Validity of an ecometric neighborhood physical disorder measure constructed by virtual street audit

Abstract

Neighborhood physical disorder is thought to affect mental and physical health, but it has been difficult to measure objectively and reliably across large geographical areas or multiple locales. Virtual street audits are a novel method for assessing neighborhood characteristics. We evaluated the ecometric properties of a neighborhood physical disorder measure constructed from virtual street audit data. Eleven trained auditors assessed 9 previously validated items developed to capture physical disorder (e.g., litter, graffiti, and abandoned buildings) on 1,826 block faces using Google Street View imagery (Google, Inc., Mountain View, California) dating from 2007-2011 in 4 US cities (San Jose, California; Detroit, Michigan; New York, New York; and Philadelphia, Pennsylvania). We constructed a 2-parameter item response theory scale to estimate latent levels of disorder on each block face and defined a function using kriging to estimate physical disorder levels, with confidence estimates, for any point in each city. The internal consistency reliability of the resulting scale was 0.93. The final measure of disorder was positively correlated with US Census data on unemployment and housing vacancy and negatively correlated with data on owner-occupied housing. These results suggest that neighborhood physical disorder can be measured reliably and validly using virtual audits, facilitating research on possible associations between physical disorder and health.

Keywords: cities; data collection; epidemiologic methods; psychometrics; residence characteristics; social environment; spatial analysis; urban health.

Figure 1.

Variation in a measure of neighborhood physical disorder as related to distance between points (semivariograms) across 4 US cities: A) New York, New York; B) Philadelphia, Pennsylvania; C) Detroit, Michigan; and D) San Jose, California. The curve on each plot represents the exponential function visually fitted to that semivariogram. The measure was constructed using Google Street View imagery that was initially captured between 2007 and 2011.

Figure 2.

Interpolated levels of physical disorder in Philadelphia, Pennsylvania, constructed using Google Street View imagery that was initially captured between 2007 and 2011. Lighter areas have more physical disorder; the large, central cluster of physical disorder corresponds roughly with North Philadelphia, and the smaller clusters south and west of it correspond with West Philadelphia and the Gray's Ferry and Point Breeze areas of South Philadelphia.

Figure 3.

Average level of physical disorder as related to 2010 US Census estimates of housing vacancy rates by census tract in Philadelphia, Pennsylvania, estimated using Google Street View imagery dating from 2007–2011.