Abstract
Tourists are an important asset for the economy of the regions they visit. The answer to the question “where do tourists actually go?” could be really useful for public administrators and local governments. In particular, they need to understand what tourists actually visit, where they actually spend nights, and so on and so forth.
In this paper, we introduce an original approach that exploits geo-located messages posted by Twitter users through their smartphones when they travel. Tools developed within the FollowMe suite track movements of Twitter users that post tweets in an airport and reconstruct their trips within an observed area. To illustrate the potentiality of our method, we present a simple case study in which trips are traced on the map (through KML layers shown in Google Earth) based on different analysis dimensions.
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References
Bora, N., Chang, Y.-H., Maheswaran, R.: Mobility patterns and user dynamics in racially segregated geographies of US cities. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds.) SBP 2014. LNCS, vol. 8393, pp. 11–18. Springer, Heidelberg (2014)
Cuzzocrea, A.: Analytics over big data: Exploring the convergence of datawarehousing, OLAP and data-intensive cloud infrastructures. In: 37th Annual IEEE Computer Software and Applications Conference, COMPSAC 2013, Kyoto, Japan, 22–26 July 2013, pp. 481–483 (2013)
Cuzzocrea, A.: Big data mining or turning data mining into predictive analytics from large-scale 3Vs data: the future challenge for knowledge discovery. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds.) MEDI 2014. LNCS, vol. 8748, pp. 4–8. Springer, Heidelberg (2014)
Cuzzocrea, A., Bellatreche, L., Song, I.-Y.: Data warehousing and OLAP over big data: current challenges and future research directions. In: Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP, DOLAP 2013, San Francisco, CA, USA, 28 October 2013, pp. 67–70 (2013)
Cuzzocrea, A., Saccà, D., Ullman, J.D.: Big data: a research agenda. In: 17th International Database Engineering & Applications Symposium, IDEAS 2013, Barcelona, Spain, 09–11 October 2013, pp. 198–203 (2013)
Cuzzocrea, A., Song, I.-Y.: Big graph analytics: The state of the art and future research agenda. In: Proceedings of the 17th International Workshop on Data Warehousing and OLAP, DOLAP 2014, Shanghai, China, 3–7 November 2014, pp. 99–101 (2014)
Grabovitch, I., Kanza, Y., Kravi, E., Pat, B.: On the correlation between textual content and geospatial locations in microblogs. In: GeoRich 2014, Snowbird, Utah (USA), 23 June 2014, June 2014
Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., Ratti, C.: Geo-located twitter as proxy for global mobility patterns. Cartography Geogr. Inf. Sci. 41(1), 260–271 (2014)
Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In: ACM LBSN 2010, San Jose, CA, (USA), November 2010
Stephens, M., Poorthuis, A.: Follow thy neighbor: connecting the social and the spatial networks on Twitter. Comput. Environ. Urban Syst. 41(1) (2014). doi:10.1016/j.compenvurbsys.2014.07.002
Walther, M., Kaisser, M.: Geo-spatial event detection in the twitter stream. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 356–367. Springer, Heidelberg (2013)
Widener, M., Li, W.: Using geolocated twitter data to monitor the prevalence of healthy and unhealthy food references across the us. Appl. Geogr. 54, 189–197 (2014)
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Cuzzocrea, A., Psaila, G., Toccu, M. (2015). Knowledge Discovery from Geo-Located Tweets for Supporting Advanced Big Data Analytics: A Real-Life Experience. In: Bellatreche, L., Manolopoulos, Y. (eds) Model and Data Engineering. Lecture Notes in Computer Science(), vol 9344. Springer, Cham. https://doi.org/10.1007/978-3-319-23781-7_23
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DOI: https://doi.org/10.1007/978-3-319-23781-7_23
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