🤖 AI Summary
This study investigates the driving mechanisms of community formation in human mobility networks under extreme weather and its implications for optimizing disaster response. Using mobile signaling data from Harris County, Houston, during the extreme cold event “Uri,” we construct a dynamic weighted mobility network and integrate Louvain community detection, multivariate regression, and spatial autocorrelation analysis to quantify— for the first time—the synergistic effects of sociodemographic homophily, heterogeneous risk exposure, and social tie strength. We propose the novel concept of “spatial co-location communities,” revealing that high-risk areas attract inflows toward weakly connected regions and demonstrating strong spatial clustering of such communities. The findings provide electric grid operators with evidence-based, fairness- and resilience-aware criteria for zonal power-shedding decisions, advancing urban resilience governance from reactive crisis management toward proactive, human-mobility-informed optimization.
📝 Abstract
Community formation in socio-spatial human networks is one of the important mechanisms for ameliorating hazard impacts of extreme weather events. Research is scarce regarding latent network characteristics shaping community formation in human mobility networks during natural disasters. We examined human mobility networks in Harris County, Texas, in the context of the managed power outage forced by 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 analysis results show that population movements were shaped by socio-demographic homophily, heterophilic hazard exposure, and social connectedness strength. The results also indicate that a community encompassing more high-impact areas would motivate population movements to areas with weaker social connectedness. Hence, the 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.
