Using Google Street View to audit neighborhood environments

Am J Prev Med. 2011 Jan;40(1):94-100. doi: 10.1016/j.amepre.2010.09.034.


Background: Research indicates that neighborhood environment characteristics such as physical disorder influence health and health behavior. In-person audit of neighborhood environments is costly and time-consuming. Google Street View may allow auditing of neighborhood environments more easily and at lower cost, but little is known about the feasibility of such data collection.

Purpose: To assess the feasibility of using Google Street View to audit neighborhood environments.

Methods: This study compared neighborhood measurements coded in 2008 using Street View with neighborhood audit data collected in 2007. The sample included 37 block faces in high-walkability neighborhoods in New York City. Field audit and Street View data were collected for 143 items associated with seven neighborhood environment constructions: aesthetics, physical disorder, pedestrian safety, motorized traffic and parking, infrastructure for active travel, sidewalk amenities, and social and commercial activity. To measure concordance between field audit and Street View data, percentage agreement was used for categoric measures and Spearman rank-order correlations were used for continuous measures.

Results: The analyses, conducted in 2009, found high levels of concordance (≥80% agreement or ≥0.60 Spearman rank-order correlation) for 54.3% of the items. Measures of pedestrian safety, motorized traffic and parking, and infrastructure for active travel had relatively high levels of concordance, whereas measures of physical disorder had low levels. Features that are small or that typically exhibit temporal variability had lower levels of concordance.

Conclusions: This exploratory study indicates that Google Street View can be used to audit neighborhood environments.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Environment Design*
  • Feasibility Studies
  • Geographic Information Systems / economics
  • Geographic Information Systems / instrumentation*
  • Humans
  • New York City
  • Residence Characteristics*
  • Statistics, Nonparametric
  • Walking