Martín Yago, Li Zhenlong, Cutter Susan L
Department of Geography and Hazards and Vulnerability Research Institute, University of South Carolina, Columbia, South Carolina, United States of America.
PLoS One. 2017 Jul 28;12(7):e0181701. doi: 10.1371/journal.pone.0181701. eCollection 2017.
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. The storm and its projected landfall triggered a massive social media reaction. 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 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. As expected, peak Twitter response was reached during the pre-impact and preparedness phase, and decreased abruptly after the passage of the storm. A comparison between two time periods-pre-evacuation (October 2th-4th) and post-evacuation (October 7th-9th)-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. 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. 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.
飓风“马修”是自2005年卡特里娜飓风以来最致命的大西洋风暴，引发了美国东南沿海地区近期规模最大的一次飓风疏散行动。这场风暴及其预计登陆引发了社交媒体的大量反应。本文利用推特数据，研究了社交媒体反应的时空变化，并开发了一种新方法，利用带有地理标记的推文来评估居民的疏散反应。该方法包括从推特流中检索推文、创建和筛选不同的数据集，以及进行统计和空间处理与分析，以提取、绘制和映射结果。正如预期的那样，推特反应高峰出现在影响前和准备阶段，风暴过后迅速下降。对两个时间段——疏散前（10月2日至4日）和疏散后（10月7日至9日）——的比较表明，54%的推特用户从沿海地区转移到了更安全的地方，不同州在疏散时间上存在差异。对南卡罗来纳州的一个特定子州分析显示了总体上对疏散命令的遵守情况，以及关于离开沿海地区时间和目的地的详细信息。这些发现推动了大数据和公民作为传感器方法在公共安全问题上的应用，为衡量对疏散命令的遵守情况提供了一种有效且近乎实时的替代方法。
