This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in people lifestyles, built environment conditions, and businesses status. The study utilized digital trace data of unique visits to POIs in the context of the 2017 Hurricane Harvey in Houston (Texas, USA) to examine spatial patterns of impact and total recovery effort and utilized these measures to quantify community resilience. The results showed that certain POI categories such as building materials and supplies dealers and grocery stores were the most resilient elements of the community compared to the other POI categories. On the other hand, categories such as medical facilities and entertainment were found to have lower resilience values. This result suggests that these categories were either not essential for community recovery or that the community was not able to access these services at normal levels immediately after the hurricane. In addition, the spatial analyses revealed that many areas in the community with lower levels of resilience experienced extensive flooding. However, some areas with low resilience were not flooded extensively, suggesting that spatial reach of the impacts goes beyond flooded areas. The results demonstrate the importance of the approach proposed in our study. While this study focused on Houston and only analysed one natural hazard, the approach can be applied to other communities and disaster contexts. Applying this approach, emergency managers and public officials can efficiently monitor the patterns of disaster impacts and recovery across different spatial areas and POI categories and also identify POI categories and areas of their community that need to be prioritized for resource allocation.

中文翻译：

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基于数字跟踪数据访问兴趣点的波动量化社区弹性

本研究旨在根据对各种兴趣点 (POI) 位置的访问量的波动来量化社区恢复力。对兴趣点的访问是人类活动的一个重要指标，它可以捕捉人们生活方式、建筑环境条件和企业状况的综合影响。该研究利用了在 2017 年休斯敦（美国德克萨斯州）飓风哈维的背景下对 POI 的独特访问的数字跟踪数据来检查影响和总体恢复工作的空间模式，并利用这些措施来量化社区恢复力。结果表明，与其他 POI 类别相比，某些 POI 类别（例如建筑材料和用品经销商以及杂货店）是社区中最具弹性的元素。另一方面，发现医疗设施和娱乐等类别的弹性值较低。这一结果表明，这些类别对于社区恢复不是必不可少的，或者社区在飓风过后无法立即以正常水平获得这些服务。此外，空间分析显示，社区中许多韧性较低的地区经历了大面积洪水。然而，一些恢复力低的地区没有被广泛淹没，这表明影响的空间范围超出了淹没地区。结果证明了我们研究中提出的方法的重要性。虽然这项研究侧重于休斯顿，并且只分析了一种自然灾害，但该方法可以应用于其他社区和灾害环境。应用这种方法，