Activity Spaces, Route Choices, and Neighborhoods: Assessing the Built Environment Associations with Walking Trips

Physical inactivity is one of the leading causes of the obesity epidemic. Bicycling and walking for transportation, also called active travel, is one strategy to address inactive lifestyles. There is growing evidence that supportive built environments can encourage active travel. The goal of this dissertation is to conduct an in-depth inquiry into walking and the built environment with three interrelated studies. The first study develops methods for assessing walkability within individual activity spaces: the geographic region accessible to an individual during a given walking trip. It uses three summary measures for walkability within activity spaces: i) the average walkability score across block segments (representing the general level of walkability in the activity space); ii) the standard deviation (representing the walkability variation); and iii) the network autocorrelation (representing the spatial coherence of the walkability pattern). It assesses the method using data from an empirical study of built environment walkability and walking behavior in Salt Lake City, Utah, USA. It visualizes and maps these activity space summary measures to compare walkability among individuals’ trips within their neighborhoods. Results indicate that there is little match between walkability attribute summaries at Census geographies and summaries at activity spaces. This suggests Census geographies are not appropriate to summarize built environment walkability. The second study develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. It applies the data-driven technique of random forests to select variables for use in walking route choice models. It compares estimated models of data-driven route choice models with theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models, relative to models based on the predefined walkability dimensions. Furthermore, it assesses interpretability and policy relevance of the two sets of models. It finds that data-driven measures are slightly less interpretable, but have better policy relevance. These findings indicate the utility of adopting data-driven techniques to analyze large attribute built environment data sets to explain walking route choice. The third study compares self-defined neighborhoods with revealed activity spaces for explaining home-based walking trips. It first examines the spatial relationship between self-defined neighborhoods and activity spaces derived from participant Global Positioning System (GPS) data. Then, it compares models of the relationships between walking trips and the perceived and objective walkability qualities of neighborhoods. Lastly, it models the changes in walking trips with the change in these qualities. It finds different associations between the built environment and walking trips depending on time period, spatial scale, objective versus perceived measures, and for predicting walking trip counts versus changes in walking trip counts. Results suggest that researchers need to consider carefully the appropriate spatial and temporal scale when using perceived and objective built environment measures for assessing active travel correlates. This dissertation contributes empirical findings to the relationship between the built environment and walking. Also, this dissertation advances analytical methods to assess the appropriate spatial scales to measure the influence of the built environment on walking trips. These contributions further the research into identifying active travel correlates of the built environment. This research adds to the intersection of health and transportation studies to understand and counter the obesity epidemic.

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  • Supplemental Notes:
    • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Ohio State University, Columbus

    Department of Geography, 1036 Derby Hall
    Columbus, OH  United States  43210-1361

    National Institute for Transportation and Communities

    Portland State University
    P.O. Box 751
    Portland, OR  United States  97207

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Tribby, Calvin P
  • Publication Date: 2016


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 176p

Subject/Index Terms

Filing Info

  • Accession Number: 01641181
  • Record Type: Publication
  • Report/Paper Numbers: NITC-D-721
  • Created Date: Jul 17 2017 4:25PM