paper.md
In ropensci/bikedata: Download and Aggregate Data from Public Hire Bicycle Systems

title: bikedata tags: - public hire bicycle - open data - R authors: - name: Mark Padgham affiliation: 1 orcid: 0000-0003-2172-5265 - name: Richard Ellison affiliation: 2 affiliations: - name: Department of Geoinformatics, University of Salzburg, Austria index: 1 - name: Institute of Transport and Logistics Studies, The University of Sydney, Australia index: 2 bibliography: paper.bib date: 28 Nov 2017

Summary

The R package bikedata collates and facilitates access to arguably the world's largest open ongoing dataset on human mobility. All other comparable sources of data (such public transit data, or mobile phone data) are either not publicly available, or have been released only at single distinct times for single distinct purposes. Many public hire bicycle systems in the U.S.A., along with Santander Cycles in London, U.K., issue ongoing releases of their usage data, providing a unique source of data for analysing, visualising, and understanding human movement and urban environments [@Austwick2013; @Borgnat2011; @Padgham2012]. Such data provide an invaluable resource for urban planners, geographers, social and health scientists and policy makers, data visualisation specialists, and data-affine users of the systems themselves. The bikedata package aims to provide unified access to usage statistics from all public hire bicycle systems which provide data. These currently including Santander Cycles in London, U.K., and from the U.S.A., citibike in New York City NY, Divvy in Chicago IL, Capital Bikeshare in Washington DC, Hubway in Boston MA, Metro in Los Angeles LA, and Indego in Philadelphia PA. Additional systems will be added on an ongoing basis. The package facilitates the three necessary steps of (1) downloading data; (2) storing data in a readily accessible form (in this case in a single SQLite3 database); (3) extracting aggregate statistics. The two primary aggregate statistics are matrices of numbers of trips between all pairs of stations, and daily time series. Both forms of aggregation may be extracted for specific dates, times, or demographic characteristics of cyclists.