  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.
