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Choose your own adventure: How data source selection shapes urban accessibility analyses

Geoff Boeing, Jiyoon Kim, Daniele Simon-Thompson, Jaehyun Ha, Ruoyu Chen · 2026 · Journal of Transport Geography

DOI: 10.1016/j.jtrangeo.2026.104841 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

因此,测量是城市和景观规划的核心。尽管研究人员已经讨论了不同可访问性措施对分析结果的影响,但对数据源选择的变量很少关注。可访问性分析的结果是否取决于用于定义公园位置和属性的数据集?我们比较了四种广泛使用的地理空间数据源 – OpenStreetMap,Esri,SafeGraph和公共土地信托 – 通过验证它们的数据和测量两个城市的公园访问量。这些数据集假定涵盖相同的空间特征,但我们的发现揭示了可访问性结果的广泛差异,原因是数据集完整性,覆盖性和来源之间的准确性差异。我们提供针对准确选择和验证可访问性数据的指导方针,因为这些决定直接影响城市公园规划和下游决策的平等

Machine translation (Meta M2M100); the English original follows.

Abstract

Urban park accessibility shapes mental health, physical well-being, and community cohesion. Measuring it is thus central to urban and landscape planning. Although researchers have debated the impacts of different accessibility measures on analysis results, scant attention has been paid to variability from data source selection. Do the results of accessibility analyses vary depending on the dataset used to define park locations and attributes? We compare four widely used geospatial data sources – OpenStreetMap, Esri, SafeGraph, and The Trust for Public Land – by validating their data and measuring park access across two cities. These datasets purport to cover the same spatial features, but our findings reveal wide discrepancies in accessibility results, driven by differences in dataset completeness, coverage, and accuracy between the sources. We provide guidelines for selecting and carefully validating data for accessibility analyses, as these decisions directly impact the accuracy and equity of downstream urban park planning and decision-making.

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Topics: Geospatial analysis, Geocoding, Geography, Land cover, Land use, Computer science

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