论文标题
位置智能揭示了飓风准备的程度,时机和空间变化
Location Intelligence Reveals the Extent, Timing, and Spatial Variation of Hurricane Preparedness
论文作者
论文摘要
改善飓风的准备对于减少飓风影响至关重要。量化和监测飓风准备的传统方法固有的是重要的滞后。这项研究建立了一个方法学框架,以使用高分辨率位置智能数据来量化飓风备件的程度,时机和空间变化。 2017年哈维飓风之前,匿名的手机数据访问了每次CBG的POIS,用于检查飓风的准备。在准备期间计算了四类POI,杂货店,加油站,药房,药房和家居装修商店与飓风的准备密切关系,并且在准备期间计算了每个CBG到这四类POI的每日访问数量。与基线期相比,根据每日访问百分比的变化计算了两个指标,即准备的程度和积极性。结果表明,对药房的高峰访问经常发生在早期,而访问加油站的峰值发生在更接近登陆的地方。杂货店和家庭装修商店的访问的时空模式非常相似。但是,相关分析表明,准备性和积极性的程度彼此独立。结合同步撤离数据,将CBG分为四个集群,以准备程度和疏散率。避免量低和疏散率低的集群被确定为对住房家庭脆弱性的热点,在响应过程中需要紧急关注。该研究促进了对人类保护行动的数据驱动的理解,并为应急响应经理提供了新颖的见解,以主动监控灾难的准备,促进识别准备不足的领域并及时更好地分配资源。
Improving hurricane preparedness is essential to reduce hurricane impacts. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the CBG level using high-resolution location intelligence data. Anonymized cell phone data on visits to POIs for each CBG before 2017 Hurricane Harvey were used to examine hurricane preparedness. Four categories of POI, grocery stores, gas stations, pharmacies and home improvement stores, were identified as having close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage, whereas the peak of visits to gas stations happened closer to landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. The study advances data-driven understanding of human protective actions and provide emergency response managers with novel insights to proactively monitor disaster preparedness, facilitating identifying under-prepared areas and better allocating resources timely.