From the Department of Epidemiology, University of Washington, Seattle, WA.
Department of Surgery, Division of Trauma and Critical Care, New York University School of Medicine, New York, NY.
Epidemiology. 2020 Mar;31(2):301-309. doi: 10.1097/EDE.0000000000001124.
Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits.
We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery.
Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76).
Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.
评估可能影响行人受伤风险的交叉点方面对于制定儿童行人伤害预防策略至关重要,但访问交叉点进行检查既昂贵又耗时。几个研究小组已经验证了使用 Google 街景来进行虚拟邻里审计的方法,这种方法不需要实地考察团队进行现场审计。
我们开发了一个 38 项的虚拟审计工具,用于评估行人受伤风险的交叉点,并使用带有 Google 街景图像的计算机辅助邻里视觉评估系统 (CANVAS) 在距离纽约市 26 所学校 700 米范围内的交叉点上对其进行了测试。
六名经过培训的审计员在 111 个随机选择的交叉点上测试了该工具的内部信度,在另外 264 个交叉点上测试了其重测信度。内部信度的 Kappa 评分范围从-0.01 到 0.92,近一半的评分超过了 0.41,这是中度一致性的传统阈值。重测信度 Kappa 评分略高于但与内部信度评分高度相关(Spearman rho = 0.83)。高度可靠的项目包括行人信号的存在(K = 0.92)、架空结构(如高架列车或高速公路)的存在(K = 0.81)和交叉点复杂性(K = 0.76)。
使用通过 CANVAS 和 Google 街景实现的虚拟审计协议,可以可靠地测量与行人安全相关的交叉点的建筑环境特征。