With the rapid development of urbane-centered economy, urban area has gone through strong but heterogeneous sprawl. In such complex urban systems, it is impossible to established teaching centers of night school in every district of city for continuing education programs. Part-time students tend to be educated in popular locations of city due to convenience. Since call logs and geographical nature of mobile phone data can provide an opportunity to measure human behavior and social dynamics, we investigate how to infer urban popular locations with large-scale quasi-social network for avoiding the limitation of data collection and even privacy problems. A large-scale quasi-social network model is developed via measuring the number of shared-user between zones, which is different from previous models for social network. We first verify whether or not this model also can show the social structure of given data, the ranking of places in the model have been calculated based on eigvalue metric. To understand the connections between popular locations of human activity and spatial structure, we present a method to infer the core zones in given region, and then we use a simple metric to evaluate the most popular locations of human activity.
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