Understanding the spatial correlation of places plays an important role in geographic information retrieval and recommendation systems, urban traffic management, and resident travel pattern exploration. In order to represent the places and their spatial relationships specifically, we propose a deep learning-based vectorization method for places. The correlation between places can be calculated by the place vectors. Firstly, the trajectories of long-distance and short-distance are matched and connected to build a large-scale traffic network, which could cover multiple travel modes and obtain a complete cognition of spatial relations. Then we propose a spatial vectorization method which is based on graph neural network and combines place features and trajectory information. Besides, we improve the representation ability of latent representations for places by optimizing a node sampling method. Finally, the empirical analysis is performed on the shared bicycle track data and public traffic data in Beijing. The result demonstrates that the proposed method outperforms the existing methods such as DeepMove on place correlation analysis and cluster analysis.