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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.
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Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage
Abstract Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.
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- Award ID(s):
- 1846069
- PAR ID:
- 10394490
- Date Published:
- Journal Name:
- Humanities and Social Sciences Communications
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2662-9992
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1057/s41599-022-01353-8
