Spatial Access of America: Multiple indicators of accessibility to opportunities

Spatial measures of accessibility are widely used in urban and transportation planning to understand the impact of the transportation system on influencing people’s access to places, but publicly available large-scale datasets are rare and limited. This paper presents a highly parametric dataset con...

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Bibliographic Details
Published in:Scientific data Vol. 12; no. 1; pp. 1223 - 13
Main Authors: Verma, Rajat, Mittal, Shagun, Ukkusuri, Satish V.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.07.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/986
706/2808
706/689/159
Censuses
Competition
Data Descriptor
Datasets
Humanities and Social Sciences
Infrastructure
multidisciplinary
Science
Science (multidisciplinary)
Urban areas
Urban planning
United States > US
ISSN:2052-4463, 2052-4463
Online Access:Get full text
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Summary:Spatial measures of accessibility are widely used in urban and transportation planning to understand the impact of the transportation system on influencing people’s access to places, but publicly available large-scale datasets are rare and limited. This paper presents a highly parametric dataset containing values of spatial accessibility measured by combinations of multiple metrics, travel modes, types of opportunity (including jobs and amenities like schools, hospitals, and electric vehicle charging stations), and travel time thresholds. This includes both cumulative opportunities types of measures as well as competition metrics. A total of 600 accessibility values are computed for each zone at three administrative levels for the 50 most populous urban areas of the United States. Additionally, the dataset also includes the travel time matrix files for each of these urban areas by three travel modes – driving, walking, and bicycling – to facilitate self-validation. Further, comparisons with similar travel time and accessibility datasets show a high degree of similarity with our dataset.
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ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-025-05440-8