Smarter Urban Dynamics - Social Event Analytics


Social Event Analytics 

Francesco Calabrese, Giusy Di Lorenzo, Gavin McArdle, Stefano Braghin, Fabio Pinelli 


Managing public safety at large events is important. Crowd control and traffic management are particularly relevant for non-ticketed events in public spaces. In such cases, it can be difficult for organisers to anticipate the number of people who will attend and to validate the event’s success. Given the ubiquitous nature of mobile phones, Call Detail Records (CDRs), which are the logs of user transactions with a mobile phone service provider, have been widely used to study urban processes. Our solution explores the use of real-time CDR data as a proxy to estimate the density of crowds in different areas of a city while events are taking place. The research has also been extended to estimate the density of vehicles on the main access routes to a city. This has led to the development of an application entitled Social Event Analytics (SEA) which provides both real-time and historic information about crowd and vehicle densities.

The solution uses advanced spatio-temporal and predictive analytics running in the cloud to process anonymized mobile phone location data as it gets generated by Mobistar. The application can be used by authorities and event organisers to manage the event and gauge its success.

The application was used in January 2015 for monitoring city wide events in Mons, Belgium which marked the launch of Mons as the European City of Culture for 2015. Using SEA local police simultaneously monitored the density of vehicles on the road network and the crowd density in different areas of the city.


Analyzing Social Events in Real-Time using Big Mobile DataGavin McArdle, Giusy Di Lorenzo, Fabio Pinelli, Francesco Calabrese, Erik Van Lierde, IEEE COMSOC MMTC E-Letter, 2015

Real-Time Social Event AnalyticsF Calabrese, G Di Lorenzo, G McArdle, F Pinelli, E Van Lierde, Proceedings of Netmob, 2015

Comparing Urban Sensing Applications using Event and Network-driven Mobile Phone Location Data, F Pinelli, G Di Lorenzo, F Calabrese16th IEEE International Conference on Mobile Data Management, 2015

Evaluating urban sensing applications using actively and passively-generated mobile phone location dataF Pinelli, G Di Lorenzo, F CalabreseProceedings of Netmob, 2015