Location-based check-in services enable individuals to share their activity-related choices providing a new source of human activity data for researchers. In this paper urban human mobility and activity patterns are analyzed using locationbased data collected from social media applications (e.g. Foursquare and Twitter). We first characterize aggregate activity patterns by finding the distributions of different activity categories over a city geography and thus determine the purpose-specific activity distribution maps. We then characterize individual activity patterns by finding the timing distribution of visiting different places depending on activity category. We also explore the frequency of visiting a place with respect to the rank of the place in individual's visitation records and show interesting match with the results from other studies based on mobile phone data.

Abstract. The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples' visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.

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