解析韧性三角：揭示面临气象灾害的人类系统中的韧性曲线原型与特性
Dissecting Resilience Triangle: Unravelling Resilience Curve Archetypes and Properties in Human Systems Facing Weather Hazards
AI总结：
本研究基于美国多起气象灾害后的2000余条人类移动韧性曲线，通过k-means聚类识别出三类韧性原型，明确了关键阈值与恢复速率要求，为韧性曲线提供了数据驱动的实证表征。
摘要：
韧性曲线一直是灾害事件期间概念化和表征社区韧性行为的主要方法；然而，韧性曲线的应用仍仅停留在概念和可视化工具层面，数据驱动的表征和实证基础十分有限。对韧性曲线的实证表征能为受不同程度影响的社区系统如何吸收扰动、从中断中恢复提供关键洞见。为填补这一空白，本研究通过分析基于高分辨率位置的移动数据构建的2000多条实证韧性曲线，研究了美国多起气象相关灾害事件后的人类移动韧性模式。随后，研究基于多种特征使用k-means聚类对这些实证韧性曲线进行分类，划分出不同原型。研究识别出人类移动韧性的三种主要原型：I型为轻度影响后快速恢复；II型表现为中度影响后的双峰恢复；III型则是严重影响后恢复较慢。研究结果还揭示了关键阈值，例如影响程度为20%时的双峰恢复断点——此时恢复速率会下降，以及影响程度为60%时的关键功能阈值——超过该阈值后恢复速率会相当缓慢。结果表明，当影响程度超过20%时，要遵循双峰韧性原型，需要达到每天2.5%的关键功能恢复速率。这些发现为不同韧性曲线原型及其基本特性提供了新颖且重要的洞见。不同于仅将韧性曲线用作概念和可视化工具，对韧性曲线原型及其特性的数据驱动界定提升了我们对韧性模式的理解……
英文摘要：
Resilience curves have been the primary approach for conceptualizing and representing the resilience behavior of communities during hazard events; however, the use of resilience curves has remained as a mere conceptual and visual tool with limited data-driven characterization and empirical grounding. Empirical characterizations of resilience curves provide essential insights regarding the manner in which differently impacted systems of communities absorb perturbations and recover from disruptions. To address this gap, this study examines human mobility resilience patterns following multiple weather-related hazard events in the United States by analyzing more than 2000 empirical resilience curves constructed from high-resolution location-based mobility data. These empirical resilience curves are then classified using k-means clustering based on various features into archetypes. Three main archetypes of human mobility resilience are identified: Type I, with rapid recovery after mild impact; Type II, exhibiting bimodal recovery after moderate impact; and Type III, showing slower recovery after severe impact. The results also reveal critical thresholds, such as the bimodal recovery breakpoint at a 20% impact extent, at which the recovery rate decreases, and the critical functional threshold at a 60% impact extent, above which recovery rate would be rather slow. The results show that a critical functional recovery rate of 2.5% per day is necessary to follow the bimodal resilience archetype when impact extent exceeds more than 20%. These findings provide novel and important insights into different resilience curve archetypes and their fundamental properties. Departing from using resilience curves as a mere concept and visual tool, the data-driven specification of resilience curve archetypes and their properties improve our understanding of the resilience patterns...
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