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The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

Overview of attention for article published in PLOS ONE, September 2013
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Title
The Structure of Spatial Networks and Communities in Bicycle Sharing Systems
Published in
PLOS ONE, September 2013
DOI 10.1371/journal.pone.0074685
Pubmed ID
Authors

Martin Zaltz Austwick, Oliver O’Brien, Emanuele Strano, Matheus Viana

Abstract

Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Colombia 2 1%
United Kingdom 2 1%
Spain 2 1%
Brazil 1 <1%
Australia 1 <1%
Denmark 1 <1%
Indonesia 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 151 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 26%
Student > Master 34 20%
Researcher 18 11%
Student > Bachelor 14 8%
Student > Doctoral Student 8 5%
Other 28 17%
Unknown 21 13%
Readers by discipline Count As %
Engineering 32 19%
Social Sciences 21 13%
Computer Science 17 10%
Medicine and Dentistry 12 7%
Physics and Astronomy 11 7%
Other 40 24%
Unknown 33 20%