How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science
How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science
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DOI：
10.3390/urbansci3020051
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发表时间：
2019-06-01
期刊：
影响因子：
2
通讯作者：
Cao, Guofeng
中科院分区：
文献类型：
--
作者：
Han, Su Yeon;Tsou, Ming-Hsiang;Cao, Guofeng
了解人类在面对自然灾害时的活动对于灾难疏散计划、管理和救援至关重要。尽管明确需要这样的工作，但由于缺乏测量实际灾害期间时空流动模式的可用数据，这些研究在文献中很少。本研究通过利用用户在飓风马修发生前数小时内发布的数百万条推文中的位置信息，探索了疏散旅行的时空模式。我们的分析产生了一些实际的见解，包括以下几点：(1)我们确定了Twitter用户在疏散命令下达后离开疏散区，然后在飓风过去后返回家园的轨迹。(2)疏散区居民在疏散令期间在疏散区外发出了异常大量的推文。在强制撤离命令下达后，南卡罗来纳和乔治亚州的撤离人员都花了几天时间才离开他们的居民区，但乔治亚州的居民通常需要更多的时间才能回家。(4)撤离人员更有可能选择更远的大城市作为安全目的地，而不是附近的小城市。(5)疏散过程中人员的运动服从对数正态分布。
Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users' location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution.