Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs

Abstract

GPS coordinates and other location indicators are fine-grained location indicators that are difficult to be effectively utilized by machine learning models in Geo-aware applications. Previous location embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of the entire cities, existing approaches always suffer from extensive computational cost and significant information loss. Increasing amount of location based service (LBS) data are being accumulated and released to public and enables us to study the urban dynamics and human mobility. This study learns vector representations for locations using the large-scale LBS data. Different from existing studies, we propose to consider both spatial connection and human mobility, and jointly learn the representations from a flow graph and a spatial graph through a GCN aided skip-gram model named GCN-L2V. This model embeds context information in human mobility and spatial information. By doing so, GCN-L2V is able to capture relationships among locations, and provide a better notion of semantic similarity in a spatial environment. Across quantitative experiments and case study, we empirically demonstrate that the representations learned by GCN-L2V are effective. GCN-L2V can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.