{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T12:31:29Z","timestamp":1763037089136,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,6,27]],"date-time":"2017-06-27T00:00:00Z","timestamp":1498521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists.<\/jats:p>","DOI":"10.3390\/info8030074","type":"journal-article","created":{"date-parts":[[2017,6,28]],"date-time":"2017-06-28T10:25:56Z","timestamp":1498645556000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Discovering and Understanding City Events with Big Data: The Case of Rome"],"prefix":"10.3390","volume":"8","author":[{"given":"Barbara","family":"Furletti","sequence":"first","affiliation":[{"name":"Istituto di Scienza e Tecnologie dell\u2019Informazione, National Research Council of Italy (ISTI-CNR), 56100 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Trasarti","sequence":"additional","affiliation":[{"name":"Istituto di Scienza e Tecnologie dell\u2019Informazione, National Research Council of Italy (ISTI-CNR), 56100 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Cintia","sequence":"additional","affiliation":[{"name":"Istituto di Scienza e Tecnologie dell\u2019Informazione, National Research Council of Italy (ISTI-CNR), 56100 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Gabrielli","sequence":"additional","affiliation":[{"name":"Istituto di Scienza e Tecnologie dell\u2019Informazione, National Research Council of Italy (ISTI-CNR), 56100 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lane, N.D., Miluzzo, E., Hong, L., Peebles, D., Choudhury, T., and Campbell, A.T. (2010). A survey of mobile phone sensing. IEEE Commun. Mag., 48.","DOI":"10.1109\/MCOM.2010.5560598"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Quercia, D., Lathia, N., Calabrese, F., Lorenzo, G.D., and Crowcroft, J. (2010, January 13\u201317). Recommending Social Events from Mobile Phone Location Data. Proceedings of the IEEE 10th International Conference on Data Mining (ICDM), Sydney, Australia.","DOI":"10.1109\/ICDM.2010.152"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TITS.2010.2074196","article-title":"Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome","volume":"12","author":"Calabrese","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15274","DOI":"10.1073\/pnas.0900282106","article-title":"Inferring friendship network structure by using mobile phone data","volume":"106","author":"Eagle","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, D., Pedreschi, D., Song, C., Giannotti, F., and Barabasi, A.L. (2011, January 21\u201324). Human mobility, social ties, and link prediction. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.","DOI":"10.1145\/2020408.2020581"},{"key":"ref_6","unstructured":"Furletti, B., Gabrielli, L., Garofalo, G., Giannotti, F., Milli, L., Nanni, M., Pedreschi, D., and Vivio, R. (2014, January 11\u201313). Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach. Proceedings of the 47th Meeting of the Italian Statistical Society (SIS 2014), Cagliari, Italy."},{"key":"ref_7","unstructured":"Barcaroli, G., De Francisci, S., Scannapieco, M., and Summa, D. (2014, January 14\u201316). Dealing with Big data for Official Statistics: IT Issues. 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Mobile Landscapes: Graz in Real Time, Springer."},{"key":"ref_11","first-page":"25","article-title":"Urban Sensing Using Mobile Phone Network Data: A Survey of Research","volume":"47","author":"Calabrese","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.atmosenv.2016.02.011","article-title":"A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic","volume":"131","author":"Gariazzo","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Furletti, B., Gabrielli, L., Trasarti, R., Giannotti, F., and Pedreschi, D. (November, January 29). City users\u2019 classification with mobile phone data. Proceedings of the 2015 IEEE International Conference on Big Data, Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7363852"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1140\/epjds\/s13688-016-0086-0","article-title":"Countrywide arrhythmia: Emergency event detection using mobile phone data","volume":"5","author":"Gundogdu","year":"2016","journal-title":"EPJ Data Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., and Soares, C. (2015). Inferring Unusual Crowd Events from Mobile Phone Call Detail Records. Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference (ECML PKDD 2015), Porto, Portugal, 7\u201311 September 2015, Springer.","DOI":"10.1007\/978-3-319-23528-8"},{"key":"ref_16","unstructured":"Giulio Barcaroli, P.R. (2017, January 01). Big Data in Official Statistics: Ongoing Work at Statistics Italy. 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