Bader Michael D M, Mooney Stephen J, Lee Yeon Jin, Sheehan Daniel, Neckerman Kathryn M, Rundle Andrew G, Teitler Julien O
Department of Sociology and Center on Health, Risk and Society, American University, Washington, DC, United States.
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States.
Health Place. 2015 Jan;31:163-72. doi: 10.1016/j.healthplace.2014.10.012. Epub 2014 Dec 27.
Public health research has shown that neighborhood conditions are associated with health behaviors and outcomes. Systematic neighborhood audits have helped researchers measure neighborhood conditions that they deem theoretically relevant but not available in existing administrative data. Systematic audits, however, are expensive to conduct and rarely comparable across geographic regions. We describe the development of an online application, the Computer Assisted Neighborhood Visual Assessment System (CANVAS), that uses Google Street View to conduct virtual audits of neighborhood environments. We use this system to assess the inter-rater reliability of 187 items related to walkability and physical disorder on a national sample of 150 street segments in the United States. We find that many items are reliably measured across auditors using CANVAS and that agreement between auditors appears to be uncorrelated with neighborhood demographic characteristics. Based on our results we conclude that Google Street View and CANVAS offer opportunities to develop greater comparability across neighborhood audit studies.
公共卫生研究表明,社区环境与健康行为及结果相关。系统性的社区审计有助于研究人员衡量那些他们认为在理论上相关但现有行政数据中没有的社区环境状况。然而,系统性审计实施成本高昂,而且在不同地理区域之间很少具有可比性。我们描述了一种在线应用程序——计算机辅助社区视觉评估系统(CANVAS)的开发,该系统利用谷歌街景对社区环境进行虚拟审计。我们使用这个系统对美国150个街道段的全国样本中与步行便利性和物理无序相关的187个项目进行评分者间信度评估。我们发现,使用CANVAS,许多项目在审计人员之间能够得到可靠的测量,而且审计人员之间的一致性似乎与社区人口特征无关。基于我们的结果,我们得出结论,谷歌街景和CANVAS为在社区审计研究中实现更大的可比性提供了机会。