Abstract
Recent decades have witnessed an increasing risk of natural disasters, causing a large amount of loss of assets all over the world. It is widely acknowledged that a more comprehensive understanding of the evacuation behaviors will significantly mitigate the loss of natural hazards, and can also improve the management process for corresponding agencies. Social media platform with user-generated content provides great potentials in better understanding the spatiotemporal pattern of the evacuation. However, existing work mainly focuses on the general spatio-temporal pattern, failing to analyze evacuation movements at different stages of disaster (e.g. preparedness, response, and recovery), and lacks comprehensive analysis with user-generated content (e.g. sentiment). Taking the 2017 Hurricane Harvey as a case study, we generate the Twitter-based trajectories of users spatio-temporally affected by Hurricane Harvey, and conduct analysis by considering differently affected regions at different disaster stages, and further visualize the spatiotemporal and sentiment pattern of the evacuation behavior.
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Guo, C.(., Huang, Q. (2021). Examining Spatiotemporal and Sentiment Patterns of Evacuation Behavior During 2017 Hurricane Harvey. In: Nara, A., Tsou, MH. (eds) Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-83010-6_8
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DOI: https://doi.org/10.1007/978-3-030-83010-6_8
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