Evidence map›Paper›PMID 31596793›Full record

ArticleEpidemiology (Cambridge, Mass.)2020

Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

Stephen J Mooney, Katherine Wheeler-Martin, Laura M Fiedler, Celine M LaBelle, Taylor Lampe, Andrew Ratanatharathorn, Nimit N Shah, Andrew G Rundle, Charles J DiMaggio

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
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  5. Validation of the Urban Walkability Perception Questionnaire (UWPQ) in the Balearic Islands.International journal of environmental research and public health · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Stephen J MooneyFrom the Department of Epidemiology, University of Washington, Seattle, WA.
Katherine Wheeler-MartinDepartment of Surgery, Division of Trauma and Critical Care, New York University School of Medicine, New York, NY.
Laura M FiedlerThe Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School, Hanover, NH.
Celine M LaBelleEdward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, New Brunswick, NJ.
Taylor LampeDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.
Andrew RatanatharathornDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.
Nimit N ShahDepartment of Biostatistics and Epidemiology, School of Public Health, Rutgers, The State University of New Jersey, Piscataway, NJ.
Andrew G RundleDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.
Charles J DiMaggioDepartment of Surgery, Division of Trauma and Critical Care, New York University School of Medicine, New York, NY.
Columbia University · USNew York University · USDartmouth Institute for Health Policy and Clinical Practice · USRutgers, The State University of New Jersey · USUniversity of Washington · US

Funding

Data Integration and Advanced Statistical Modeling to Describe and Control Pediatric Pedestrian Injuries in The United StatesR01HD087460 · NICHD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI DIMAGGIO, CHARLES · 2016 to 2018
$984k
NCIPC CDC HHS R49 CE002096NICHD NIH HHS R01 HD087460
6 · The paper itself

Abstract

backgroundAssessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits.

methodsWe developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery.

resultsSix trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76).

conclusionsBuilt environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

Indexed as

Built EnvironmentGeographic Information SystemsPedestriansResidence CharacteristicsSafetyHumansNew York CityReproducibility of ResultsWounds and Injuries

Identifiers

PMID31596793
PMCPMC7002252
OpenAlexW2980275160

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.