Dissecting resilience curve archetypes and properties in human systems facing weather hazards

Resilience curves have been widely used for conceptualizing and representing specific aspects of resilience behavior during hazard events; however, their use has often remained conceptual with limited data-driven characterization and empirical grounding. While broader community resilience encompasses multiple social, economic, and infrastructure dimensions, targeted analyses of specific systems can provide valuable insights into resilience patterns. 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 into archetypes using k-means clustering based on various features (e.g., residual performance, disruption duration, and recovery duration). 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 (i.e., function loss), 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 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 of human systems of communities and enable researchers and practitioners to better anticipate and analyze ways communities bounce back in the aftermath of disruptive hazard events.