  Resilience curves have been the primary approach for conceptualizing and
representing the resilience behavior of communities during hazard events;
however, the use of resilience curves has remained as a mere conceptual and
visual tool with limited data-driven characterization and empirical grounding.
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 using
k-means clustering based on various features into archetypes. 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, 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 more than 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...
