Exploring good cycling cities using multivariate statistics

Abstract

Some U.S. cities are excellent for cycling, like Portland, and some cities are not so good. This observation raises the question: what are the characteristics of a city that make it good for cycling? This study investigates the characteristics of 119 cities to explore what factors help make a city good for cycling. What “good” means in terms of cycling cities is subjective and we use the popular Bicycling Magazine ranking of cities for this purpose. We collected a variety of data sources about our cities including geographic, meteorology, and socioeconomic data. These data were used to conduct cluster analyses and create multivariate generalized linear regression models. We hypothesized that geographic and meteorology factors were important in determining good cycling cities. However, our hypothesis was proved wrong because socio-economic factors, like house pricing and obesity rates, play a more important role. For example, hilly cities, like San Francisco, can have excellent cycling infrastructure. The analysis shows what cities are like each other, regarding our considered characteristics; thus, city planners might wish to look at similar cities to help determine forecasts of expected use and public benefit of cycling. We use a case study of the Hampton Roads region of Virginia to show the application of our regression models.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. Bicycling Magazine (2017) The 50 best bike cities of 2016. https://www.bicycling.com/culture/news/the-50-best-bike-cities-of-2016. Accessed 25 May 2017

  2. BikesForPeople (2019) City ratings. https://cityratings.peopleforbikes.org/. Accessed 16 Aug 2019

  3. Breakaway Now Research Group (2015) U.S. Bicycling participation benchmarking study report, pp 1–64

  4. City of Norfolk (2014) City of Norfolk bicycle and pedestrian strategic plan, pp 1–158

  5. Clark SS, Seager TP, Chester MV (2018) A capabilities approach to the prioritization of critical infrastructure. Environ Syst Decis 38(3):339–352

    Article  Google Scholar 

  6. Community Cycling Center (2012) Understanding barriers to bicycling project. Community Cycling Center, Portland

    Google Scholar 

  7. Elton-Walters J, Wynn N (2017) 17 best cycling apps: iPhone and Android tools for cyclists. https://www.cyclingweekly.com/news/product-news/best-cycling-apps-143222. Accessed 9 June 9, 2017

  8. Everitt BS, Dunn G (2010) Applied multivariate data analysis. Wiley, New York

    Google Scholar 

  9. Ferguson K (2008) The destructive impact of mountain biking on forested landscapes. Environmentalist 28(2):67–68

    Article  Google Scholar 

  10. Geelong Planning Committee (1978) Geelong Bikeplan. Geelong Planning Committee, Geelong

    Google Scholar 

  11. Harkey D, Reinfurt D, Knuiman M (1998) Development of the bicycle compatibility index. Transp Res Rec 1636:13–20

    Article  Google Scholar 

  12. Jackson M, Ruehr E (1998) Let the people be heard: San Diego County Bicycle use and attitude survey. Transp Res Rec 1636:8–12

    Article  Google Scholar 

  13. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  14. Marqués R, Hernández-Herrador V, Calvo-Salazar M, García-Cebrián J (2015) How infrastructure can promote cycling in cities: lessons from Seville. Res Transp Econ 53:31–44

    Article  Google Scholar 

  15. McFadden D (1973) Conditional logit analysis of qualitative choice behavior. Wiley, New York

    Google Scholar 

  16. Meletiou M, Lawrie J, Cook T, Obrien S, Guenther J (2005) Economic impact of investments in bicycle facilities: case study of North Carolina's Northern Outer Banks. Transp Res Rec 1939:15–21

    Article  Google Scholar 

  17. Moudon AV, Lee C, Cheadle AD, Collier CW, Johnson D, Schmid TL, Weather RD (2005) Cycling and the built environment, a US perspective. Transp Res Part D 10(3):245–261

    Article  Google Scholar 

  18. Parkin J, Wardman M, Page M (2008) Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation 35(1):93–109

    Article  Google Scholar 

  19. Pierce J, Kolden CA (2015) The Hilliness of US Cities. Geogr Rev 105(4):581–600

    Article  Google Scholar 

  20. Pucher J, Buehler R (2016) Safer cycling through improved infrastructure. American Public Health Association, Washington, DC

    Google Scholar 

  21. Pucher J, Dill J, Handy S (2010) Infrastructure, programs, and policies to increase bicycling: an international review. Prev Med 50:S106–S125

    Article  Google Scholar 

  22. Pucher J, Buehler R, Merom D, Bauman A (2011) Walking and cycling in the United States, 2001–2009: evidence from the National Household Travel Surveys. Am J Public Health 101(S1):S310–S317

    Article  Google Scholar 

  23. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  24. Schoner JE, Levinson DM (2014) The missing link: Bicycle infrastructure networks and ridership in 74 US cities. Transportation 41(6):1187–1204

    Article  Google Scholar 

  25. Sears J, Flynn B, Aultman-Hall L, Dana G (2012) To bike or not to bike. Transp Res Rec 2314:105–111

    Article  Google Scholar 

  26. Sener I, Eluru N, Bhat C (2009) Who are bicyclists? Why and how much are they bicycling?". Transp Res Rec 2134:63–72

    Article  Google Scholar 

  27. Sorton AA, Walsh T (1998) Bicycle stress level as a tool to evaluate urban and suburban bicycle compatibility. Transp Res Rec 1438:17–24

    Google Scholar 

  28. Statistica (2017) Number of cities, towns and villages (incorporated places) in the United States in 2015, by population size. https://www.statista.com/statistics/241695/number-of-us-cities-towns-villages-by-population-size/. Accessed 25 May 2017

  29. Thorndike RL (1953) Who belongs in the family? Psychometrika 18(4):267–276

    Article  Google Scholar 

  30. Tukey JW (1980) We need both exploratory and confirmatory. Am Stat 34(1):23–25

    Google Scholar 

  31. Your Weather Service (2017) U.S. Climate data. www.usclimatedata.com. Accessed 9 June 2017

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Andrew J. Collins.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Collins, A.J., Jordan, C.A., Robinson, R.M. et al. Exploring good cycling cities using multivariate statistics. Environ Syst Decis 40, 526–543 (2020). https://doi.org/10.1007/s10669-019-09753-z

Download citation

Keywords

  • Bicycling
  • Cycling
  • City planning
  • Cluster analysis
  • Multivariate regression