Urban Region Profiling via Multi-Graph Representation Learning

Yan Luo, Fu-Lai Chung, Kai Chen

Abstract: Profiling urban regions is essential for urban analytics and planning. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still limitations on modelling local-level signals, developing an effective yet integrated fusion framework, and performing well in regions with high variance socioeconomic attributes. Thus, we propose a multi-graph representation 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. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. 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.