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Using GIS to Examine the Using GIS to Examine the Relationship Between Recreational Relationship Between Recreational vs. Utilitarian Walking and Bicyclingvs. Utilitarian Walking and Bicycling
Amy ZlotAmy Zlot
Richard KillingsworthRichard Killingsworth
Sandra HamSandra Ham
Muthukumar Subrahmanyam Muthukumar Subrahmanyam
Laurie BarkerLaurie Barker
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
Background: Physical Activity
1974 1978 1982 1986 1990 1994
10
15
20
25
30
35
40
Year
Perc
en
tag
e
Trips made on foot
5
0
Adults who are overweight
Background: Physical Activity
• Urban Form:– Communities can be designed to promote
physical activity
Hypothesis
• Metropolitan Statistical Areas (MSAs) that exhibit high levels of utilitarian walking/bicycling also exhibit high levels of recreational walking/bicycling
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
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
Correlation Results
Recreational vs. Utilitarian Walking/Bicycling
65
60
55
50
5 10 15 20 25
Recr
eati
onal %
Utilitarian %
Correlation Results
Recreational vs. Utilitarian Walking/Bicycling(excluding New York)
65
60
55
50
2 4 6 8 12
Recr
eati
onal %
Utilitarian %10
Any Recreational Walking and Biking in the Past Month -1996 & 1998 BRFSS
Nlow (36% - 50%)middle (51% 60%)high (61%% - 83%)data unavailable
N
Travel Behavior - Walking and Bicycling Trips - 1995 NPTS
low (2%-5%)medium (6%-9%)high (10%-13%)New York (28%)data unavailable
Recreational and Utilitarian Walking/Bicycling Index
N
High Util/High RecLow Util/High RecHigh Util/Low RecLow Util/Low Recdata unavailable
Recreational and Utilitarian Walking/Bicycling Index
N
High Util/High RecLow Util/High RecHigh Util/Low RecLow Util/Low Recdata unavailable
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Atlanta
N
Features of the Atlanta Metropolitan Statistical Area Level
Atlanta- Lowest Ranked MSAWalking/Bicycling Index
Urban AreasParksNational Parks
&\ Recreational Centers# Cities
Features of the San Francisco Metropolitan Statistical Area Level
N
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San FranciscoOakland
San Jose
Sacramento
Low Util/High RecHigh Util/High RecSan Francisco - Highest Ranked MSA
Walking/Bicycling Index
Urban AreasParksNational Parks
&\ Recreational Centers# Cities
Recreational/Utilitarian Matrix
High Util /High Rec
Denver, COLos Angeles-Long Beach, CANew Orleans, LAOakland, CAOrange County, CARiverside-San Bernardino, CAPortland-Vancouver, OR-WASan Francisco, CASan Jose, CA
High Rec /Low Util
Detroit, MIMilwaukee-Waukesha, WIMonmouth-Ocean, NJOklahoma City, OKPittsburgh, PASacramento, CASaint Louis, MO-ILSalt Lake City-Ogden, UTSan Diego, CASeattle-Bellevue-Everette, WATampa-St. Petersburg-Clearwater, FL
High Util /Low Rec
Baltimore, MDBergen-Passaic, NJChicago, ILCincinnati, OH-KYLas Vegas, NV-AZMinneapolis-St. Paul, MN-WINassau-Suffolk, NYNew York, NYNewark, NJPhiladelphia, PA-NJWashington, DC - MD, VA, WV
Low Util /Low Rec
Atlanta, GABuffalo-Niagara Falls, NYCleveland-Lorain-Elyria, OHColumbus, OHHouston, TXMiami, FLMiddlesex-Somerset-Hunterdon, NJPhoenix-Mesa, AZRochester, NY
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.
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
Lessons Learned
• Gaps in recreational and travel behavior data at the MSA level
• A GIS can display meaningful patterns
Future Directions
• Understand urban form, behavior and morbidity outcomes
Presentation Available: http://apha.confex.com/apha/129am/techprogram/paper_27398.htm
Contact Information:
Amy Zlot: [email protected]
Richard Killingsworth: [email protected]
