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Abstract Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large‐scale mobile phone data. Specifically, we propose a novel enhanced two‐step floating catchment area method with travel preferences (E2SFCA‐TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA‐TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underserved areas.
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This content will become publicly available on April 1, 2026
An Areal Approach to Spatial Accessibility Analysis
Place‐based spatial accessibility quantifies the distribution of access to goods and services across space. The Two‐Step Floating Catchment Area (2SFCA) family of methods have become a default tool for spatial accessibility analysis in part due to their intuitive approach and interpretability. This family of methods relies on calculating catchment areas around supply locations to estimate the area and population that may utilize them. However, these “catchment areas” are generally defined by origin‐destination matrices of travel‐time, giving us point‐to‐point distances and not polygons with actual area. This means that population geographies (census tracts, blocks, etc.) are binarily included or excluded, with no room for partial inclusion. When using nongranular data, which is often the case due to data privacy restrictions, this has the potential to cause significant errors in accessibility measurements. In this article, we propose Areal 2SFCA: a new approach that considers the area of overlap between travel‐time polygons and population geographies. We demonstrate the effectiveness of the Areal 2SFCA method using a case study that compares the Enhanced Two‐Step Floating Catchment Area (E2SFCA) and Areal E2SFCA for the state of Illinois in the USA using multiple population granularities.
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- PAR ID:
- 10637098
- Publisher / Repository:
- Wiley Periodicals LLC
- Date Published:
- Journal Name:
- Geographical Analysis
- Volume:
- 57
- Issue:
- 2
- ISSN:
- 0016-7363
- Page Range / eLocation ID:
- 233 to 269
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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