Abstract
Can we automatically identify relevant places and events happening in the city from the analysis of mobile network use? In this paper we present a methodology to discover events from human mobility patterns as recorded by mobile network usage. Experiments conducted over an extensive dataset from the main Italian telecom operator show that the proposed approach is effective and can be applied to a number of different scenarios. These results can have a strong impact on a wide range of pervasive applications ranging from location-based services to urban planning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Report Agcom 2011, http://www.agcom.it.
Report Telecom Italia 2011 http://www.telecomitalia.com.
References
Andrienko G, Andrienko N, Mladenov M, Mock M, Poelitz C (2012) Identifying place histories from activity traces with an eye to parameter impact. IEEE Trans Visual Comput Graph 18(5):675–688
Calabrese F, Colonna M, Lovisolo P, Parata D, Ratti C (2011) Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans Intell Transport Syst 12:141–151
Calabrese F, Reades J, Ratti C (2010) Eigenplaces: analysing cities using the space-time structure of the mobile phone network. IEEE Pervasive Comput 9(1):78–84
Candia J, Gonzalez M, Wang P, Schoenharl T, Madey G, Barabasi A (2008) Uncovering individual and collective human dynamics from mobile phone records. J Phys A Math Theor 41(22):1–11
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):58
Ferrari L, Rosi A, Mamei M, Zambonelli F (2011) Extracting urban patterns from location-based social networks. ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN)
Lee R., Wakamiya S., Sumiya K. (2011) Discovery of unusual regional social activities using geo-tagged microblogs. The World Wide Web J Special Issue Mobile Serv Web 14(4):321–349
Mamei M, Rosi A, Zambonelli F (2010) Automatic analysis of geotagged photos for intelligent tourist services. In: IEEE intelligent environment, Kuala Lumpur, Malaysia
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet 9:62–66
Pedersen T, Jensen C (2001) Multidimensional database technology. IEEE Comput 34(12):40–46
Rattenbury T, Good N, Naaman M (2007) Towards automatic extraction of event and place semantics from flickr tags. In: International ACM SIGIR conference on research and development in information retrieval, Amsterdam, The Netherlands
Ratti C, Pulselli RM, Williams S, Frenchman D (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plann B: Plann Design 33(5):727–748
Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: real-time event detection by social sensors. In: International conference on World Wide Web
Vieira M, Frias-Martinez V, Oliver N, Frias-Martinez E (2010) Characterizing dense urban areas from mobile phone-call data: discovery and social dynamics. In: International Conference on Social Computing (SocialCom), pp 241–248
Wilcox R (2012) Introduction to robust estimation and hypothesis testing, statistical modeling and decision science, 3rd edn. Elsevier, Amsterdam
Acknowledgments
Work supported by the SAPERE (Self-Aware Pervasive Service Ecosystems) project (EU FP7-FET, Contract No. 256873) and by the project Mr.Typ—Mobile and Real-Time Yellow Pages, funded by Telecom Italia.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ferrari, L., Mamei, M. & Colonna, M. Discovering events in the city via mobile network analysis. J Ambient Intell Human Comput 5, 265–277 (2014). https://doi.org/10.1007/s12652-012-0169-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-012-0169-0