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.

通过提出一种方法，利用和观测数据点的局部密度有关的空间层次信息生成细粒度的地点嵌入，解决了降低空间分辨率时数据稀疏性导致嵌入质量下降的问题。通过使用日本 3 个城市的真实世界轨迹数据进行下一个地点预测任务，比较了我们的细粒度地点嵌入与基准方法的有效性。此外，我们展示了我们的细粒度地点嵌入在土地利用分类应用中的价值。我们认为，将空间分层信息纳入其中可以补充和加强各种地点嵌入生成方法。