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.
Highlights
Introduction and ContextMobile devices are nowadays one of the main means by which people disseminate digital tracks of their everyday activities: trips, purchase transactions, preferences, opinions, and so on
Given a big dataset of mobile phone records (Call Detail Records—CDRs) covering six months of observations over one of the Italian biggest and touristic cities, we focus on the problem of identifying and isolating “important” events
Even if the normalization is applied, the effect of a massive presence of people in these two days is predominant w.r.t the other minor public gathering. With these examples we wanted to highlight the fact that, thanks to the Sociometer, it is possible to discover events and characterize them in detail, as well as to understand their influence on people’s composition in the city. In this case we focused on famous point of interest (POI) and events in order to assess the validity of the results, it has to be clear that the same process may be used to spot unknown events and it can be extensively applied on different locations or POIs of a city
Summary
Mobile devices are nowadays one of the main means by which people disseminate digital tracks of their everyday activities: trips, purchase transactions, preferences, opinions, and so on. A hot topic in the modernization of official statistics is precisely how to use big data in combination with traditional data sources, in order to improve quality, timeliness and spatio-temporal granularity of statistical information [7] Despite their limits in spatial precision compared to other location data such as GPS tracks, mobile phone data are of uttermost interest due to their global availability for any countries, and the ability to portray mobility independently from the transportation means. Users categorization represents a further semantic layer that enrich the information provided by each single CDR, introducing a novel approach w.r.t. to [14,15] The former proposes a method to detect unusual events relying on users’ mobility profile, considering each antenna the user connected to as a location.
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