2020/02/06 by Toru Shimizu, Takahiro Yabe, Shimizu, Toru +3
Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Geographic Information Systems Studies #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2002.02058
openalex publication_date 2020/02/06 · openalex created_date 2020/02/14 · openalex updated_date 2026/07/28
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.