## 3 Materials and Methods  
### Data Description  
With the development of technology and the expansion of telecom market, mobile data detection system can collect network signaling and user's service information extraction and processing data mining and analysis provide operators with a very valuable data model. In the current 5G commercialization era, facing the challenge of massive data, mobile data detection system needs to be able to collect data properly to meet the service response requirements and the application requirements of smart city. Data acquisition has always been a means for operators to effectively optimize the network. Through optimization, key indicators that directly affect users' subjective experience such as connection rate, call drop rate and voice quality can be improved, so as to provide users with reliable, stable and high-quality network services for quality of life changes in smart city.  
When mobile phone and the base station conduct service, multiple types of mobile communication data with positioning information will be generated. These data include both event records and service records. In cellular networks, the event types of mobile communication data mainly include receiving calls, sending and receiving short messages, connection of data links and so on. This information will be recorded with the user's use and every communication between the mobile phone and the base station, and the data will be directly accessed to the server by base station. These data are stored in the base station and are usually summarized and counted every hour. In this paper, 100 days of data are used for method validation. The data structure of cellular network was shown in Table 1. Real wireless network traffic was collected at 22 base stations in an urban area. The traffic data analyzed in this paper is downlink traffic of the base station. According to the longitude and latitude information of base stations, combined with Voronoi [26] algorithm, we can obtain the approximate coverage of the base station. Among them, the Voronoi algorithm was widely used in base station coverage area estimation [27; 28; 29]. On this basis, we applied wireless network data to the research of urban computing. The coverage of 22 base stations is shown in Figure 3. Urban population analysis based on mobile communication data is essentially a data mining process, which usually includes data collection, data preprocessing, data mining and data visualization. Proposed method for data mining in the following chapters will be introduced in detail.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Timestamp** & **eNodeB** & \begin{tabular}{c} **Average Number of** \\ **Users Conected to the** \\ **eNodeB** \\ \end{tabular} & \begin{tabular}{c} **Maximum Number of** \\ **Users Conected to the** \\ **eNodeB** \\ \end{tabular} & \begin{tabular}{c} **Maximum Number of** \\ **Users Conected to the** \\ **eNodeB** \\ \end{tabular} & \
\begin{tabular}{c} **Uplink Traffic** \\ **(GB)** \\ \end{tabular} &
\begin{tabular}{c} **Downlink Traffic** \\ **(GB)** \\ \end{tabular} \\ \hline
2019/7/1 0:00 & **1** & 77.872 & 145 & 0.8802 & 5.5389 \\ \hline
2019/7/1 10:00 & **1** & 67.8724 & 124 & 0.7015 & 4.0745 \\ \hline  & &... & & & \\ \hline
2019/7/1 22:00