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Related Experiment Video

Updated: Jun 6, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Using Google Street View to audit neighborhood environments.

Andrew G Rundle¹, Michael D M Bader, Catherine A Richards

¹Department of Epidemiology, Columbia University, New York, New York 10032, USA. agr3@columbia.edu

American Journal of Preventive Medicine

|December 15, 2010

Summary

Google Street View offers a feasible method for auditing neighborhood environments, potentially reducing costs and time compared to in-person assessments. This digital tool shows promise for public health research and urban planning.

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Area of Science:

Urban Health
Environmental Auditing
Geospatial Technology

Background:

Neighborhood environments significantly impact health and behavior.
Traditional in-person environmental audits are resource-intensive.
Google Street View presents a potential low-cost alternative for data collection.

Purpose of the Study:

To evaluate the feasibility of using Google Street View for neighborhood environment audits.
To compare Google Street View data with traditional field audit data.

Main Methods:

Compared 2008 Google Street View data with 2007 field audit data for 37 NYC block faces.
Assessed 143 items across seven neighborhood constructs.
Used percentage agreement and Spearman correlations to measure data concordance.

Main Results:

High concordance (≥80% agreement or ≥0.60 correlation) found for 54.3% of items.
Pedestrian safety and active travel infrastructure showed higher concordance.
Physical disorder measures and small/temporally variable features had lower concordance.

Conclusions:

Google Street View is a feasible tool for neighborhood environment auditing.
The technology shows particular promise for objective measures like infrastructure.
Further research may refine its application for subjective or dynamic features.