http://www.cnr.it/ontology/cnr/individuo/prodotto/ID295073
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)
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- Pagina fine
- 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
- 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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