Next Article in Journal
Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
Previous Article in Journal
Deployment and Field Evaluation of In-Vehicle Traffic Signal Advisory System (ITSAS)
Open AccessArticle

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
*
Author to whom correspondence should be addressed.
Information 2017, 8(3), 74; https://doi.org/10.3390/info8030074
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop