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Virtual audits of the urban streetscape: comparing the inter-rater reliability of GigaPan® to Google Street View.
International Journal of Health Geographics ( IF 4.9 ) Pub Date : 2020-08-12 , DOI: 10.1186/s12942-020-00226-0
Katherine N Bromm 1 , Ian-Marshall Lang 1 , Erica E Twardzik 1, 2 , Cathy L Antonakos 1 , Tamara Dubowitz 3 , Natalie Colabianchi 1, 4
Affiliation  

Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan®, a tool that assists with capturing high-definition panoramic images, relative to Google Street View. Street segments (n = 614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen’s kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan® and Google Street View. GigaPan® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using GigaPan® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of prevalence and bias in future reliability studies, researchers should consider using PABAK to supplement or expand upon Cohen’s kappa findings.

中文翻译:

城市街道景观的虚拟审核:比较GigaPan®和Google街景视图的评估者之间的可靠性。

尽管先前的研究强调了建筑环境与个人健康之间的关系,但是评估建筑环境的方法仍然存在挑战。特别是,许多研究人员已经证明,使用Google Street View评估大型或客观的建筑环境特征的评估者间可靠性高,而评估小型或主观的建筑环境特征的评估者间可靠性低。必须评估用于审核建筑环境的新方法,以了解是否存在其他工具,研究人员可以通过这些工具高度一致地评估所有类型的建筑环境特征。本文研究了GigaPan®(与Google街景视图有关的辅助捕获高清全景图像的工具)的评估者间可靠性的度量。使用GigaPan®和Google街景视图,随机选择了美国宾夕法尼亚州匹兹堡的街道路段(n = 614)。每次审核都评估与土地使用,交通和安全以及公共设施有关的功能。评估者之间的可靠性统计数据,包括一致性百分比,Cohen的kappa和流行度调整后的偏差调整后的kappa(PABAK),是由两个不同的人工审核员编码的106个路段计算得出的。大多数大规模的客观特征(例如,公交车站的存在或停车标志的存在)都显示出两种方法的评分者之间的可靠性至少是相当高的,但是细致的特征(例如,垃圾箱)和路段终点的特征(例如,人行道的连续性)却出现了显着差异。 )。调整偏见和普遍性的影响后,对于跨GigaPan®和Google Street View的几乎所有内置环境功能,评估者之间的可靠性估计始终较高。GigaPan®是Google Street View的可靠替代审核工具,用于研究建筑环境。GigaPan®可能特别适合于在不存在或很少更新Google Street View图像的区域进行研究设置的建筑环境项目。使用GigaPan®增强细节图像的潜力在需要当前,时间敏感数据或难以在Google Street View中看到微型建筑环境特征的研究中将是最有益的。此外,为了更好地了解普遍性和偏见在未来可靠性研究中的影响,研究人员应考虑使用PABAK补充或扩展Cohen的kappa发现。
更新日期:2020-08-12
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