Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories

Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places, and could be applied as essential resources to various downstream tasks including land use classification and human mobility prediction. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution could degrade the quality of embeddings due to data sparsity, especially in less populated areas. Our proposed method addresses this issue by leveraging the hierarchical nature of spatial information, according to the local density of observed data points. We evaluated the effectiveness of our fine grained place embeddings via next place prediction tasks using real world trajectory data from 3 cities in Japan, and compared it with non-hierarchical baseline methods. Our technique of incorporating spatial hierarchical structure can complement and reinforce various other geospatial models using place embedding generation methods.