Validation of Walk Scores and Transit Scores for estimating neighborhood walkability and transit availability: a small-area analysis |
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Authors: | Dustin T. Duncan Jared Aldstadt John Whalen Steven J. Melly |
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Affiliation: | 1. Department of Society, Human Development, and Health, Harvard School of Public Health, 677 Huntington Avenue, Kresge Building 7th Floor, Boston, MA, 02115, USA 2. Department of Geography, University at Buffalo, State University of New York, Buffalo, NY, USA 3. Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
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Abstract: | 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. |
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