论文标题

根据数字跟踪数据的访问访问访问点的波动来量化社区的韧性

Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data

论文作者

Podesta, Cristian, Coleman, Natalie, Esmalian, Amir, Yuan, Faxi, Mostafavi, Ali

论文摘要

这项研究旨在根据访问各种利益点(POI)位置的波动来量化社区的韧性。参观POI是人类活动的必要指标,并捕获了人们生活方式,建立环境条件和企业状况的扰动的综合影响。该研究利用在2017年在美国德克萨斯州休斯敦飓风哈维(Harvey)的背景下,利用了对POI的独特访问的数字痕量数据来检查影响的空间模式和全面的恢复工作,并利用这些措施来量化社区的应变能力。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和杂货店)是社区中最有弹性的元素。另一方面,发现医疗设施和娱乐等类别具有较低的弹性价值。该结果表明,这些类别对于社区恢复不是必不可少的,或者社区无法在飓风过后立即在正常水平上访问这些服务。此外,空间分析表明,弹性水平较低的社区中许多地区经历了广泛的洪水。但是,某些具有弹性低的地区并未被广泛淹没,这表明影响的空间范围超出了洪水泛滥的地区。结果证明了我们研究中提出的方法的重要性。尽管这项研究的重点是休斯顿,并且仅分析了一种自然危害,但该方法可以应用于其他社区和灾难环境。采用这种方法,应急管理人员和公职人员可以有效地监视不同空间领域和POI类别的灾难影响和恢复模式,还可以确定需要优先考虑以进行资源分配的POI类别及其社区领域。

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.

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