From tweets to semantic trajectories: mining anomalous urban mobility patterns (Contributo in volume (capitolo o saggio))

Type
Label
  • From tweets to semantic trajectories: mining anomalous urban mobility patterns (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-04178-0_3 (literal)
Alternative label
  • Gabrielli L., Rinzivillo S., Ronzano F., Villatoro D. (2014)
    From tweets to semantic trajectories: mining anomalous urban mobility patterns
    Springer, London (Regno Unito) in Citizen in Sensor Networks, 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gabrielli L., Rinzivillo S., Ronzano F., Villatoro D. (literal)
Pagina inizio
  • 26 (literal)
Pagina fine
  • 35 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Grant agreement: 270833 Tipo Progetto: EU_FP7 Citizen in Sensor Networks. Second International Workshop, CitiSens 2013 (Barcelona, Spain, September 19, 2013). Revised Selected Papers (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007/978-3-319-04178-0_3 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Citizen in Sensor Networks (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 8313 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Barcelona Digital Technology Centre, Spain; Barcelona Digital Technology Centre, Spain; (literal)
Titolo
  • From tweets to semantic trajectories: mining anomalous urban mobility patterns (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-319-04177-3 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Jordi Nin, Daniel Villatoro (literal)
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 res- ident 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 net- work analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their varia- tions 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. (literal)
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