Street Audits to Measure Neighborhood Disorder: Virtual or In-Person?

Abstract

Neighborhood conditions may influence a broad range of health indicators, including obesity, injury, and psychopathology. In particular, neighborhood physical disorder—a measure of urban deterioration—is thought to encourage crime and high-risk behaviors, leading to poor mental and physical health. In studies to assess neighborhood physical disorder, investigators typically rely on time-consuming and expensive in-person systematic neighborhood audits. We compared 2 audit-based measures of neighborhood physical disorder in the city of Detroit, Michigan: One used Google Street View imagery from 2009 and the other used an in-person survey conducted in 2008. Each measure used spatial interpolation to estimate disorder at unobserved locations. In total, the virtual audit required approximately 3% of the time required by the in-person audit. However, the final physical disorder measures were significantly positively correlated at census block centroids (r = 0.52), identified the same regions as highly disordered, and displayed comparable leave-one-out cross-validation accuracy. The measures resulted in very similar convergent validity characteristics (correlation coefficients within 0.03 of each other). The virtual audit–based physical disorder measure could substitute for the in-person one with little to no loss of precision. Virtual audits appear to be a viable and much less expensive alternative to in-person audits for assessing neighborhood conditions.

data collection, Detroit, Michigan, epidemiologic methods, Google Street View, social environment, spatial analysis, urban health