Virtual 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. Approximately 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. Prediction 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. Audits 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.

中文翻译：

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通过 drop-and-spin 虚拟邻域审计评估的建筑环境特征的空间预测特性。

虚拟邻里审计已被用于直观地评估建筑环境的特征，以进行健康研究。很少有研究调查审计项目响应模式的空间预测特性，这对于抽样效率和审计项目选择很重要。我们调查了美国东北部一个主要大都市地区与建筑环境相关的 31 个单独审计项目的空间属性，重点是预测准确性。使用 CANVAS 虚拟审计工具评估了大约 8000 个谷歌街景 (GSV) 场景。11 名训练有素的评估员审核了每个 GSV 场景的 360° 视图，以了解 10 个人行道、10 个十字路口和 11 个邻里身体障碍相关的特征。嵌套半变异函数和回归克里金法用于研究大小空间尺度关系的存在和影响，以及评估者变异性对审计项目空间属性（测量误差、空间自相关、预测准确性）的作用。基于交叉验证的空间模型的接收算子曲线 (ROC) 曲线下面积 (AUC) 总结了整体预测准确性。调查了预测的审计项目响应与选定的人口、经济和住房特征之间的相关性。在考虑小尺度和大空间尺度变化的所有项目的空间模型中，预测准确性更好（与仅大尺度变化相比），并且在大多数建模项目中通过对评分者的额外调整进一步提高。对于除四个项目之外的所有模型，空间预测准确性被认为是“优秀”（0.8 ≤ ROC AUC < 0.9）。预测准确度最高，并且在评估者调整邻里身体障碍相关项目时提高最多。将大 + 小规模模型与仅大规模模型相比，预测准确性的最大提升出现在交叉路口和人行道项目中。对邻里身体障碍相关项目的预测反应相互之间密切相关，并且还与种族-民族构成、社会经济指标和居住流动性密切相关。对人行道和交叉口特征的审计表现出明显的可变性，需要比邻里物理障碍审计更多的空间密集样本才能达到同等的准确性。