Characterizing equitable access to grocery stores during disasters using location-based data

Abstract

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

Disparities in access and association between access to critical facilities during day-to-day and disrupted access as a result of storm extreme weather events

“…Access to critical infrastructure has been identified as a valid proxy for household vulnerability to service losses. Studies have explored access to critical facilities and infrastructure through the lens of individual facilities such as transportation [ 14 , 32 ], healthcare [ [33] , [34] , [35] ], and grocery stores [ 24 ]; and by examining multiple critical facilities together [ 34 , [36] , [37] , [38] ].…”

“… Increased travel time We define the increased travel time as the extra time needed for an individual to reach a critical facility during a storm event, as suggested by Neutens, Delafontaine [ 22 ]. Loss of access We consider the loss of access to be an incapacitation to reach [ 24 ] a certain critical facility as a result of a storm-related event. The loss of access could be a result of secondary causes such as road inundations, high winds or power outages.…”

“… Disparity Disparities are great differences in the way one particular or certain subpopulations access certain critical facility [ 26 ]. The disparities can occur before, after and/or during certain storm-related extreme events [ 24 ]. …”

Spillover Effects of Built-Environment Vulnerability on Resilience of Businesses in Urban Crises

“…Studies have acknowledged the critical role economic resilience plays in post-disaster community recovery. Businesses, especially small, local business, significantly influence the socio-economic well-being of communities during the recovery phase [1] [25] [32] [10]. Prior research focused on the multifaceted nature of business recovery, encompasses physical damage repairs, operational disruptions, and the broader economic impact.…”

Spatial model for predictive recovery monitoring based on hazard, built environment, and population features and their spillover effects

“…Location-based data provide insights into the spatial distribution of disaster impacts by gathering information people’s movement. Using location-based data during different disaster stages has been used to reveal disrupted access, hurricane preparedness, and disaster impacts, as well as the specification of critical recovery milestones (Jiang et al, 2022; Esmalian et al, 2022; Li and Mostafavi, 2022; Lee et al, 2022; Yuan et al, 2022b; Coleman et al, 2022b; Podesta et al, 2021).…”