Urban region profiling can benefit urban analytics. Although existing studies
have made great efforts to learn urban region representation from multi-source
urban data, there are still three limitations: (1) Most related methods focused
merely on global-level inter-region relations while overlooking local-level
geographical contextual signals and intra-region information; (2) Most previous
works failed to develop an effective yet integrated fusion module which can
deeply fuse multi-graph correlations; (3) State-of-the-art methods do not
perform well in regions with high variance socioeconomic attributes. To address
these challenges, we propose a multi-graph representative learning framework,
called Region2Vec, for urban region profiling. Specifically, except that human
mobility is encoded for inter-region relations, geographic neighborhood is
introduced for capturing geographical contextual information while POI side
information is adopted for representing intra-region information by knowledge
graph. Then, graphs are used to capture accessibility, vicinity, and
functionality correlations among regions. To consider the discriminative
properties of multiple graphs, an encoder-decoder multi-graph fusion module is
further proposed to jointly learn comprehensive representations. Experiments on
real-world datasets show that Region2Vec can be employed in three applications
and outperforms all state-of-the-art baselines. Particularly, Region2Vec has
better performance than previous studies in regions with high variance
socioeconomic attributes.

提出了一种名为 Region2Vec 的多图表示学习框架，用于城市区域分析，可以捕捉地理上下文信号和区域间的联系，同时用于表示区域内部信息的知识图谱。使用编码器 - 解码器多图融合模块来联合学习综合表示。在现实世界数据集上的实验证明，Region2Vec 可以在三个应用程序中使用，并优于所有其他基线。特别地，Region2Vec 在具有高方差社会经济属性的区域中的表现优于先前的研究。