飓风伊恩期间基于大规模移动设备位置数据的飓风疏散分析
Hurricane Evacuation Analysis with Large-scale Mobile Device Location Data during Hurricane Ian
AI总结：
本研究利用太字节级移动设备GPS数据，提出一种全面的飓风疏散行为算法，分析伊恩飓风期间疏散者的出发时间与行为分类，揭示区域疏散率差异及三次疏散浪潮，为灾害规划提供参考。
摘要：
飓风伊恩是佛罗里达州历史上致死人数最多、经济损失最严重的飓风，约有250万人被命令疏散。随着气候变化背景下我们目睹越来越强烈的飓风，移动设备位置数据为研究飓风疏散行为提供了前所未有的机会。利用太字节级别的GPS数据集，我们提出了一种全面的飓风疏散行为算法，并以伊恩飓风为案例进行研究：我们推断疏散者的出发时间，并将其分为不同的行为群体，包括自行疏散者、自愿疏散者、强制疏散者、影子疏散者和区域内疏散者。结果表明，登陆区域（迈尔斯堡，李县）的跨区域疏散率较低，但总体疏散率较高，而预测登陆区域（坦帕，希尔斯伯勒县）则相反，这表明疏散命令延迟的影响。跨区域疏散率从海岸向内陆递增。时空分析识别出三次疏散浪潮：飓风形成期间、登陆前和登陆后。这些见解对于加强未来的灾害规划和管理具有重要价值。
英文摘要：
Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedented opportunity to study hurricane evacuation behaviors. With a terabyte-level GPS dataset, we introduce a holistic hurricane evacuation behavior algorithm with a case study of Ian: we infer evacuees' departure time and categorize them into different behavioral groups, including self, voluntary, mandatory, shadow and in-zone evacuees. Results show the landfall area (Fort Myers, Lee County) had lower out-of-zone but higher overall evacuation rate, while the predicted landfall area (Tampa, Hillsborough County) had the opposite, suggesting the effects of delayed evacuation order. Out-of-zone evacuation rates would increase from shore to inland. Spatiotemporal analysis identified three evacuation waves: during formation, before landfall, and after landfall. These insights are valuable for enhancing future disaster planning and management.
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