Abstract
This paper proposes and experiments new techniques to detect urban mobility patterns and anomalies by analyzing trajectories mined from publicly available geo-positioned social media traces left by the citizens (namely Twitter). By collecting a large set of geo-located tweets characterizing a specific urban area over time, we semantically enrich the available tweets with information about its author – i.e. a resident or a tourist – and the purpose of the movement – i.e. the activity performed in each place.
We exploit mobility data mining techniques together with social network analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their variations over time. We apply and validate the proposed trajectory mining approaches to a large set of trajectories built from the geo-positioned tweets gathered in Barcelona during the Mobile World Congress 2012 (MWC2012), one of the greatest events that affected the city in 2012.
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Notes
- 1.
Twitter Streaming API: https://dev.twitter.com/docs/streaming-apis/parameters#locations
- 2.
DBpedia: http://dbpedia.org/
- 3.
Foursquare API: https://developer.foursquare.com/
References
Giannotti, F., Pedreschi, D., Pentland, A., Lukowicz, P., Kossmann, D., Crowley, J., Helbing, D.: A planetary nervous system for social mining and collective awareness. Eur. Phys. J. Special Topics 214(1), 49–75 (2012)
Villatoro, D., Serna, J., Rodríguez, V., Torrent-Moreno, M.: The tweetbeat of the city: microblogging used for discovering behavioural patterns during the MWC2012. In: Nin, J., Villatoro, D. (eds.) CitiSens 2012. LNCS (LNAI), vol. 7685, pp. 43–56. Springer, Heidelberg (2013)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: an analysis of a microblogging community. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Lee Giles, C., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) WebKDD/SNA-KDD 2007. LNCS, vol. 5439, pp. 118–138. Springer, Heidelberg (2009)
Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, LBSN ’10, pp. 1–10. ACM, New York (2010)
Lee, R., Wakamiya, S., Sumiya, K.: Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web 14(4), 321–349 (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 851–860. ACM, New York (2010)
Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D., Ertl, T.: Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 143–152 (2012)
Fuchs, G., Andrienko, N., Andrienko, G.: Extracting personal behavioral patterns from geo-referenced tweets. In: AGILE 2013 (2013)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., Trasarti, R.: Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J. Int. J. Very Large Data Bases 20(5), 695–719 (2011)
Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: Proceedings of the sixth ACM International Conference on Web Search and Data Mining, pp. 717–726. ACM (2013)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)
Acknowledgements
This work has been completed with the support of ACC1Ó, the Catalan Agency to promote applied research and innovation; and by the Spanish Centre for Development of Industrial Technology under the INNPRONTA program, project IPT-20111006, “CIUDAD2020”. The work was also partially supported by the EU project DATASIM N. 270833.
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Gabrielli, L., Rinzivillo, S., Ronzano, F., Villatoro, D. (2014). From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns. In: Nin, J., Villatoro, D. (eds) Citizen in Sensor Networks. CitiSens 2013. Lecture Notes in Computer Science(), vol 8313. Springer, Cham. https://doi.org/10.1007/978-3-319-04178-0_3
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DOI: https://doi.org/10.1007/978-3-319-04178-0_3
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