Understanding cycling behaviour through visual analysis of a large-scale observational dataset

Beecham, R. (2014). Understanding cycling behaviour through visual analysis of a large-scale observational dataset. (Unpublished Doctoral thesis, City University London)

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Abstract

The emergence of third-generation, technology-based public bikeshare schemes offers new opportunities for researching cycling behaviour. In this study, data from one such scheme, the London Cycle Hire Scheme (LCHS), are analysed. Algorithms are developed for summarising and labelling cyclists’ usage behaviours and tailored visual analysis applications are designed for exploring their spatiotemporal context.

Many of the research findings provide support to existing literature, particularly around gendered cycling behaviour. As well as making more discretionary journeys, women appear to preferentially select parts of London associated with greater levels of safety; and this is true even after controlling for geodemographic differences and levels of LCHS cycling experience. One hypothesis is that these differences represent diverging attitudes and perceptions. After developing a technique for identifying cyclists’ workplaces, these differences might also be explained by where cyclists need to travel for work and other facilities. An additional explanation is later offered that relates to the nature of cyclists’ estimated routes. The size and precision of the LCHS dataset allows under-explored aspects of behaviour to be investigated. Group cycling events – instances where two or more cyclists make journeys together in space and time – are labelled and analysed on a large scale. For certain types of cyclist, group cycling appears to encourage more extensive spatiotemporal cycling behaviour and there is some evidence to suggest that group cycling may help initiate scheme usage.

The domain-specific findings, emerging research questions and also behavioural classifications are this study’s principal and unique contribution. A second contribution relates to the analysis approach. This is a data-driven study that takes a large dataset, measuring use of a relatively new cycle facility, and uses it to engage with research questions that are typically answered with very different datasets. There is some uncertainty around how discriminating and generalisable LCHS cycle behaviours may be and which variables, either directly measured or derived, might delineate those behaviours. Visual analysis techniques are shown to be effective in this more speculative research context: numerous behaviours are very quickly explored and understood. These techniques also enable a set of colleagues with relatively limited analysis experience, but substantial domain knowledge, to participate in the analysis and a general argument is made for their use in other, interdisciplinary analysis contexts.

Item Type: Thesis (Doctoral)
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: City University London PhD theses
School of Informatics > giCentre
URI: http://openaccess.city.ac.uk/id/eprint/4770

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