Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories
This work addresses a specific issue in urban computing and geographic information systems for applications like mobility analysis and land use planning, representing an incremental improvement over existing methods.
The paper tackles the problem of data sparsity in generating high-resolution place embeddings from human mobility trajectories by proposing a method that leverages spatial hierarchical information based on local data density. It demonstrates effectiveness through next place prediction tasks in three Japanese cities and shows value for land use classification applications.
Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution deteriorates the quality of embeddings due to data sparsity, especially in less populated areas. We address this issue by proposing a method that generates fine grained place embeddings, which leverages spatial hierarchical information according to the local density of observed data points. The effectiveness of our fine grained place embeddings are compared to baseline methods via next place prediction tasks using real world trajectory data from 3 cities in Japan. In addition, we demonstrate the value of our fine grained place embeddings for land use classification applications. We believe that our technique of incorporating spatial hierarchical information can complement and reinforce various place embedding generating methods.