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.

With the rapid development of urbane-centered economy, today swelling cities is being filled with massive socioeconomic activities, and urban area have gone through strong but heterogeneous sprawl. Marketer has to put more resources for the ground-based promotion of their products in such metropolis. Therefore, it is very significance to help marketer choose a number of more-suited districts in urban area for promotion. This paper investigates location selection problem of ground-based promotion in city with urban computing's perspective. In a provincial capital of China, massive call logs from millions of people have been used to abstract human mobility patterns and urban area have been segmented to hundreds of districts so that we model this problem with two essential cases and give corresponding solutions via greedy algorithms. Our theoretical analysis and experimental evolution show the algorithm is effectiveness. This application framework can be used in ground-based promotion of many application scenarios such as o2o applications that fusing of multisource mobility data.

Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset.

The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone call detail record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a call detail record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.

We propose a new measurement called geographic community, which provides a bridge between spatial proximity and the social nature of individuals in mobile social network. A novel approach for detecting these geographic communities has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used for detecting these geographic communities. Based on several experimental results, the advantages of this proposed measurement have been presented. Finally, several future directions extending from this new measurement have been discussed.