Translational Issues in Psychological Science, Vol 10(3), Sep 2024, 262-275; doi:10.1037/tps0000404
Neighborhood physical disorder is linked to deleterious resident physical and mental health. It is thus critical to develop low-cost, reliable methods that utilize publicly available imagery (e.g., Google Street View) to comprehensively audit neighborhoods. We aimed to create a reliable, efficient, and scale-flexible virtual audit of neighborhood disorder (Lot Assessment of Neighborhood Disorder [LAND]) that can be aggregated to larger geographical units. A total of 710 block faces on 355 street segments were coded in Detroit, Michigan. We tested reliability between coders on 20% of the sample (71 segments; i.e., two sides of the street and 146 block faces; i.e., one side of the street) and found that reliability was adequate at the individual lot (κs ranged from .60 to 1), block face (intraclass correlations [ICCs] ranged from .94 to .98), and segment (ICCs ranged from .96 to .98) levels, with the sole exception of graffiti (for which ICCs were typically in the .56–57 range). Moreover, LAND’s score was positively correlated with number of vacant lots, area deprivation, and resident perceptions of their neighborhood. Overall, LAND evidence higher levels of reliability than previous physical neighborhood disorder virtual audits and evidenced significant correlations across several known correlates of physical neighborhood disorder, thus highlighting LAND as an effective virtual audit tool in the study of physical neighborhood disorder. (PsycInfo Database Record (c) 2024 APA, all rights reserved)