ArticlePreventing chronic disease2026
Assessing the Accuracy and Completeness of OpenStreetMap Sidewalks: A Comparative Study With Google Street View.
Article in Preventing chronic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Introduction: OpenStreetMap is an open-source and crowd-sourced geospatial database and one of the few available sources of sidewalk data for walkability research; however, its accuracy and completeness have not been systematically assessed. This study aimed to compare OpenStreetMap sidewalk data with Google Street View. Methods: We clipped an OpenStreetMap map of pedestrian-accessible roads in Maryland to 141 census tracts (~10.0% of census tracts in Maryland) using stratified random sampling across tertiles of population density. Fifty points within each census tract were randomly distributed across qualifying roads (n = 7,050). We determined sidewalk presence at each point in OpenStreetMap and Google Street View. We excluded points without Google Street View, resulting in a final sample of 6,103 points. In points with sidewalks in Google Street View (n = 3,674), logistic mixed effects models estimated odds of OpenStreetMap-Google Street View sidewalk agreement across population density tertiles and themes from the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). Results: Of the total points (N = 6,103), 57.8% (3,530) indicated sidewalk agreement between OpenStreetMap and Google Street View. Agreement was 30.2% among points with sidewalks present in Google Street View. Among points with sidewalks (3,674) present in Google Street View, odds of sidewalk agreement were higher in mid-density tracts (odds ratio [OR], 2.43; 95% CI, 0.63-9.40) and high-density tracts (OR, 4.50, 95% CI, 1.16-17.38) than in low-density tracts. Tracts in which a racial and ethnic minority population was larger than the non-Hispanic White population had lower sidewalk agreement (OR, 0.11; 95% CI, 0.002-0.74); no other SVI theme was associated with agreement. Conclusion: While OpenStreetMap sidewalk data were generally accurate when available, the sidewalk data were incomplete, especially in areas with low population density or areas in which a non-Hispanic racial and ethnic minority population was larger than the White population.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.