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Data generated on location-based social networks provide rich information onthe whereabouts of urban dwellers. Specifically, such data reveal who spendstime where, when, and on what type of activity (e.g., shopping at a mall, ordining at a restaurant). That information can, in turn, be used to describecity regions in terms of activity that takes place therein. For example, thedata might reveal that citizens visit one region mainly for shopping in themorning, while another for dining in the evening. Furthermore, once such adescription is available, one can ask more elaborate questions: What are thefeatures that distinguish one region from another -- is it simply the type ofvenues they host or is it the visitors they attract? What regions are similaracross cities?  In this paper, we attempt to answer these questions using publicly sharedFoursquare data. In contrast with previous work, our method makes use of aprobabilistic model with minimal assumptions about the data and thus relievesus from having to make arbitrary decisions in our analysis (e.g., regarding thegranularity of discovered regions or the importance of different features). Weperform an empirical comparison with previous work and discuss insightsobtained through our findings.

Extracting Patterns of Urban Activity from Geotagged Social Data