Research on urban space and value based on big data analysis of mobile communication

With the rapid development of deep learning, more and more communication data are used to analyze the population and cities. The analysis of residents' activities and urban functional areas is an important foundation for urban planning and land value analysis and other related topics. This study starts from mobile communication big data and traffic data to discover the relationship between traffic, urban functional areas and land value. By studying: 1) OD data analysis based on signaling data to characterize the distribution of residents; 2) unsupervised K-Means clustering algorithm to identify urban functional areas; 3) finally discussing the components of traffic, urban functional areas, and land value, combined with the visualization technology of the study of urban functional areas. The research results of this paper can play a role in decision support for future urban planning and have some practical significance.