Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data.
Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data.
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DOI：
10.1038/s41598-022-20384-4
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发表时间：
2022-09-26
影响因子：
4.6
通讯作者：
Mostafavi, Ali
中科院分区：
文献类型：
--
作者：
Lee, Cheng-Chun;Chou, Charles;Mostafavi, Ali
本研究的目的是：（1）确定疏散返回和家庭转换稳定性作为灾害期间和灾后短期恢复的两个关键里程碑;（2）了解这些关键恢复里程碑持续时间的亚人群差异。使用隐私保护精细分辨率基于位置的数据，我们在2017年飓风哈维的背景下研究了德克萨斯州哈里斯县的疏散和房屋迁出率。对于两个关键的恢复里程碑中的每一个，结果都揭示了返回持续时间较短和较长的地区，并能够评估疏散返回和回家稳定模式的差异。事实上，洪灾地区关键恢复里程碑指标持续时间较短并不一定是积极的迹象。较短的疏散返回可能是由于疏散的障碍和较短的家庭搬出率返回低收入居民与生活在出租房屋。此外，在所有亚群组中观察到疏散返回和家庭转换稳定性的偏斜和非均匀恢复模式。所有返回模式均显示两阶段返回进度模式。调查结果可以帮助灾难管理人员和政府官员以更积极主动、数据驱动和公平的方式执行恢复监控和资源分配。
The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.
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影响因子：
--
作者：
Lane K;Charles-Guzman K;Wheeler K;Abid Z;Graber N;Matte T
通讯作者：
Matte T
影响因子：
29.9
作者：
Aleta A;Martín-Corral D;Pastore Y Piontti A;Ajelli M;Litvinova M;Chinazzi M;Dean NE;Halloran ME;Longini IM Jr;Merler S;Pentland A;Vespignani A;Moro E;Moreno Y
通讯作者：
Moreno Y
影响因子：
11.7
作者：
Fan, Chao;Jiang, Xiangqi;Mostafavi, Ali
通讯作者：
Mostafavi, Ali
DOI：
10.1073/pnas.1203882109
发表时间：
2012-07-17
影响因子：
11.1
作者：
Lu, Xin;Bengtsson, Linus;Holme, Petter
通讯作者：
Holme, Petter
影响因子：
4.7
作者：
Browne, MJ;Hoyt, RE
通讯作者：
Hoyt, RE