Using GIS to Examine the Relationship Between Recreational vs. Utilitarian Walking and Bicycling Amy...

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: azlot@cdc.gov

Richard Killingsworth: rich_killingsworth@yahoo.com