Abstract

Modeling Urban Behavior by Mining Geotagged Social Data project report on web mining Data generated on location-based social networks provide rich information on the where abouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That information can, in turn, be used to describe city regions in terms of activity that takes place therein. For example, the data might reveal that citizens visit one region mainly for shopping in the morning, while another for dining in the evening. Furthermore, Modeling Urban Behavior by Mining Geotagged Social Data using web mining once such a description is available, one can ask more elaborate questions. For example, one might ask what features distinguish one region from another – some regions might be different in terms of the type of venues they host and others in terms of the visitors they attract.

Conclusions

In this Modeling Urban Behavior by Mining Geotagged Social Data project report on web mining work, we made use of a probabilistic model to reveal how venues are distributed in cities in terms of several features. As most habitants of a city do not visit most of the available venues, we cope with the induced sparsity by adapting the sparse modeling approach of to data at hand. This time we also benefit of the solid theoretical grounds of probabilistic models to define a principled measure of similarity and we describe a procedure to greedily find two regions maximizing measure.

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