Rating Protocol: Drop-and-Spin Virtual Neighborhood Auditing for Assessment of Large Geographies
Description
Introduction: Various built environment factors might influence certain health behaviors and outcomes. Reliable and resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger and more robust studies. We report the prevalence and reliability of a new virtual neighborhood audit technique, drop-and-spin auditing, developed specifically for assessment of walkability and physical disorder characteristics across large geographic areas. Methods: Drop-and-spin auditing, a method where a GSV scene was rated by spinning 360o around the location to be rated, was developed using a modified version of the extant virtual audit tool CANVAS. Approximately 8,000 locations within Essex County, New Jersey were assessed by eleven trained auditors. Thirty-two built environment items per a location within Google Street View (GSV) were audited using a standardized protocol. Test-retest and inter-rater Kappa statistics were from a 5% subsample of locations. Data were collected 2017-2018 and were analyzed in 2018. Results: Roughly 70% of GSV scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of GSV scenes. Maximum test-retest reliability indicates substantial agreement (κ ≥ 0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds. Conclusions: Drop-and-spin virtual neighborhood auditing might be a reliable, resource-efficient method for assessing built environment characteristics across large geographies.