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.
UrbanVerse: Learning Urban Region Representation Across Cities and Tasks
School of Computing and Information System, University of Melbourne（计算机与信息系统学院，墨尔本大学）；School of Computer Science and Engineering, University of New South Wales（计算机科学与工程学院，新南威尔士大学）
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；Northwestern Polytechnical University（西北工业大学）；Alibaba Group, US（美国阿里巴巴集团）；National University of Singapore（新加坡国立大学）
Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Washington（华盛顿大学）；Chang’an University（长安大学）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；JD iCity, JD Technology（京东科技京东智联云）；JD Intelligent Cities Research（京东智能城市研究院）；Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images
University of Warwick（华威大学）；University of Oxford（牛津大学）；King’s College London（伦敦国王学院）；University of Middlesex（密德萨斯大学）；Queen Mary University of London（伦敦玛丽女王大学）；Brunel University London（伦敦布鲁内尔大学）；Manchester University（曼彻斯特大学）；Medizinische Hochschule Hannover（汉诺威医学院）；Imperial College London（帝国理工学院）
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach
School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Engineering, IT and Physical Sciences, Federation University Australia（澳大利亚联邦大学工程、信息技术与物理科学学院）
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Urban AI Institute, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院城市人工智能研究院）；Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；Department of Industrial Engineering and Systems, KAIST（韩国科学技术院工业工程与系统系）；Department of Civil and Environmental Engineering, KAIST（韩国科学技术院土木与环境工程系）
