论文标题
城市地区分析通过多画像表示学习框架
Urban Region Profiling via A Multi-Graph Representation Learning Framework
论文作者
论文摘要
城市地区分析可以使城市分析受益。尽管现有的研究竭尽全力从多源城市数据中学习城市地区的代表性，但仍然存在三个局限性：（1）最相关的方法仅集中在全球级别的区域间关系上，同时忽略了本地级别的地理上下文信号和内部区域信息； （2）以前的大多数作品未能开发出有效但综合的融合模块，该模块可以深层融合多绘图相关性； （3）最新方法在具有较高差异的社会经济属性的地区表现不佳。为了应对这些挑战，我们提出了一个用于城市地区分析的多盖代表性学习框架，称为region2vec。具体而言，除了编码人类的移动性以用于区域间关系外，还引入了地理社区以捕获地理上下文信息，而POI侧信息则是通过知识图来表示内部区域信息的。然后，图表用于捕获区域之间的可访问性，附近和功能相关性。为了考虑多个图的判别特性，进一步提出了编码器折线融合模块，以共同学习全面的表示。现实世界数据集的实验表明，可以在三个应用程序中使用区域2VEC，并且表现优于所有最新基准。特别是，在具有较高差异的社会经济属性的地区，区域2VEC的性能要比以前的研究更好。
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.
