Telecom data is a rich source of information for many purposes, ranging from urban planning (Toole et al., 2012), human mobility patterns (Ficek and Kencl, 2012; Gambs et al., 2011), points of interest detection (Vieira et al., 2010), epidemic spread modeling (Lima et al., 2013), community detection (Morales et al., 2013) disaster planning (Pulse, 2013) and social interactions (Eagle et al., 2013).
One common task for these applications is to identify dense areas where many users stay for a significant time (activity zones), the regions relaying theses activity zones (transit zones) as well as the interaction between identified activity zones. Thus, in the present article we will identify activity and transit zones to monitor and predict the activity levels in the telecom operators network based on the SMS and calls input/output activity levels issued from the Telecom Italia Big Data Challenge. The results of the present study could be directly applied to:
- Location-Based Advertising
- defining a suitable place to open a new store in a city
- planning where to add cell towers to improve QoS
The contribution of this work is twofold: to present a model accounting for changes of activity levels (over time) and to predict those changes using Markov chains. We also propose a methodology to detect activity and transit zones.