Huddling with Families after Disaster: Human Resilience and Social Disparity

Abstract

Disasters, from hurricanes to pandemics, tremendously impact human lives and behaviors. Physical closeness with family post-disaster plays a critical role in mental healing and societal sustainability. Nonetheless, little is known about whether and how family colocation alters after a disaster, a topic of immense importance to a post-disaster society. We analyze 1 billion records of population-scale, granular, individual-level mobile location data to quantify family colocation, and examine the magnitude, dynamics, duration, and socioeconomic heterogeneity of the shift in family colocation from the pre- to post-disaster period. Leveraging Hurricane Florence as a natural experiment, and Geographic Information System (GIS), machine learning, and statistical methods to investigate the shift across the landfall (treated) city of Wilmington, three partially treated cites on the hurricane’s path, and two control cities off the path, we uncover dramatic (18.9%), widespread (even across partially treated cities), and enduring (over 3 months) escalations in family colocation. These findings reveal the powerful psychological and behavioral impact of the disaster upon broader populations, and simultaneously remarkable human resilience via behavioral adaptation during disastrous times. Importantly, the disaster created a gap across socioeconomic groups non-existent beforehand, with the disadvantaged displaying weaker lifts in family colocation. This sheds important lights on policy-making and communication to promote sustainable family colocation, healthy coping strategies against traumatic experiences, social parity, and societal recovery.

Keywords: family colocation, disaster, location big data, machine learning, GIS, statistical analysis