论文标题
从人类流动性轨迹中学习具有空间层次结构的细粒度嵌入
Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories
论文作者
论文摘要
由人类流动性轨迹产生的位置嵌入已成为了解地方功能的一种流行方法。对于许多应用来说，具有高空间分辨率的位置嵌入是可取的，但是，缩小空间分辨率会由于数据稀疏性而导致的嵌入质量恶化，尤其是在人口较少的地区。我们通过提出一种生成细粒度位置嵌入的方法来解决此问题，该方法根据观察到的数据点的局部密度来利用空间层次信息。通过下一个位置预测任务，使用来自日本3个城市的现实世界轨迹数据，将我们细颗粒的位置嵌入的有效性与基线方法进行了比较。此外，我们证明了我们的细颗粒地点嵌入对土地使用分类应用的价值。我们认为，合并空间分层信息的技术可以补充和加强嵌入生成方法的各种位置。
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.
