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.

Details

Title
High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns
Author
Deng, Hengfang 1 ; Aldrich, Daniel P. 2   VIAFID ORCID Logo  ; Danziger, Michael M. 3 ; Gao, Jianxi 4   VIAFID ORCID Logo  ; Phillips, Nolan E. 5 ; Cornelius, Sean P. 6 ; Wang, Qi Ryan 1   VIAFID ORCID Logo 

 Northeastern University, Department of Civil & Environmental Engineering, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) 
 Northeastern University, Department of Political Science, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) 
 Northeastern University, Network Science Institute, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) 
 Rensselaer Polytechnic Institute, Department of Computer Science, Troy, USA (GRID:grid.33647.35) (ISNI:0000 0001 2160 9198) 
 Harvard University, Department of Sociology, Cambridge, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Accenture, Arlington, USA (GRID:grid.455598.0) (ISNI:0000 0004 5898 4321) 
 Ryerson University, Department of Physics, Toronto, Canada (GRID:grid.68312.3e) (ISNI:0000 0004 1936 9422) 
Publication year
2021
Publication date
Dec 2021
Publisher
Palgrave Macmillan
e-ISSN
2662-9992
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2541134179
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.