城市洪水灾害中时间性人员流动网络的潜在子结构弹性机制
Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding
摘要：
在研究时间的人类网络的弹性，仅仅依靠全球网络的措施是不够的，潜在的子结构网络机制需要检查，以确定这些网络的影响和恢复的扰动，如城市洪水的程度。在这项研究中，我们利用高分辨率聚合基于位置的灾害相关数据来构建时间人类移动网络。使用构建的时间网络模型，我们研究的特点，如模体分布，模体的持久性和时间稳定性，和模体属性，揭示潜在的子结构机制，在灾害引起的扰动人类流动网络的弹性。结果表明，城市洪水的影响持续几个星期的子结构水平上的人的流动网络。不同网络类型的影响程度和恢复持续时间不同。此外，扰动影响持续在子结构水平，而全球拓扑网络属性可能表明网络已经恢复。研究结果强调了研究微观结构及其动态过程和属性的重要性，在理解时间人类移动网络（和其他时间网络）的弹性时，必须研究微观结构及其扰动影响和恢复。研究结果还可以为灾害管理人员，政府官员和交通规划人员提供见解，以更好地评估影响，并根据子结构和全球网络层面的人类流动网络的影响和恢复模式监测受影响社区的恢复情况。
英文摘要：
In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilized high-resolution aggregated location-based disaster related data to construct temporal human mobility networks. Using the constructed temporal network models, we examined characteristics such as motif distribution, motif persistence and temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persists in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration is heterogeneous across different network types. Also, perturbation impacts persist at the sub-structure level while global topological network properties might indicate the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks), it is essential to examine the microstructures and their perturbation impacts and recovery. The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and to monitor recovery in affected communities based on the patterns of impact and recovery in human mobility networks at both sub-structure and global-network levels.
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