论文标题

人类流动性网络在宏观,亚结构和微观尺度上表现出不同的弹性特征

Human Mobility Networks Manifest Dissimilar Resilience Characteristics at Macroscopic, Substructure, and Microscopic Scales

论文作者

Hsu, Chia-Wei, Ho, Matthew Alexander, Mostafavi, Ali

论文摘要

人类流动网络可以揭示对弹性现象的见解,例如人口反应,影响和从危机中恢复。但是,大多数人类流动网络的弹性表征主要集中在宏观网络属性上。关于测得的弹性特征(即影响和恢复持续时间的程度)的变化知之甚少,跨宏观,亚结构(基序)和微观迁移率尺度。为了解决这一差距,在这项研究中,我们研究了由2021年IDA飓风影响的路易斯安那州八个教区的人类流动网络。我们使用基于位置的数据构建了人类移动网络,并检查了三组措施:(1)宏观措施,例如网络密度,巨型组件大小和模块化; (2)实基序分布; (3)微观迁移率措施,例如回旋半径和平均行程距离。为了确定恢复的影响程度和恢复持续时间,我们确定了基线值,并检查了飓风IDA造成的扰动期间措施的波动。结果揭示了从不同尺度下不同度量集获得的影响程度和恢复持续时间的变化。宏观措施(例如巨型组件)往往比子结构和显微镜测量更快地恢复。实际上,与其他量表中的度量相比,微观措施恢复的趋势往往更慢。这些发现表明,人类移动网络中的弹性特征是规模变化的,因此,特定规模的单一度量可能无法代表整个网络中的扰动影响和恢复持续时间。这些结果聚焦于需要在不同尺度上使用措施正确表​​征人类移动网络中的弹性。

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks.

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