BACKGROUND: We investigate the association of different composite walkability measures with individual walking behaviors to determine if multicomponent metrics of walkability are more useful for assessing the health impacts of the built environment than single component measures. METHODS: We use a previously published composite walkability measure as well as a new measure that was designed to represent easier methods of combination and which includes 2 metrics obtained using Google data sources. Logistic regression was used to assess the relationship between walking behavior and walkability metrics. RESULTS: Our results suggest that composite measures of walkability are more consistent predictors of walking behavior than single component measures. Furthermore, a walkability measure developed using free, publicly available data from Google was found to be nearly as effective in predicting walking outcomes as a walkability measure derived without such publicly and nationally available measures. CONCLUSIONS: Our findings demonstrate the effectiveness of free and locally relevant data for assessing walkable environments. This facilitates the use of locally derived and adaptive tools for evaluating the health impacts of the built environment.
BACKGROUND: We investigate the association of different composite walkability measures with individual walking behaviors to determine if multicomponent metrics of walkability are more useful for assessing the health impacts of the built environment than single component measures. METHODS: We use a previously published composite walkability measure as well as a new measure that was designed to represent easier methods of combination and which includes 2 metrics obtained using Google data sources. Logistic regression was used to assess the relationship between walking behavior and walkability metrics. RESULTS: Our results suggest that composite measures of walkability are more consistent predictors of walking behavior than single component measures. Furthermore, a walkability measure developed using free, publicly available data from Google was found to be nearly as effective in predicting walking outcomes as a walkability measure derived without such publicly and nationally available measures. CONCLUSIONS: Our findings demonstrate the effectiveness of free and locally relevant data for assessing walkable environments. This facilitates the use of locally derived and adaptive tools for evaluating the health impacts of the built environment.
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