1. Using GIS to Examine the Relationship Between Recreational vs. Utilitarian Walking and Bicycling Amy Zlot Richard Killingsworth Sandra Ham Muthukumar Subrahmanyam Muthukumar Subrahmanyam Laurie Barker

2. Background: Physical Activity Physical Inactivity is a primary factor in the following: –25% of chronic disease deaths –10% of all deaths in the U.S. annually Adult U.S. Population, 2000 –27% sedentary –57% overweight

3. Background: Physical Activity 197419781982198619901994 10 15 20 25 30 35 40 Year Percentage Trips made on foot 5 0 Adults who are overweight

4. Background: Physical Activity Urban Form: –Communities can be designed to promote physical activity

5. Hypothesis Metropolitan Statistical Areas (MSAs) that exhibit high levels of utilitarian walking/bicycling also exhibit high levels of recreational walking/bicycling

6. Assumptions Utilitarian walking/bicycling is a proxy for infrastructure. High levels of utilitarian walking/bicycling indicate the following: –More sidewalks –More bikeways –Greater overall connectivity

7. Methods Leisure-time physical activity data – BRFSS, 1996 & 1998: –“What type of physical activity or exercise did you spend the most time doing in the past month?” Travel behavior data – NPTS, 1995: –“What means of transportation did you use for this trip?” Software: SAS, SUDAAN, ArcView

8. Correlation Results Recreational vs. Utilitarian Walking/Bicycling 65 60 55 50 510152025 Recreational % Utilitarian %

9. Correlation Results Recreational vs. Utilitarian Walking/Bicycling (excluding New York) 65 60 55 50 246812 Recreational % Utilitarian % 10

16. Recreational/Utilitarian Matrix High Util / High Rec Denver, CO Los Angeles-Long Beach, CA New Orleans, LA Oakland, CA Orange County, CA Riverside-San Bernardino, CA Portland-Vancouver, OR-WA San Francisco, CA San Jose, CA High Rec / Low Util Detroit, MI Milwaukee-Waukesha, WI Monmouth-Ocean, NJ Oklahoma City, OK Pittsburgh, PA Sacramento, CA Saint Louis, MO-IL Salt Lake City-Ogden, UT San Diego, CA Seattle-Bellevue-Everette, WA Tampa-St. Petersburg-Clearwater, FL High Util / Low Rec Baltimore, MD Bergen-Passaic, NJ Chicago, IL Cincinnati, OH-KY Las Vegas, NV-AZ Minneapolis-St. Paul, MN-WI Nassau-Suffolk, NY New York, NY Newark, NJ Philadelphia, PA-NJ Washington, DC - MD, VA, WV Low Util / Low Rec Atlanta, GA Buffalo-Niagara Falls, NY Cleveland-Lorain-Elyria, OH Columbus, OH Houston, TX Miami, FL Middlesex-Somerset-Hunterdon, NJ Phoenix-Mesa, AZ Rochester, NY

17. Conclusions No clear correlation between recreational and utilitarian walking/bicycling. Possible Explanations: –No correlation. –Travel behavior may not be a proxy for infrastructure. –MSA too large to detect a correlation. –Questionnaire restrictions.

18. Limitations Ecological Fallacy (two disparate data sets) –Association at aggregate vs. individual level Selection Bias: –Limited to 40 MSAs (12% of all MSAs) Sample design not support analysis at MSA level Covariates not included in analysis

19. Lessons Learned Gaps in recreational and travel behavior data at the MSA level A GIS can display meaningful patterns

20. Future Directions Understand urban form, behavior and morbidity outcomes

21. Presentation Available: http://apha.confex.com/apha/129am/techprogram/ paper_27398.htm Contact Information: Amy Zlot: azlot@cdc.gov Richard Killingsworth: rich_killingsworth@yahoo.com