Department of Epidemiology, Columbia University, New York, New York 10032, USA.
Am J Prev Med. 2011 Jan;40(1):94-100. doi: 10.1016/j.amepre.2010.09.034.
Research indicates that neighborhood environment characteristics such as physical disorder influence health and health behavior. In-person audit of neighborhood environments is costly and time-consuming. Google Street View may allow auditing of neighborhood environments more easily and at lower cost, but little is known about the feasibility of such data collection.
To assess the feasibility of using Google Street View to audit neighborhood environments.
This study compared neighborhood measurements coded in 2008 using Street View with neighborhood audit data collected in 2007. The sample included 37 block faces in high-walkability neighborhoods in New York City. Field audit and Street View data were collected for 143 items associated with seven neighborhood environment constructions: aesthetics, physical disorder, pedestrian safety, motorized traffic and parking, infrastructure for active travel, sidewalk amenities, and social and commercial activity. To measure concordance between field audit and Street View data, percentage agreement was used for categoric measures and Spearman rank-order correlations were used for continuous measures.
The analyses, conducted in 2009, found high levels of concordance (≥80% agreement or ≥0.60 Spearman rank-order correlation) for 54.3% of the items. Measures of pedestrian safety, motorized traffic and parking, and infrastructure for active travel had relatively high levels of concordance, whereas measures of physical disorder had low levels. Features that are small or that typically exhibit temporal variability had lower levels of concordance.
This exploratory study indicates that Google Street View can be used to audit neighborhood environments.
研究表明，邻里环境特征，如物质无序，会影响健康和健康行为。对邻里环境进行实地审核既昂贵又耗时。谷歌街景也许可以更容易且成本更低地对邻里环境进行审核，但关于这种数据收集的可行性知之甚少。
评估使用谷歌街景审核邻里环境的可行性。
本研究比较了 2008 年使用街景拍摄的邻里测量值和 2007 年收集的邻里审核数据。样本包括纽约市高步行性社区的 37 个街区。实地审核和街景数据是针对与七个邻里环境结构相关的 143 个项目收集的：美观、物质无序、行人安全、机动车和停车、促进积极出行的基础设施、人行道设施和社会及商业活动。为了衡量实地审核和街景数据之间的一致性，使用分类测量的百分比一致性，以及连续测量的斯皮尔曼等级相关系数。
在 2009 年进行的分析中，发现 54.3%的项目具有较高的一致性（≥80%的一致性或≥0.60 的斯皮尔曼等级相关系数）。行人安全、机动车和停车以及促进积极出行的基础设施等措施具有较高的一致性，而物质无序的措施一致性较低。较小或通常表现出时间可变性的特征一致性较低。
这项探索性研究表明，谷歌街景可用于审核邻里环境。
