Estimating human trajectories and hotspots through mobile phone data

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding is that trajectory estimation methods show different error regimes whether used within or outside the ''territory'' of the user defined by the radius of gyration. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions (''hotspots'') with a median error lower than 7%.