Understanding correlation between offline mobility and online browsing tendency in mobile network

Abstract

In recent years, the explosion of mobile Internet traffic data not only brings a high commercial value, but also helps us get a better understanding of human behavior. The study of human mobility which aims at revealing the general laws of human movement is the basis of many social, economic, and technological phenomena. It has received wide attention. But at present, the researches on the relationship between user offline and online behavior is very limited, only stop at discovering that users' current locations impact their application access behavior. This paper attempts to explore the relationship between human offline mobility and online browsing tendency from a more general point of view. To achieve this goal, we need to address several challenges including the effect of data size on the credibility of experimental results, urban functional regions identification, the modeling of user offline mobility and online browsing tendency. In this paper, our dataset consists of 7 days real mobile Internet traffic in a northern city of China, which covers 181873 users' traffic logs. In addition, we improve the existing urban functional regions identification method, and propose a new spatio-temporal-based user mobility model — user mobility image. Finally we analyze the correlation between user offline behavior and online browsing tendency. The result of this work paves the way for the identification of urban functional regions based on base station patterns. Compared with spatial-based mobility model, proposed user mobility image is more effective in illustrating user offline mobility, and shows high accuracy when predicting online browsing tendency.