Dynamic Identification of Urban Functional Areas and Visual Analysis of Time-varying Patterns Based on Trajectory Data and POIs

At present, most of the methods for urban functional areas identification are based on the road net- work and the types of land utilization, and cannot reflect the dynamic changes of the coverage areas and the functionalities of functional areas, accompanying with the changes of human activities. In this paper, we pro- pose a method to identify the urban regions and analyze their spatial-temporal features based on trajectory data mining and POIs （point of interest） semantic analysis. Through taking the correlation between vehicle running conditions and functions of regions into account, the characteristic points in trajectory data are clustered adap- tively based on their densities. The functional areas are divided reasonably through building Voronoi diagrams based on the cluster centers. In order to effectively evaluate the compositional functions of the regions, the topic words are mined and the corresponding probabilities are calculated based on the POIs＇ categories in each region, using LDA （latent Dirichlet allocation） topic model. Furthermore, we propose a quantifiable method of computing the function strength based on the results of LDA. Moreover, based on the time-variant characteris- tics of trajectory data, an interactive visual analysis system called UFAVIS （urban functional areas visualization） is constructed to explore the impact of human activities on the spatial-temporal patterns of the functional areas. Using the method of functional area recognition with spatial-temporal feature analysis, experimental verifica- tions and multiple case studies of the real data in Beijing are performed. The results demonstrate that UFAVIS can effectively identify the compositional functions of urban areas and find their spatial-temporal patterns changing with the variations of human activities, which provides guidance for urban planning and policy deci- sion.