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As social networks become increasingly integrated with their users' daily lives, and users are willing to publicly share data about their offline activities on these networks, the resultant data offers a powerful tool to non-intrusively understand city dynamics as it captures human behaviour and interactions. In this paper, we derive lifestyle patterns from the Foursquare social network data, using matrix factorization and tensor decomposition as unsupervised methods to extract latent spatio-temporal behavior patterns. The extracted patterns offer precise definition of activity levels associated with specific lifestyles and showcase that users' behaviors are a combination of several lifestyles, in contrast to traditional circadian topology theory which classifies individuals to a specific temporal pattern. The obtained patterns can provide deeper insights into city dynamics, the people within them and how society functions.
With cycling moving from being a pastime and sport to a mainstream form of mobility and transport, bike sharing systems (BSS) are increasingly being deployed in many cities. Analysis of the BSS usage data can provide insights into factors that shape the patterns of trips, uncovering latent city dynamics. A Poisson mixture model is proposed to cluster the stations according to their usage profiles and reveal latent links between the social and economic activities of BSS station neighbourhood type and the generated mobility patterns. It reveals the varying functions of different urban areas that induce specific bike trip patterns. Pairwise clustering of bike station with appreciable trip activity between them further advance the understanding of urban neighbourhoods with the strongest mobility patterns. The results are showcased through an analysis of 15 million bike journeys of the London Santander Cycles BSS over a 3-year period.
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