Exploiting foursquare and cellular data to infer user activity in urban environments

Proceedings - IEEE International Conference on Mobile Data Management

Volumn 1, Issue , 2013, Pages 167-176

  Noulas, Anastasios ^a   Mascolo, Cecilia ^a   Frias Martinez, Enrique ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b TELEFONICA RESEARCH   (Spain)

Author keywords

cellular data; location based services; urban mining

Indexed keywords

ACTIVITY INFERENCE; APPLICATION SCENARIO; BETTER PERFORMANCE; CELLULAR DATA; MOBILE PHONE USAGES; TELECOMMUNICATION PROVIDERS; TELECOMMUNICATION SIGNALS; USER COMMUNICATION;

FORECASTING; INFORMATION MANAGEMENT; LEARNING ALGORITHMS; LOCATION BASED SERVICES; SUPERVISED LEARNING;

URBAN PLANNING;

EID: 84883524144     PISSN: 15516245     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MDM.2013.27     Document Type: Conference Paper

References (31)

1. CASA Smart Cities: bridging physical and digital. http://www.bartlett. ucl.ac.uk/casa/events/2012-04-20-Conference.
2. European Smart Cities. http://www.smart-cities.eu/.
3. Foursquare application. http://www.foursquare.com/.
4. Foursquare Venue Categories. http://aboutfoursquare.com/foursquare- categories/.
5. IBM Smart Cities. http://www.ibm.com/smarterplanet/us/en/smarter cities/overview/index.html.
6. London to test 'smart city' operating system. http://www.bbc.co.uk/news/ technology-17940797.
7. MIT Smart Cities. http://cities.media.mit.edu/.
8. Smart Santander. http://www.smartsantander.eu/.
9. A density-based algorithm for discovering clusters in large spatial databases with noise, 1996.
10. C.-C. Chang and C.-J. Lin. Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3):27:1-27:27, May 2011.
11. D. J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world's photos. In Proceedings of the 18th international conference on World wide web, WWW '09, pages 761-770, 2009.
12. Facebook. Building better stories with location and friends. http://developers.facebook.com/blog/post/2012/03/07/building-better-storieswith- location-and-friends, March 2012.
13. T. Fujisaka, R. Lee, and K. Sumiya. Exploring urban characteristics using movement history of mass mobile microbloggers. In Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications, pages 13-18. ACM, 2010.
14. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1):10-18, Nov. 2009.
15. T. Horanont and R. Shibasaki. Evolution of urban activities and land use classification through mobile phone and gis analysis. In CUPUM, 2009.
16. Isaacman, S. and Becker, R. and Ćaceres, R. and Kobourov, S. and Rowland, J. and Varshavsky, A. A tale of two cities . In Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications, pages 19-24, 2010.
17. S. Kinsella, V. Murdock, and N. Oare. I am eating a sandwich in glasgow modeling locations with tweets. In Proc. of the 3rd Workshop on Search and Mining User-generated Contents, Glasgow, UK, 2011.
18. N. Landwehr, M. Hall, and E. Frank. Logistic model trees. Mach. Learn., 59(1-2):161-205, May 2005.
19. N. Lathia, S. Ahmed, and L. Capra. Measuring the impact of opening the London shared bicycle scheme to casual users. 22, 2012.
20. N. Lathia and L. Capra. Mining mobility data to minimise travellers' spending on public transport. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pages 1181-1189, New York, NY, USA, 2011. ACM.
21. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. In 3rd Workshop Social Mobile Web (SMW 2011).
22. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An empirical study of geographic user activity patterns in foursquare. ICWSM2011, 2011.
23. C. Ratti, R. M. Pulselli, S. Williams, and D. Frenchman. Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5):727-748, 2006.
24. J. Reades, F. Calabrese, A. Sevtsuk, and C. Ratti. Cellular census: Explorations in urban data collection. IEEE Pervasive Computing, 6(3):30-38, 2007.
25. F. Rosenblatt. Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. 1961.
26. C. Roth, S. M. Kang, M. Batty, and M. Barthlemy. Structure of urban movements: Polycentric activity and entangled hierarchical flows. PLoS ONE, 6(1):8, 2011.
27. V. Soto and E. Frias-Martinez. Robust land use characterization of urban lanscapes using cell phone data. In 1st Workshop on Pervasive Urban Applications, in conjunction with 9th Int. Conf. Pervasive Computing, 2011.
28. J. Su, H. Zhang, C. X. Ling, and S. Matwin. Discriminative parameter learning for bayesian networks. In Proceedings of the 25th international conference on Machine learning, ICML '08, pages 1016-1023, New York, NY, USA, 2008. ACM.
29. Techcrunch. Facebook has acquired gowalla. http://techcrunch.com/2011/12/ 02/report-facebook-has-acquired-gowalla/, December 2011.
30. The Next Web. Foursquare hits 20 millions users and 2 billion check-ins. http://thenextweb.com/socialmedia/2012/04/16/foursquare-hits-20-million-users, April 2012.
31. S. Wakamiya, R. Lee, and K. Sumiya. Urban area characterization based on semantics of crowd activities in twitter. In C. Claramunt, S. Levashkin, and M. Bertolotto, editors, GeoSpatial Semantics, volume 6631 of Lecture Notes in Computer Science, pages 108-123. Springer Berlin/Heidelberg, 2011.