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Understanding mobile big data, inherent within large-scale cellular towers in the urban environment, is extremely valuable for service providers, mobile users, and government managers of the modern metropolis. By extracting and modeling the mobile cellular data associated with over 9600 cellular towers deployed in a metropolitan city of China, this article aims to link cyberspace and the physical world with social ecology via such big data. We first extract a human mobility and cellular traffic consumption trace from the dataset, and then investigate human behavior in cyberspace and the physical world. Our analysis reveals that human mobility and the consumed mobile traffic have strong correlations, and both have distinct periodical patterns in the time domain. In addition, both human mobility and mobile traffic consumption are linked with social ecology, which in turn helps us to better understand human behavior. We believe that the proposed big data processing and modeling methodology, combined with the empirical analysis on mobile traffic, human mobility, and social ecology, paves the way toward a deep understanding of human behaviors in a large-scale metropolis.
Xu et al. (Sun,) studied this question.