Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
Int J Health Geogr. 2020 May 29;19(1):21. doi: 10.1186/s12942-020-00213-5.
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.
虚拟邻里审计已被用于直观评估健康研究相关的建筑环境特征。少数研究调查了审计项目反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区内 31 个与建筑环境相关的单个审计项目的空间特性，重点是预测准确性。
使用 CANVAS 虚拟审计工具评估了大约 8000 个 Google 街景 (GSV) 场景。11 名经过培训的审核员对每个 GSV 场景的 360 视图进行了审核，审核内容包括 10 项人行道、10 项交叉口和 11 项邻里物理障碍相关特征。嵌套半变异函数和回归克里金被用于调查大空间和小空间尺度关系的存在和影响，以及审核员变异性对审计项目空间特性（测量误差、空间自相关、预测准确性）的作用。基于交叉验证空间模型的接收者操作特征曲线 (ROC) 曲线下面积 (AUC) 总结了整体预测准确性。还调查了预测审计项目响应与特定人口统计学、经济和住房特征之间的相关性。
在考虑小尺度和大尺度空间变化的所有项目的空间模型中（与仅大尺度相比），预测准确性更好，并且在大多数模型项目中进一步通过调整审核员来提高预测准确性。除了四个项目外，所有项目的完整模型的空间预测准确性都被认为是“优秀”（0.8≤ROC AUC<0.9）。在与邻里物理障碍相关的项目中，审核员调整后预测准确性最高且提高最多。在交叉口和人行道项目中，与仅大尺度模型相比，大尺度+小尺度模型的预测准确性提高幅度最大。与邻里物理障碍相关的项目的预测响应彼此之间高度相关，并且与种族-民族构成、社会经济指标和居住流动性也高度相关。
人行道和交叉口特征的审核显示出明显的可变性，需要比邻里物理障碍审核更密集的空间样本才能达到相同的准确性。将审核员效应纳入空间模型可以提高预测准确性，特别是在与邻里物理障碍相关的项目中。
