Abstract
BACKGROUND: Health behaviors are shaped by the context in which people live. However, documenting environmental context has remained a challenge. More specifically, direct observation techniques require large investments in time and resources and auditing the environment through web-based platforms has limited stability in spatio-temporal imagery. This study examined the validity of a new methodology, using GigaPan(®) imagery, where we took photos locally and, stitched them together using GigaPan(®) technology, and quantified environmental attributes from the resulting panoramic photo. For comparison, we examined validity using Google Earth imagery. METHODS: A total of 464 street segments were assessed using three methods: GigaPan(®) audits, Google Earth audits, and direct observation audits. Thirty-seven different attributes were captured representing three broad constructs: land use, traffic and safety, and amenities. Sensitivity (i.e. the proportion of true positives) and specificity (i.e. the proportion of true negatives) were used to estimate the validity of GigaPan(®) and Google Earth audits using direct observation audits as the gold standard. RESULTS: Using GigaPan(®), sensitivity was 80% or higher for 6 of 37 items and specificity was 80% or higher for 31 of 37 items. Using Google Earth, sensitivity was 80% or higher for 8 of 37 items and specificity was 80% or higher for 30 of 37 items. The validity of GigaPan(®) and Google Earth was similar, with significant differences in sensitivity and specificity for 7 items and 2 items, respectively. CONCLUSION: GigaPan(®) performed well, especially when identifying features absent from the environment. A major strength of the GigaPan(®) technology is its ability to be implemented quickly in the field relative to direct observation. GigaPan(®) is a method to consider as an alternative to direct observation when temporality is prioritized or Google Earth imagery is unavailable.