Specifying Evacuation Return and Home-switch Stability During Short-term Disaster Recovery Using Location-based Data

Abstract

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation return and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short-and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.

Homophilic and Heterophilic Characteristics Shaping Community Formation in Human Mobility Networks During Extreme Weather Response

“…The characterization of human mobility networks is particularly applicable to evaluating population response and recovery to hazards. Human mobility networks capture aspects of population protective actions, such as preparedness (Arrighi et al, 2019;Yuan et al, 2021) and relocation (Deng et al, 2021;Lee et al, 2022;Song et al, 2016); the collective signatures of these protective behaviors become encoded in the structural and dynamical properties of human mobility networks. One significant characteristic of human mobility networks is the formation of communities shaped by population's collective responses to hazards.…”

Human Mobility Networks Manifest Dissimilar Resilience Characteristics at Macroscopic, Substructure, and Microscopic Scales

“…Human mobility networks can reveal insights phenomena such as population response to (Fan, Jiang, & Mostafavi, 2021) impacts on (Yuan, Fan, et al, 2022;Yuan, Yang, Li, & Mostafavi, 2022), and recovery from (Lee, Chou, & Mostafavi, 2022) crises, such as natural disasters. Human movements can serve as a medium for understanding the vulnerability and resilience of a community in facing disasters (C.-W. Hsu, Liu, Nguyen, Chien, & Mostafavi, 2022;Lee, Namburi, Xiao, & Mostafavi, 2022;Rajput & Mostafavi, 2022).…”

Location Intelligence Reveals the Extent, Timing, and Spatial Variation of Hurricane Preparedness

“…The limitations of surveys could be overcome with the increasing availability and popularity of location-based big data. Location intelligence data, also known as location-based service data, could provide important insights regarding human activities and mobility both during normal times, as well as in times of crises ( (Fan, Jiang, & Mostafavi, 2021;Gao et al, 2020;Lee, Chou, & Mostafavi, 2022;Lee, Maron, & Mostafavi, 2021). Location intelligence data is usually collected passively from smartphone devices through GPS, Wi-Fi, and Bluetooth (Darzi, Frias-Martinez, Ghader, Younes, & Zhang, 2021).…”

“…Location intelligence data has shown promise in disaster research. Researchers have used largescale geo-based data to analyze population displacement pattern after the 2010 Haiti earthquake (Lu, Bengtsson, & Holme, 2012); developed methods to characterize flood impacts on a population (Pastor-Escuredo et al, 2014); simulated and predicted human movement pattern after disaster (Song et al, 2016); detected impacts of extreme events on human movement (Roy, Cebrian, & Hasan, 2019); assessed flood inundation status (farahmand, Wang, Mostafavi, & Maron, 2021); quantified resilience based on the magnitude of impacts and time-to-recovery (Hong, Bonczak, Gupta, & Kontokosta, 2021); evaluated hurricane perturbation on urban mobility (Fan et al, 2021); assessed short-term disaster recovery by evacuation return and home switch (Lee et al, 2022). Nevertheless, the majority of studies have harnessed locationbased data for examining human mobility and evacuation in post-disaster contexts, and the potential of these data for examining pre-disaster human preparedness activities has not been realized.…”