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WBC: Walkable and Bikable Communities Project

The Walkable and Bikable Communities Project (WBC) was supported by Cooperative Agreement Number 1- U48/CCU209663 from the Centers for Disease Control and Prevention (CDC) through the University of Washington Health Promotion Research Center.

The team included:

Principal investigator,
Dr. Anne Vernez Moudon.

Co-investigators,
Prof. Allen Cheadle, UW Health Services,
Prof. Donna Johnson, UW Nutritional Sciences,
Dr. Jean-Yves Courbois, UW Statistics,
Prof. Robert Weathers, Seattle Pacific University, Physical Education,
Dr. Cheza Collier, Public Health Seattle and King Co,
Phil Hurvitz, GIS Analyst, UW Forest Resources,
Chanam Lee, Doctoral Research Assistant,
Dr. Tom Schmid, Project Officer at CDCs.

Background
Assessing physical environments for their support of transport- and recreation-based walking serves to gauge their potential to promote walking as a popular form of physical activity. This work sought to establish a gold standard for developing retrospective audit instruments of walking-supportive residential environments. The goals were to identify the measurable aspects of physical environments that were associated with walking and could help promote walking, and to expand the theoretical basis for future research in physically active travel and recreation walking.

Methods
The project used a social ecologic approach. It relied on a 27 minute telephone survey administered to 608 able-bodied respondents randomly sampled over 80 square miles of urban and suburban development in the Puget Sound (Seattle) region. Objectives measures of physical environments came from parcel (polygon) and network GIS databases. Multinomial models served to estimate the relative likelihood of walking moderately (<150 minutes/week), sufficiently for health purposes (>150 minutes/week), and not walking at all. A two-step modeling process included a base model of survey responses and final models, which considered objective measures of environment.

Results
Significant positive associations between demographic, behavior, attitude, perception, and household factors and levels of walking included: age, income, education, health status, level of physical activity and overall walking, transit use, preference for walking and biking to solve congestion, and neighborhood social environment. Significant negative associations were perceived vehicular traffic and problems related to automobiles. Objectively measured environmental variables that were significant include: availability of individual and groups of utilitarian destinations, such as grocery stores/markets, restaurants, retail stores, and banks; shorter distances to these destinations; longer distance to land uses that create undesirable environment for walking, such as large office complexes and educational facilities; higher density and smaller block size of the respondent’s residential location; and greater length of sidewalks along major streets. Recreational facilities, slopes, and objective measures of vehicular traffic were found not to be associated with the likelihood of walking for all purposes (transport and recreation). Objectively measured environmental variables captured approximately one quarter of the final models’ overall variations (Pseudo R-square values range up to 0.47).

Conclusions
Study findings confirmed the value of the social ecologic model to explain the effects of personal, social, community, and environmental factors on walking. Model results highlighted strong relationships between physical environment and walking, controlling for individual and social factors. The statistical models isolated a relatively small number of significant objective environment measures that could facilitate future auditing of environments for their support of walking. These measures could also serve surveillance purposes. Two instruments have been developed and validated to help communities and researchers assess environments’ potential for walking: a survey-based audit for use by lay communities at the level of neighborhood and higher scales and a GIS inventory tool for professionals:

Project results suggested further modeling to understand possible differences in the environmental determinants of transportation versus recreation walking; external validity testing on other cities or regions where similar GIS databases were available; and similar research to be conducted for work-based environments, as well as small-town and rural settings to complete approaches promoting walking as a means to be physically active.

Table 1: Top predictors of walkability in the WBC model

Environmental Characteristic (Threshold Value)

Odds ratio of walking >150 in/week vs. not walking (airline measurement)

Shorter distance to closest grocery store (<440 m)

2.257**

Fewer grocery stores or markets within buffer (less than 3.7)

1.50*

More grocery store/restaurant/retail clusters in 1km buffer (more than 1.8)

1.697**

More dwelling units per acre of the parcel where the residence is located (more than 21.7 units/acre)

1.959**

Fewer educational parcels in 1km buffer (less than 5.1)

1.553*

Smaller size of closest office complex (less than 36,659 m2)

1.28*

Longer distance to closest office/mixed use complex (more than 544 m)

1.27*§

Smaller block size where residence is located (less than 23,876 m2)

1.19*

* p < 0.1; **p < 0.05
Adapted from Moudon AV, Lee C, Cheadle A, et al. Attributes of Environments Supporting Walking. Am J Health Promot. 2007;21(5):448-459. *: significant at 0.1 level; **: significant at 0.05 level

 

 

Walkability Scoring Instruments
The two instruments developed based on the research results and to assess neighborhood walkability are: The Neighborhood Walkability Score Survey Tool  and the Neighborhood Walkability Score GIS Tool. They are available at http://depts.washington.edu/hprc/projects/walkability.htm

WBC1

Figure 1: Map showing the likelihood of walking sufficiently for health (>150 minutes per week) for an average person. The map is the result of surface modeling (SM), a collection of techniques for interpolating from marked point (x, y, and z) data to a three-dimensional surface The map reflects the WBC model results holding socio-demographic variables constant to calculate the effects of environments on amounts of walking (environments were objectively measured on more than 1000 points). The map information constitutes new data layers in GIS, indexing neighborhood walkability.

 

WBC2

Figure 2: WBC Surface model results showing the probabilities of walking sufficiently for health for older (> 65 years) and younger (<35 years) adults.

Publications available on the WBC project
[those not available on PubMed or TRIS databases can be found on ALR website at http://www.activelivingresearch.org/index.php/JPAH_ALR_Supplement/385]

  1. Moudon AV, Lee C. Walking and Biking: An Evaluation of Environmental Audit Instruments. American Journal of Health Promotion 2003; 18: 21-37.
  2. Lee C, Moudon AV. Physical activity and environment research in the health field: Implications for Urban and Transportation Planning Practice and Research. Journal of Planning Literature 2004; 19:147-181
  3. Moudon AV, Lee C, Cheadle AD, Collier CW, Johnson DB, Schmid TL and Weathers RD. Cycling and The Built Environment, A U.S. Perspective. Transportation Research D 2005; 10(3): 245-261.
  4. Lee C, Moudon AV, Courbois JY. Built Environment and Behavior: Spatial Sampling Using Parcel Data. Annals of Epidemiology 2006;16(5):387-394
  5. Lee C, Moudon AV. . The 3Ds + R: Quantifying Land Use and Urban Form Correlates of Walking. Transportation Research Part D: Transport and Environment. 2006; 1(3): 204-215.
  6. Moudon AV, Lee C, Cheadle AD, Garvin CW, Johnson DB, Schmid TL, Weathers RD, Lin L. Operational Definitions of Walkable Neighborhood: Theoretical and Empirical Insights. Journal of Physical Activity and Health 2006; 3 Suppl 1 :S99-117. http://www.activelivingresearch.org/alr/files/JPAH_7_Moudon.pdf
  7. Lee C, Moudon AV. Correlates of Walking for Transportation or Recreation Purposes. Journal of Physical Activity and Health. 2006; 3 Suppl 1:S77-98.
    http://www.activelivingresearch.org/alr/files/JPAH_6_Lee.pdf
  8. Berke E, Koepsell T, Moudon A, Hoskins R. Physical Activity and Obesity in Older Persons: Association With The Built Environment. American Journal of Public Health, 97, 3 :1-7.
  9. Moudon AV, Lee C, Cheadle AD, Garvin CW, Johnson DB, Schmid TL, Weathers RD. Attributes of Environments Supporting Walking. American Journal of Health Promotion, 2007, 21(3):in press
  10. Berke EM, Gottlieb, LM, Moudon AV, Larson, EB. Protective Association Between Neighborhood Walkability and Depression in Older Men. Journal of the American Geriatrics Society. In press, April 2007
  11. Lovasi GS, Moudon AV, Smith NL, Lumley T, Larson EB, Sohn DW, Siscovick DS, Psaty BM. Evaluating Options for Measurement of Neighborhood Socioeconomic Context: Evidence From a Myocardial Infarction Case-control Study. Health Place. 2008,14(3): 453-67
  12. Lovasi, G. S., A. V. Moudon, A. L. Pearson, P. M. Hurvitz, E. B. Larson, D. S. Siscovick, E. M. Berke, T. Lumley and B. M. Psaty. Using Built Environment Characteristics to Predict Walking for Exercise. International Journal of Health Geographics, 2008, 7(10).
  13. Chanam Lee’s dissertation (supported by an ALR/RWJF Dissertation grant): Activity-Friendly Communities: Correlates of Transportation or Recreation Walking, and Correlates of Physical Activity for Different Sub-populations. Seattle, WA: University of Washington; 2004
  14. Lee, C. Environment and Active Living: The Roles of Health Risk and Economic Factors. American Journal of Health Promotion. 2007 Mar-Apr;21(4 Suppl):293-304.

 

Project
EXAMPLES

TELUMI: The Transportation-Efficient Land Use Mapping Index

WBC: Walkable And Bikable Communities Project

Home Versus Nonhome Neighborhood

How to Identify Food Deserts

Walking Objectively Measured