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Information 2017, 8(3), 74; doi:10.3390/info8030074

Discovering and Understanding City Events with Big Data: The Case of Rome

Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR), 56100 Pisa, Italy
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Received: 31 March 2017 / Revised: 21 June 2017 / Accepted: 25 June 2017 / Published: 27 June 2017
(This article belongs to the Special Issue Big Data Analytics and Data-Driven Science)

Abstract

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. View Full-Text
Keywords: city events detection; big data analytics; distributed systems; sociometer; mobile phone data; case of study; Rome city events detection; big data analytics; distributed systems; sociometer; mobile phone data; case of study; Rome
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Furletti, B.; Trasarti, R.; Cintia, P.; Gabrielli, L. Discovering and Understanding City Events with Big Data: The Case of Rome. Information 2017, 8, 74.

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