007-p2.html (in Japanese) [accessed February 25, 2019]
-  United Nations Office for Disaster Risk Reduction (UNDRR), “Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction,” 41pp., 2017.
-  T. Yabe, K. Tsubouchi, A. Sudo, and Y. Sekimoto, “Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones,” Proc. of the 24th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information System, 2016.
-  E. Cho, S. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks,” Proc. of the 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 1082-1090, 2011.
-  N. T. Li, Y. T. Lau, A.H. Nor, and H. H. Mohd, “GPS systems literature: inaccuracy factors and effective solutions,” Int. J. of Computer Networks & Communications (IJCNC), Vol.8, No.2, pp. 123-131, 2016.
-  Statistic Laerd, “Standard Score,” https://statistics.laerd.com/statistical-guides/standard-score.php [accessed February 28, 2019]
-  D. V. Cicchetti, “Standard scores (Z and T scores),” F. R. Volkmar (Ed.), “Encyclopedia of Autism Spectrum Disorders,” Springer, 2013.
-  R. Pedace, “Econometrics for dummies,” John Wiley & Sons, ISBN: 978-1-118-53384-0, 2013.
-  W. H. Greene, “Econometric Analysis,” 7th edition, Pearson Education, pp. 803-806, ISBN 978-0-273-75356-8, 2012.
-  Wikipedia, “Multinomial logistic regression,” https://en.wikipedia.org/wiki/Multinomial_logistic_regression [accessed February 22, 2019]
-  J. Shi, A. Ren, and C. Chen, “Agent-based evacuation model of large public buildings under fire conditions,” Automatic in Construction, Vol.18, Issue 3, pp. 338-347, 2009.
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