Abstract
BackgroundVirtual neighborhood audits have been used to visually assess characteristics of the built environment for health research. Few studies have investigated spatial predictive properties of audit item responses patterns, which are important for sampling efficiency and audit item selection. We investigated the spatial properties, with a focus on predictive accuracy, of 31 individual audit items related to built environment in a major Metropolitan region of the Northeast United States.MethodsApproximately 8000 Google Street View (GSV) scenes were assessed using the CANVAS virtual audit tool. Eleven trained raters audited the 360° view of each GSV scene for 10 sidewalk-, 10 intersection-, and 11 neighborhood physical disorder-related characteristics. Nested semivariograms and regression Kriging were used to investigate the presence and influence of both large- and small-spatial scale relationships as well as the role of rater variability on audit item spatial properties (measurement error, spatial autocorrelation, prediction accuracy). Receiver Operator Curve (ROC) Area Under the Curve (AUC) based on cross-validated spatial models summarized overall predictive accuracy. Correlations between predicted audit item responses and select demographic, economic, and housing characteristics were investigated.ResultsPrediction accuracy was better within spatial models of all items accounting for both small-scale and large- spatial scale variation (vs large-scale only), and further improved with additional adjustment for rater in a majority of modeled items. Spatial predictive accuracy was considered ‘Excellent’ (0.8 ≤ ROC AUC < 0.9) for full models of all but four items. Predictive accuracy was highest and improved the most with rater adjustment for neighborhood physical disorder-related items. The largest gains in predictive accuracy comparing large- + small-scale to large-scale only models were among intersection- and sidewalk-items. Predicted responses to neighborhood physical disorder-related items correlated strongly with one another and were also strongly correlated with racial-ethnic composition, socioeconomic indicators, and residential mobility.ConclusionsAudits of sidewalk and intersection characteristics exhibit pronounced variability, requiring more spatially dense samples than neighborhood physical disorder audits do for equivalent accuracy. Incorporating rater effects into spatial models improves predictive accuracy especially among neighborhood physical disorder-related items.
Highlights
Virtual neighborhood audits have been used to visually assess characteristics of the built environment for health research
In order to have enough power to test for spatial autocorrelation, which studies of similar constructs have reported occur within distances of 1000 meters, the above Geographic Information System (GIS) operations were repeated until the average point-to-point near distance was within 1 standard deviation of 150 meters, resulting in 8262 total candidate audit locations (25.3 per square km) (Fig. 1)
There are, notable trends especially when audit item response patterns are considered within item groupings
Summary
Virtual neighborhood audits have been used to visually assess characteristics of the built environment for health research. We investigated the spatial properties, with a focus on predictive accuracy, of 31 individual audit items related to built environment in a major Metropolitan region of the Northeast United States. Characteristics of the built environment measured through visual observation have been associated with various health-related factors and outcomes, including physical activity [1, 2], obesity [3], injuries [4], violence [5], diabetes [6], and depression [7, 8]. Associations of many of these studies are small, underscoring the importance of well-designed measures of the built environment that maximize accuracy [12]
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