Semantic analysis of spatial temporal trajectory in LBSNs
Haoteng Yin, Yang LIU · Scientia Sinica Informationis · 2017
Spatial temporal data has associated multidimensional features. Deep learning has attracted much attention due to its ability to perform high-level abstraction of complex data. In this paper, we give the definition of the track-data based on its characteristics, and build a spatial temporal semantic trajectory model using Word2vec as its foundation. We explore the semantics of different user-tracks under varying time periods by training position vectors in the model network. During the experiments, we use Top-$K$ neighbor prediction and cluster analysis to verify that the position vector has both good semantic meaning and structure. The vector is derived from a trajectory model that employs unsupervised learning. The results also test a word-vector-based language model that can be applied to the study of trajectory mining.