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Big Data and Cycling

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Big Data has begun to create significant impacts in urban and transport planning. This paper covers the explosion in data-driven research on cycling, most of which has occurred in the last ten years. We review the techniques, objectives and findings of a growing number of studies we have classified into three groups according to the nature of the data they are based on: GPS data (spatio-temporal data collected using the global positioning system (GPS)), live point data and journey data. We discuss the movement from small-scale GPS studies to the ‘Big GPS’ data sets held by fitness and leisure apps or specific cycling initiatives, the impact of Bike Share Programmes (BSP) on the availability of timely point data and the potential of historical journey data for trend analysis and pattern recognition. We conclude by pointing towards the possible new insights through combining these data sets with each other – and with more conventional health, socio-demographic or transport data.
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Keywords: Big data; Cycling; GPS; bike mobility; bikeshare; spatial analysis

Document Type: Research Article

Affiliations: 1: tGIS Transport, Infrastructure and Territory Research Group, Complutense University of Madrid, Profesor Aranguren S/N, Ciudad Universitaria, 28040 Madrid, Spain 2: Bartlett Centre for Advanced Spatial Analysis, University College London, Gower Street WC1E 6BT, London 3: Faculty of Geosciences, Utrecht University, PO Box 80115, 3508 TC Utrecht, The Netherlands 4: Faculty of Geosciences, Utrecht University, Heidelberglaan 2, Room 621, 3584 CS Utrecht, The Netherlands

Publication date: January 2, 2016

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