Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories

TL;DR

This paper proposes a method to generate high-resolution place embeddings by leveraging spatial hierarchical information from human mobility data, improving accuracy in less populated areas and benefiting applications like land use classification.

Contribution

It introduces a novel approach that incorporates spatial hierarchy into place embeddings, addressing data sparsity issues in fine-grained spatial resolutions.

Findings

Enhanced next place prediction accuracy

Improved land use classification performance

Effective in areas with sparse data

Abstract

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.