Clustering reveals key behaviours driving human movement network structure
Gibbs, H.; Eggo, R. M.; Cheshire, J.
Show abstract
A commonly-used form of human mobility data, called in-app mobility data, is based on GPS locations collected from a panel of mobile applications. In this paper, we analysed daily travel patterns from in-app GPS data in the United Kingdom to identify characteristic modes of travel behaviour, and assessed whether certain behavioural modes were more common among users of different groups of mobile applications. We also explored the relative importance of different mobility behaviours for the topology of an aggregated travel network. Our findings point to the presence of behavioural bias in in-app mobility data driven by the interaction between mobile device users and specific mobile applications. Our study also presents a general methodology for detecting behavioural bias in in-app mobility data, allowing for greater transparency into the characteristics of in-app mobility datasets without risking individual privacy or identifying specific mobile applications underlying a given dataset. Overall, the analysis highlights the need to understand the process of data generation for in-app mobility data, and the way that this process can bias the collective dynamics reported in aggregate mobility data.
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