Understanding and Visualisation of Geographic Mesh Similarity by Trajectory Data and Gaussian Process Modelling

摘要

A new concept is proposed of estimating mesh similarity based on trajectory data. The model is formulated as an unsupervisedlearning method using a type of Gaussian process on a continuous coordinate system. This allows for the features of meshes andtrajectories to be determined as the estimated latent coordinates that are different from geographic ones. The similarities ofmeshes and trajectories are represented through those of coordinates. In addition, this allows for easy visualisation. Afterintroducing the coordinate estimation method with a type of Markov Chain Monte Carlo approach, the proposed method wasverified using actual trajectory data from the city of Sendai, Japan.