Modeling Urban Behavior by Mining Geotagged Social Data

Çelikten, Emre; Le Falher, Géraud; Mathioudakis, Michael

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1109/TBDATA.2016.2628398

Title :

Modeling Urban Behavior by Mining Geotagged Social Data

Author(s) :

Çelikten, Emre [Auteur]
Department of Information and Computer Science
Le Falher, Géraud [Auteur]
Machine Learning in Information Networks [MAGNET]
Mathioudakis, Michael [Auteur]
Helsinki Institute for Information Technology [HIIT]
Department of Information and Computer Science

Journal title :

IEEE Transactions on Big Data

Pages :

Publisher :

IEEE

Publication date :

2016-12-01

ISSN :

2332-7790

English keyword(s) :

User Activity
Urban Computing
Location Based Social Network
Cities

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

Data generated on location-based social networks provide rich information on the whereabouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a ...
Show more >Data generated on location-based social networks provide rich information on the whereabouts 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, 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. As another example, one might ask which regions are similar across cities. In this paper, we present a method to answer such questions using publicly shared Foursquare data. Our analysis makes use of a probabilistic model, the features of which include the exact location of activity, the users who participate in the activity, as well as the time of the day and day of week the activity takes place. Compared to previous approaches to similar tasks, our probabilistic modeling approach allows us to make minimal assumptions about the data – which relieves us from having to set arbitrary parameters in our analysis (e.g., regarding the granularity of discovered regions or the importance of different features). We demonstrate how the model learned with our method can be used to identify the most likely and distinctive features of a geographical area, quantify the importance features used in the model, and discover similar regions across different cities. Finally, we perform an empirical comparison with previous work and discuss insights obtained through our findings.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

European Project :

SoBigData Research Infrastructure

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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