High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns

Abstract

Major disasters such as extreme weather events can magnify and exacerbate pre-existing social disparities, with disadvantaged populations bearing disproportionate costs. Despite the implications for equity and emergency planning, we lack a quantitative understanding of how these social fault lines translate to different behaviours in large-scale emergency contexts. Here we investigate this problem in the context of Hurricane Harvey, using over 30 million anonymized GPS records from over 150,000 opted-in users in the Greater Houston Area to quantify patterns of disaster-inflicted relocation activities before, during, and after the shock. We show that evacuation distance is highly homogenous across individuals from different types of neighbourhoods classified by race and wealth, obeying a truncated power-law distribution. Yet here the similarities end: we find that both race and wealth strongly impact evacuation patterns, with disadvantaged minority populations less likely to evacuate than wealthier white residents. Finally, there are considerable discrepancies in terms of departure and return times by race and wealth, with strong social cohesion among evacuees from advantaged neighbourhoods in their destination choices. These empirical findings bring new insights into mobility and evacuations, providing policy recommendations for residents, decision-makers, and disaster managers alike.

Human mobility data and analysis for urban resilience: A systematic review

“…The literature review of HMDA and associated risk components (i.e., hazard, exposure, and vulnerability) shows that more papers are related to exposure than to the other components. In contrast, research on vulnerability is limited, although some studies have been published recently (e.g., Yabe et al, 2021b; Deng et al, 2021) (Table 3).…”

“…As pioneers, Yabe and Ukkusuri (2020) studied income inequality in evacuation behavior during Hurricane Irma in Florida, whereas Yabe et al (2021b) explored the links between physical infrastructure (as an exposure factor) and socioeconomic systems (as a vulnerability factor). Deng et al (2021) used high-resolution human mobility data to reveal racial and wealth disparities during Hurricane Harvey. Further research can build on these studies to analyze other socioeconomic characteristics (e.g., ethnicity, age, disability status, and immigration status) for other DRR phases.…”

Social capital's impact on COVID-19 outcomes at local levels

“…In both pandemic and disaster studies, socially vulnerable residents fare worse in terms of resilience to crisis due to employment challenges, limited mobility, and the fear of discrimination or retribution from neighbors or local authorities. Groups more socially vulnerable to disaster including women and single parents 47 , families facing unemployment or poverty 52 , the elderly 53 , the LGBTQ+ community 54 , the disabled 55 , and racial, religious, or ethnic minorities 56 . During COVID, Black Americans have overwhelmingly felt a higher burden 7 – 9 , 57 , with 1 in 1000 African Americans dead from COVID-19 58 at the same time as crushing financial loss, with over 40% of Black-owned businesses in the US closed permanently this past year 59 .…”

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

“…With location-based data, several studies have examined population mobility during disasters (Coleman, Gao, DeLeon, & Mostafavi, 2021;Gray & Mueller, 2012;Hsu, Fan, & Mostafavi, 2021;Lu, Bengtsson, & Holme, 2012;Pastor-Escuredo et al, 2014;Yabe, Ukkusuri, & C. Rao, 2019) and assessed disaster impacts (Bonaccorsi et al, 2020;Esmalian, Yuan, Xiao, & Mostafavi, 2022;Fan, Jiang, & Mostafavi, 2020;Lee, Maron, & Mostafavi, 2021;Q. Wang & Taylor, 2014;Yuan, Yang, Li, & Mostafavi, 2022); however, the majority of these studies focus on evacuation patterns (Deng et al, 2021;Song et al, 2016), disruption in mobility (Arrighi, Pregnolato, Dawson, & Castelli, 2019;Esmalian et al, 2021;Galeazzi et al, 2021), and mobility resilience (Fan, Jiang, & Mostafavi, 2021;Fan, Yang, & Mostafavi, 2021;Roy, Cebrian, & Hasan, 2019;Y. Wang, Wang, & Taylor, 2017).…”