Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas

Abstract

Optimization of existing measurement tools is necessary to explore links between aspects of the neighborhood built environment and health behaviors or outcomes. We evaluate a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assess street segment representativeness in low-income neighborhoods. Eighty-two home neighborhoods of Washington, D.C. Cardiovascular Health/Needs Assessment (NCT01927783) participants were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0-2 points/question. A combinations algorithm was developed to assess street segments' representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score^®, a validated neighborhood walkability measure. Street segment quality scores ranged 10-47 (Mean = 29.4 ± 6.9) and overall neighborhood quality scores, 172-475 (Mean = 352.3 ± 63.6). Walk scores^® ranged 0-91 (Mean = 46.7 ± 26.3). Street segment combinations' correlation coefficients ranged 0.75-1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores^® (r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in low-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes.

Keywords: Active Neighborhood Checklist; Google Street View; Walk Score®; Washington D.C. Cardiovascular Health and Needs Assessment; built neighborhood environment; environment quality; residential neighborhoods; virtual audits.

Figure 1

This is an example of a neighborhood segment map that would be created for each participant before virtual audits. The lines denote different streets, forming street intersections where lines intersect. The blue dot denotes a participant’s residence. The neighborhood around a specific address, or the proximal immediate neighborhood, is defined as the 12 closest street segments to a specific address. Street segment is consistent with established definitions of where one street intersects another.

Figure 2

Correlation coefficients obtained from street segment combinations coding. Combination coefficients were generated by code developed in Statistical Analysis Software which runs a series of combinations of scores from one street segment up to twelve segments (i.e., ₁₂C₁, ₁₂C₂…₁₂C₁₂) per participant, simulating all possible ways in which a certain number of street segment scores can be randomly chosen from the overall 12 street segment scores, without any repeated segment combinations. The code calculated means for the selected combinations then averaged the means across all participants to obtain overall combination means (i.e., overall means for ₁₂C₁, ₁₂C₂…₁₂C₁₂). Spearman calculations used to determine associations between overall segment combination means (i.e., ₁₂C₁, ₁₂C₂…₁₂C₁₂) and participants’ segment means.