论文标题

通过人类流动网络来表征社区形成以应对极端天气事件

Characterizing Community Formation in Response to Extreme Weather Events through Human Mobility Networks

论文作者

Lee, Cheng-Chun, Ma, Junwei, Yin, Kai, Namburi, Siri, Xiao, Xin, Mostafavi, Ali

论文摘要

社会空间人类网络中的社区形成是减轻极端天气事件危害影响的重要机制之一。关于自然灾害期间人类流动网络中社区形成的潜在网络特征的研究很少。在这里,我们检查了德克萨斯州哈里斯县的人类流动网络,这是在2021年冬季风暴Uri强迫的托管停电中,以检测社区并评估这些社区的潜在特征。我们在人类流动性网络中形成的社区中检查了三个特征:危险暴露于异质,社会人口统计学同性恋和社会联系的力量。结果表明,人口运动是由社会人口统计学同质性,异性危害暴露和社会联系强度塑造的。我们的结果还表明,涵盖更多高影响力地区的社区将激发人口运动到社会联系较弱的地区。我们的发现揭示了在危险响应中塑造人类流动网络中社区形成的重要特征。特定于托管停电,形成的社区是空间共同的,强调了最佳的管理实践,以避免社区内部地区长时间的停电,从而改善危险暴露。这些发现对电力公司操作员在确定托管停电模式时会说明社会空间人类网络的特征。

Community formation in socio-spatial human networks is one of the important mechanisms for mitigating hazard impacts of extreme weather events. Research is scarce regarding latent network characteristics shaping community formation in human mobility networks during natural disasters. Here, we examined human mobility networks in Harris County, Texas, in the context of the managed power outage forced by 2021 Winter Storm Uri to detect communities and to evaluate latent characteristics in those communities. We examined three characteristics in the communities formed within human mobility networks: hazard-exposure heterophily, socio-demographic homophily, and social-connectedness strength. The results show that population movements were shaped by socio-demographic homophily, heterophilic hazard exposure, and social connectedness strength. Our results also indicate that a community encompassing more high-impact areas would motivate population movements to areas with weaker social connectedness. Our findings reveal important characteristics shaping community formation in human mobility networks in hazard response. Specific to managed power outages, formed communities are spatially co-located, underscoring a best management practice to avoid prolonged power outages among areas within communities, thus improving hazard exposure heterophily. The findings have implications for power utility operators to account for the characteristics of socio-spatial human networks when determining the patterns of managed power outages.

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