Introduction
Various built environment factors might influence certain health behaviors and outcomes.
Reliable, resource-efficient methods that are feasible for assessing built environment
characteristics across large geographies are needed for larger, more robust studies.
This paper reports the item response prevalence, reliability, and rating time of a
new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment
of walkability and physical disorder characteristics across large geographic areas.
Methods
Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning
360° around a point location, was developed using a modified version of the virtual
audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately
8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors.
Using a standardized protocol, 32 built environment items per a location within Google
Street View were audited. Test–retest and inter-rater κ statistics were from a 5%
subsample of locations. Data were collected in 2017–2018 and analyzed in 2018.
Results
Roughly 70% of Google Street View 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 Google Street View scenes. Maximum test–retest reliability indicated 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
Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing
is quicker and similarly reliable for assessing built environment characteristics.
Assessment of large geographies may be more feasible using drop-and-spin virtual auditing.
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© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
