Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.

中文翻译：

---

社区对野火的抵御能力：利用人员流动数据的网络分析方法

灾害一直是整个社会长期关注的问题。随着人们对复原力社区的日益关注，这种关注已成为复原力研究的前沿。然而，关于弹性的定义多种多样，并且尚未出现准确的定义。此外，迄今为止的许多工作往往只关注对事件的立即响应，因此对一个地区在很长一段时间内的恢复能力的调查在很大程度上仍未得到探索。为了克服这些问题，我们提出了一个利用网络分析和灾害科学概念（例如复原力三角）的新颖框架来量化野火的长期影响。以门多西诺综合体和坎普野火（分别是加州迄今为止最大和最致命的野火）作为案例研究，我们根据 2018 年至 2019 年的人口流动数据，了解社区的稳健性和脆弱性。结果表明，人口和社会经济特征本身只能部分反映社区的复原力，但是，通过利用人员流动数据和网络分析技术，我们可以增强对空间和时间复原力的理解，为研究灾害及其对社会的长期影响提供新的视角。