Observational audits commonly are used in public health research to collect data on built environment characteristics that affect health-related behaviors and outcomes, including physical activity and weight status. However, implementing in-person field audits can be expensive if observations are needed over large or geographically dispersed areas or at multiple points in time. A reliable and more efficient method for observational audits could facilitate extendibility (i.e., expanded geographic and temporal scope) and lead to more standardized assessment that strengthens the ability to compare results across different regions and studies. The purpose of the current study was to evaluate the degree of agreement between field audits and audits derived from interpretation of three types of omnidirectional imagery.

Street segments from St. Louis MO and Indianapolis IN were stratified geographically to ensure representation of neighborhoods with different socioeconomic characteristics in both cities. Audits were conducted in 2008 and 2009 using four methods: field audits, and interpretation of archived imagery, new imagery, and Google Street View™ imagery. Agreement between field audits and image-based audits was assessed using observed agreement and the prevalence-adjusted bias-adjusted kappa statistic (PABAK). Data analysis was conducted in 2010. When measuring the agreement between field audits and audits from the different sources of imagery, the mean PABAK statistic for all items on the instrument was 0.78 (archived); 0.80 (new); and 0.81 (Street View imagery), indicating substantial to nearly perfect agreement among methods. It was determined that image-based audits represent a reliable method that can be used in place of field audits to measure several key characteristics of the built environment important to public health research.