An Examination of Personal Mobility Patterns in Space and Time Using Twitter

An Examination of Personal Mobility Patterns in Space and Time Using Twitter

Mark Birkin (School of Geography, University of Leeds, Leeds, UK), Kirk Harland (School of Geography, University of Leeds, Leeds, UK), Nicolas Malleson (School of Geography, University of Leeds, Leeds, UK), Philip Cross (School of Geography, University of Leeds, Leeds, UK) and Martin Clarke (School of Geography, University of Leeds, Leeds, UK)
DOI: 10.4018/ijaeis.2014070104
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New sources of data relating to personal mobility and activity patterns are now providing a unique opportunity to explore movement patterns at increasing scales of spatial and temporal refinement. In this article, a corpus of messages from the Twitter social networking platform are examined. An elementary classification of users is proposed on the basis of frequency of use in space and time. The behaviour of different user groups is investigated across small areas in the major conurbation of Leeds. Substantial variations can be detected in the configuration of individual networks. An interpretation of the patterns which result is provided in terms of the underlying demographic structures, and the basic form and function of the urban area.
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The widespread availability of increasingly diverse and rich sources of data about the world around us has attracted significant interest in media as widespread as the daily press (Ganesh, 2014), professional magazines (Anderson, 2008) and the scientific literature (Bell et al, 2009). Claims that better understanding of social trends will follow naturally or automatically from the analysis of such data require qualification (Mayer-Schönberger and Cukier, 2013). Analytical frameworks are certainly required in which further empirical investigations can be undertaken in relation to established behavioural or theoretical frameworks. In this paper, an investigation of daily mobility patterns in a British city will be conducted using data from the Twitter social media platform.

It will be argued that established approaches are primarily geared towards the interrogation of long-term relocation patterns (i.e. migration). Short-term movement patterns have received much lower priority, and this reflects the poverty of available data more than a lack of intrinsic interest in the subject matter. This argument is amplified in Section 2 of the paper. In order to further the understanding of daily mobility patterns, the Twitter social messaging platform presents itself as a candidate. A substantial corpus of data for the city of Leeds has been extracted from Twitter, using the methods articulated in Section 3. A straightforward but robust approach in the literature to the analysis of small area variations in social and economic structures is geodemographics, which provides a framework for the classification of individual neighbourhoods, households or individuals. In Section 4 of the paper an elementary classifier is presented for application to Twitter users. The sub-division which follows can be used as a means for further exploration of mobility patterns in space and time. Some preliminary results from the application of this procedure are reported in Section 5. The paper concludes with a discussion of the value and potential in the approach, as well as a consideration of enhancements which may be necessary if more substantial insights are to be acquired.

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