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Validation of Walk Scores and Transit Scores for estimating neighborhood walkability and transit availability: A small-area analysis

机译:验证步行分数和公交分数以估算邻域的步行性和公交可用性:小区域分析

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We investigated the validity of Walk Scores and Transit Scores from the Walk Score website using several objective geographic information systems (GIS) measures of neighborhood walkabiltiy and transit availability based on 400-and 800-m street network buffers. Address data come from the 2008 Boston Youth Survey Geospatial Dataset, a school-based sample of public high school students in Boston, MA with complete residential address information (n = 1,292). GIS data were used to create multiple objective measures of neighborhood walkability and transit availability. We also obtained Walk Scores and Transit Scores. We calculated Spearman correlations of Walk Scores and Transit Scores with the GIS neighborhood walkability/transit availability measures as well as Spearman correlations accounting for spatial autocorrelation. Several significant correlations were observed between Walk Score and 400-m buffer GIS measures of neighborhood walkability; all significant correlations were found for the 800-m buffer. All correlations between Transit Scores and GIS measures of neighborhood transit availability were also significant (all p < 0. 0001). However, the magnitude of correlations varied by the GIS measure and neighborhood definition. Relative to the 400-m buffer, correlations for the 800-m buffer were higher. This study suggests that Walk Score is a good, convenient tool to measure certain aspects of neighborhood walkability and transit availability (such as density of retail destinations, density of recreational open space, intersection density, residential density and density of subway stops). However, Walk Score works best at larger spatial scales.
机译:我们使用基于400和800 m街道网络缓冲区的邻里步行能力和公交可用性的几种客观地理信息系统(GIS)措施,从“步行分数”网站调查了步行分数和公交分数的有效性。地址数据来自2008年波士顿青年调查地理空间数据集,该数据是马萨诸塞州波士顿市公立中学生的学校样本,其中包含完整的住所地址信息(n = 1,292)。 GIS数据用于创建邻里步行和公交可用性的多个客观指标。我们还获得了步行分数和公交分数。我们使用GIS邻域的可步行性/公交可用性度量值以及考虑空间自相关的Spearman相关性来计算步行分数和公交分数的Spearman相关性。步行得分与邻里步行能力的400-m缓冲GIS度量之间观察到一些显着相关性;对于800 m缓冲区,发现所有显着的相关性。公交成绩与GIS邻里公交可用性度量之间的所有相关性也都非常显着(所有p <0. 0001)。但是,相关程度因GIS度量和邻域定义而异。相对于400 m缓冲区,800 m缓冲区的相关性更高。这项研究表明,步行得分是一种衡量社区步行能力和公交可用性的某些方面的便捷工具(例如零售目的地的密度,休闲场所的密度,交叉路口的密度,住宅的密度和地铁站的密度)。但是,步行分数在较大的空间范围内效果最佳。

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