Exploring good cycling cities using multivariate statistics
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Exploring good cycling cities using multivariate statistics Andrew J. Collins1 · Craig A. Jordan1 · R. Michael Robinson1 · Caitlin Cornelius2 · Ross Gore1
© Springer Science+Business Media, LLC, part of Springer Nature 2019
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. Keywords Bicycling · Cycling · City planning · Cluster analysis · Multivariate regression
1 Introduction When a city planner is determining how to improve their cycling infrastructure, they must draw from a variety of information about the practicalities, both technical and social, of any proposed plan. Technical practicalities include building cost and disruption to existing traffic flow. Social practicalities include deciding on where, in the city, to build the cycle path network, likely usage rates, and public support. This paper hopes to help city planners by determining what factors make up a good cycling city and provide information on which cities with successful cycling programs are similar to their own. There are over 3000 cities in the United States, and it is not immediately obvious what factors should be considered to determine the similarity of cities. What role does population play? How influential are median economic and education levels? Do topography, daily temperatures, and average precipitation rates impact cycling rates? The analysis presented in this paper provides insight into these * Andrew J. Collins [email protected] 1
Old Dominion University, Norfolk, VA, USA
Norfolk, USA
2
questions using cluster analysis, and these factors are ranked using regression analysis. We collected a variety of data, both geographic, meteorology, and socio-economic, for 119 U.S. cities, of which 50 are considered good bicycling cities (Bicyclin
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