Rating Protocol: Drop-and-Spin Virtual Neighborhood Auditing for Assessment of Large Geographies

Introduction: Various built environment factors might influence certain health behaviors and outcomes. Reliable and resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger and more robust studies. We report the prevalence and reliability of a new virtual neighborhood audit technique, drop-and-spin auditing, developed specifically for assessment of walkability and physical disorder characteristics across large geographic areas. Methods: Drop-and-spin auditing, a method where a GSV scene was rated by spinning 360o around the location to be rated, was developed using a modified version of the extant virtual audit tool CANVAS. Approximately 8,000 locations within Essex County, New Jersey were assessed by eleven trained auditors. Thirty-two built environment items per a location within Google Street View (GSV) were audited using a standardized protocol. Test-retest and inter-rater Kappa statistics were from a 5% subsample of locations. Data were collected 2017-2018 and were analyzed in 2018. Results: Roughly 70% of GSV scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of GSV scenes. Maximum test-retest reliability indicates substantial agreement (κ ≥ 0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds. Conclusions: Drop-and-spin virtual neighborhood auditing might be a reliable, resource-efficient method for assessing built environment characteristics across large geographies.

一、引言
多种建成环境因素可能对特定健康行为与健康结局产生影响。为开展更大规模、更严谨的研究，亟需可靠且资源高效、可在大地理范围内评估建成环境特征的可行方法。本研究报道一种专为大地理范围步行性与物理环境杂乱特征评估开发的新型虚拟社区审计技术——定点旋转审计法（drop-and-spin auditing）的应用普及率与可靠性。

二、研究方法
定点旋转审计法（drop-and-spin auditing）是通过环绕待评估点位旋转360°来对谷歌街景（Google Street View, GSV）场景进行评分的方法，其开发基于对现有虚拟审计工具CANVAS的改良版本。本研究由11名经过培训的审计员对新泽西州埃塞克斯县境内约8000个点位开展评估。所有点位均按照标准化方案，对谷歌街景（GSV）内的32项建成环境特征条目进行审计。重测信度与评定者间信度的Kappa统计值来自5%的点位子样本。研究数据采集于2017至2018年，并于2018年完成分析。

三、结果
约70%的谷歌街景场景存在人行道，其中三分之二状况良好。41%的谷歌街景场景中可见至少5处明显的垃圾杂物。所有条目均达到实质性一致的最高重测信度水平（Kappa值≥0.61）。总体而言，各条目的评定者间信度均低于重测信度。单条目评分的中位耗时为7.3秒。

四、结论
定点旋转式虚拟社区审计法或许是一种可靠且资源高效的方法，可用于大地理范围内的建成环境特征评估。