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Annals of Operations Research

, Volume 263, Issue 1–2, pp 529–549 | Cite as

Spatial associations in global household bicycle ownership

  • Olufolajimi Oke
  • Kavi Bhalla
  • David C. Love
  • Sauleh Siddiqui
Data Mining and Analytics

Abstract

The interest in bicycling and its determining factors is growing within the public health, transportation and geography communities. Ownership is one factor affecting bicycle usage, but work is still ongoing to not only quantify its effects but also to understand patterns in its growth and influence. In recent work, we mined and discovered patterns in global bicycle ownership that showed the existence of four characteristic country groups and their trends. Building on these results, we show in this paper that the ownership dataset can be modeled as a network. First, we observe mixing tendencies that indicate neighboring countries are more likely to be in the same ownership group and we map the likelihoods for cross-group mixings. Further, we define the strength of connections between countries by their proximity in ownership levels. We then determine the weighted degree assortative coefficient for the network and for each group relative to the network. We find that while the weighted degree assortativity of the ownership network is statistically insignificant, the highest and lowest ownership groups exhibit disassortative behavior with respect to the entire network. The second and third ranked groups, however, are strongly assortative. Our model serves as a step toward further work in studying the relationship between proximity and bicycle ownership among nations and unearthing possible patterns of influence. Considering further developments, this work can inform policy-relevant recommendations toward regional planning. This effort also contributes to expanding research in assortativity analyses, especially in weighted networks.

Keywords

Bicycle ownership Spatial associations Networks Assortativity 

Notes

Acknowledgements

We thank the participants of the 2014 INFORMS Workshop on Data Mining and Analytics participants for their valuable comments in the earlier stages of our work. The Gordon Croft Fellowship awarded by the Energy, Environment, Sustainability and Health Institute \((\hbox {E}^{2}\hbox {SHI})\) at The Johns Hopkins University, Baltimore, Maryland, funded this research in part. This quality of this paper was greatly improved as a result of the work of the two anonymous reviewers to whom the authors are grateful.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  2. 2.International Injury Research UnitThe Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  3. 3.Center for a Livable FutureThe Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  4. 4.Department of Environmental Health SciencesThe Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  5. 5.Department of Applied Mathematics and StatisticsThe Johns Hopkins UniversityBaltimoreUSA
  6. 6.Center for Systems Science and EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  7. 7.Department of Civil and Environmental EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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