Hurricane Matthew Evacuation Research
Investigators: Zhenlong Li, Susan Cutter, Jamie Mitchem (Univ. of North Georgia), Christopher Emrich (Univ. Central Florida)
Two separate projects examined the evacuation behavior of residents in the Southeast in response to Hurricane Matthew in 2015. The first leveraged geotagged Twitter data to guage the evacuation responses of residents from Florida to South Carolina. Peak Twitter activitiy was reached during the pre-mpact and preparedness phases, and decreased abruptly after the passage of the storm. Approximately 54% of the Twitter users moved away from the coast to a safer location between October 2-4 2016. The findings help advance the use of big data and citizen-as-sensor approaches for public safety issues and provide a near real-time alternative for measuring compliance with evacuation orders. The second project deployed an online questionnaire durvey using SurveyMonkey in November-December 2017 to examine evacuation responses in three states--Florida, Georgia, and South Carolina. Around 75% of the respondents said they were ordered to evacuate with the highest percentage in Georgia (90%), and slightly more than two-thirds of the respondents did evacuate (68.3%). Florida had the lowest evacuation response (62.9%) among the three states. The factors that were important prompts for the evacuation were the hurricane strength and path and in South Carolina, the manadatory evacuation order.
Martin, Y., Z. Li, and S.L. Cutter, 2017. Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew, PLoS ONE 12(7):e0181701. https://doi.org/10.1371/journal.pone.0181701.
Martín, Yago, Susan L. Cutter, and Zhenlong Li, 2020. Bridging Twitter and survey data for evacuation assessment of Hurricane Matthew and Hurricane Irma, Natural Hazards Review 21 (2): 04020003. Online 23 January 2020.
Pham, E., Z. Li, C.T. Emrich, J. Mitchem, and S.L. Cutter, 2020. Evacuation departure timing during Hurricane Matthew, Weather, Climate and Society 12(2): 235-248. Online 31 January 2020. https://doi.org/10.1175/WCAS-D-19-0030.1