Urban function areas can be differentiated either by their external physical characteristics or by inherent social functions. And, they have been keeping in dynamic process over time. Remote sensing, as a typical traditional method in urban function area classification, has its critical defects such as high time cost and helpless in their social functions. In order to solve the problem, a new urban functional area identification method based on Call Detail Record (CDR) data was proposed. The application of this new data source in urban land use classification was verified as follow steps. First, communication station cells were labeled with five categories (residence area, office area, commercial area, college area, scenic-spot area). Second, call duration distribution features and move-frequency features, extracted from these five urban function areas were compared and analyzed. Finally, a weighted decision algorithm based on the Gaussian Mixture Model (GMM) was designed, and the simulation on the training set was conducted. The experimental results prove that the CDR data is capable of delivering useful information between different urban function areas. There are corresponding relationships between the nature of urban functional areas and the behavior characteristics of mobile phone users. When decision weight is 0.6, the weighted decision algorithm achieves 51.08% recall rate in current datasets. Combined with the error analysis, this work indicates the feasibility of CDR data in solving the problem of urban functional area identification.

Since recent studies on users' mobility based on Call Detail Record (CDR) mainly use metrics in one-dimensional space, such as the travel distance and the radius of gyration which can not exactly describe the scope of users' mobility, the Area of the Convex Hull Covering a user's daily Trajectory (ACHCT) was applied to investigate users' mobility scale in two-dimensional space, and the mobility vector was introduced to study the mobility of the crowd. Firstly, a method was designed to set up two-dimensional Cartesian coordinates based on latitude and longitude coordinates. The method applied the Mercator projection and the haversine formula to calculate the bearing and distance between scattering points, based on which the coordinates of points in the plane coordinates were determined. Then, based on the coordinates, the convex hulls covering users' daily trajectories were calculated and the distribution of the areas of all convex hulls was analyzed. Finally, the mobility vectors of agglomerated de-identified callers were accumulated respectively in different time segments and the changes in a day were analyzed. The experimental results show that, within the scale of 180 km, the average deviations of bearing angle and distance calculated with the new coordinates are 0.037° and 0.102%, compared with those calculated with Mercator projection and haversine formula. The new coordinates can maintain the distance and bearing between points well. ACHCT follows a power-law distribution and has a strong correlation with the travel distance. The changes of the crowd's mobility vector, show the tidal phenomenon of the crowd's travel and give a new sight to discover the correlation between areas where users reside and those nearby.