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In conventional cellular networks, for base stations (BSs) that are deployed far away from each other, it is general to assume them to be mutually independent. Nevertheless, after long-term evolution of cellular networks in various generations, the assumption no longer holds. Instead, the BSs, which seem to be gradually deployed by operators in a casual manner, have embedded many fundamental features in their locations, coverage and traffic loading. Their features can be leveraged to analyze the intrinstic pattern in BSs and even human community. According to large-scale measurement datasets, we build spatial correlation model of BSs by utilizing one of the most important features, that is, traffic. Coupling with the theory of complex networks, we make further analysis on the structure and characteristics of this spatial correlation model. Simulation results show that its degree distribution follows scale-free property. Moreover, the datasets also unveil the characteristics of fractality and small-world. Furthermore, we study how to choose the appropriate metric to measure the importance of each BS with a combination of network efficiency and demonstrate that degree is relatively more important.