The decisions to stay or go depend on several factors such as the perception of risk, prior experience, and the severity of the storm itself. However, the compliance rates with evacuation orders are often significantly less than 100%, with many coastal residents preferring to ride out the storm rather than leave. During tropical cyclones, evacuations are primarily ordered for those who live on the coast or in adjacent low-lying areas due to the anticipated storm surge. This is largely due to the protective actions adopted by coastal populations: sheltering in place and evacuation. is relatively low, less than 5% of the hazard fatalities. While among the costliest hazards, the death toll from tropical cyclones in the U.S. While the projections on frequency and intensity of tropical cyclones remain inconclusive, there is increasing risk exposure as population and assets continue to shift to coastal areas. since 1960 finds that tropical cyclones represent roughly 26% of the total losses, but this percentage has been increasing since 2000 and as of 2015 represents 41% of the total loss from natural hazards. The overall profile for natural hazard losses in the U.S. Tropical cyclones represent one of the costliest and devastating threats for populations in both the developed and developing world, with long-lasting consequences that extend over several years. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors received funding from Iberdrola, a commercial company, for this study. The first author of this paper wants to express its gratitude to the Fulbright Program and the Spanish Fulbright Commission and Iberdrola, as sponsoring company, for the educational funding for the doctoral degree at the University of South Carolina. Researchers who want to replicate this study can use the Twitter API ( ) to collect the tweets.įunding: This study was partially funded by the University of South Carolina through the 2015 SCFloods Research Initiative. These are within the paper and its Supporting Information files. However, we provided aggregated tweet counts, twitter user counts for both spatiotemporal analysis and evacuation analysis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Individual tweets cannot be redistributed based on the Twitter Terms of Use. Received: MaAccepted: JPublished: July 28, 2017Ĭopyright: © 2017 Martín et al. PLoS ONE 12(7):Įditor: Satish Ukkusuri, Purdue University, UNITED STATES These findings advance the use of big data and citizen-as-sensor approaches for public safety issues, providing an effective and near real-time alternative for measuring compliance with evacuation orders.Ĭitation: Martín Y, Li Z, Cutter SL (2017) Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew. A specific sub-state analysis of South Carolina illustrated overall compliance with evacuation orders and detailed information on the timing of departure from the coast as well as the destination location. A comparison between two time periods-pre-evacuation (October 2 th-4 th) and post-evacuation (October 7 th-9 th)-indicates that 54% of Twitter users moved away from the coast to a safer location, with observed differences by state on the timing of the evacuation. As expected, peak Twitter response was reached during the pre-impact and preparedness phase, and decreased abruptly after the passage of the storm. The approach involves the retrieval of tweets from the Twitter Stream, the creation and filtering of different datasets, and the statistical and spatial processing and treatment to extract, plot and map the results. Using Twitter data, this paper examines the spatiotemporal variability in social media response and develops a novel approach to leverage geotagged tweets to assess the evacuation responses of residents. The storm and its projected landfall triggered a massive social media reaction. Hurricane Matthew was the deadliest Atlantic storm since Katrina in 2005 and prompted one of the largest recent hurricane evacuations along the Southeastern coast of the United States.
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