Realized spatial accessibility vs. potential spatial accessibility in the United States: A case study based on geospatial big data

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems Pub Date : 2026-04-01 Epub Date: 2025-12-10 DOI:10.1016/j.compenvurbsys.2025.102382

Yaxiong Shao , Wei Luo

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引用次数: 0

Abstract

The COVID-19 pandemic drew significant attention to disparities in spatial access to healthcare. While potential spatial accessibility has been extensively researched, realized spatial accessibility remains relatively underexplored. This study employs geospatial big data (SafeGraph Monthly Pattern) to explore the differences between these two types of spatial accessibility using the Two-step Floating Catchment Area (2SFCA) model for the entire population at the Census Tract Level across the contiguous United States. By integrating methods such as point of interest (POI) Placekey matching, Partial Placekey, and fuzzy matching, we successfully matched SafeGraph foot traffic patterns with the American Hospital Association (AHA) survey dataset. Employing total beds as a representation of healthcare facility supply and adjusted SafeGraph visit counts as a representation of the actual healthcare service utilization, the 2SFCA model was applied to compute realized spatial accessibility. A distance decay function, derived from SafeGraph foot traffic patterns, and the same supply data along with potential demand populations were incorporated to calculate potential spatial accessibility. Results show significant differences between potential and realized spatial accessibility across the contiguous US. Compared to realized accessibility measure, the potential spatial accessibility measure significantly underestimates the spatial access to healthcare. Our approach suggests that the realized accessibility based on SafeGraph data can not only help policymakers in making more informed decisions but also serve as a catalyst in improving health access equity.

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美国已实现空间可达性与潜在空间可达性：基于地理空间大数据的案例研究

2019冠状病毒病大流行引起了人们对医疗保健空间可及性差异的高度关注。潜在的空间可达性已经得到了广泛的研究，但对已实现的空间可达性的探索相对较少。本研究采用地理空间大数据（SafeGraph Monthly Pattern），采用两步浮动集水区（two -step Floating Catchment Area, 2SFCA）模型，对美国各地人口普查区的整体人口进行了空间可达性分析。通过整合兴趣点（POI） Placekey匹配、部分Placekey匹配和模糊匹配等方法，我们成功地将SafeGraph人流量模式与美国医院协会（AHA）调查数据集进行了匹配。采用总床位作为医疗设施供应的表示，调整后的SafeGraph访问量作为实际医疗服务利用的表示，应用2SFCA模型计算实现的空间可达性。从SafeGraph步行交通模式中导出的距离衰减函数，以及相同的供应数据和潜在需求人口，结合计算潜在的空间可达性。结果表明，美国相邻地区潜在可达性与实现可达性存在显著差异。与已实现可达性测度相比，潜在空间可达性测度显著低估了医疗服务的空间可达性。我们的方法表明，基于SafeGraph数据实现的可及性不仅可以帮助决策者做出更明智的决策，还可以促进卫生可及性的公平性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers Environment and Urban Systems Multiple-

CiteScore

13.30

自引率

7.40%

发文量

111

审稿时长

32 days

期刊介绍： Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.