The available spatial data are rapidly growing and also diversifying. One may obtain in large quantities information such as annotated point/place of interest (POIs), check-in comments on those POIs, geo-tagged microblog comments, and demarked regions of interest (ROI). All sources interplay with each other, and together build a more complete picture of the spatial and social dynamics at play in a region. However, building a single fused representation of these data entries has been mainly rudimentary, such as allowing spatial joins. In this paper, we extend the concept of semantic embedding for POIs (points of interests) and devise the first semantic embedding of ROIs, and in particular ones that captures both its spatial and its semantic components. To accomplish this, we develop a multipart network model capturing the relationships between the diverse components, and through random-walk-based approaches, use this to embed the ROIs. We demonstrate the effectiveness of this embedding at simultaneously capturing both the spatial and semantic relationships between ROIs through extensive experiments. Applications like popularity region prediction demonstrate the benefit of using ROI embedding as features in comparison with baselines.

现有的空间数据正在迅速增长，并且还在多样化。人们可能会大量获取信息，例如带注释的兴趣点/地点（POI），关于这些POI的签入评论，带有地理标签的微博评论以及标记的关注区域（ROI）。所有资源相互影响，共同构成一个区域内正在发挥作用的空间和社会动态的更完整图景。但是，建立这些数据条目的单个融合表示主要是基本的，例如允许空间连接。在本文中，我们扩展了POI（兴趣点）的语义嵌入的概念，并设计了ROI的第一个语义嵌入，尤其是同时捕获其空间和语义成分的ROI。为此，我们开发了一个多部分的网络模型，该模型捕获了不同组件之间的关系，并通过基于随机游走的方法来嵌入ROI。我们通过广泛的实验证明了该嵌入在同时捕获ROI之间的空间和语义关系方面的有效性。诸如流行区域预测之类的应用程序证明了与基线相比，使用ROI嵌入作为功能的好处。