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Urban Region Pre-training and Prompting: A Graph-based Approach

12 August 2024
Jiahui Jin
Yifan Song
Dong Kan
Haojia Zhu
Xiangguo Sun
Zhicheng Li
Xigang Sun
Jinghui Zhang
    AI4TS
    AI4CE
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Abstract

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Previous work often neglects the spatial structures and functional layouts between entities, limiting their ability to capture transferable knowledge across regions. Further, these methods struggle to adapt effectively to specific downstream tasks, as they do not adequately address the unique features and relationships required for different downstream tasks. In this paper, we propose a G\textbf{G}Graph-based U\textbf{U}Urban R\textbf{R}Region P\textbf{P}Pre-training and P\textbf{P}Prompting framework (GURPP\textbf{GURPP}GURPP) for region representation learning. Specifically, we first construct an urban region graph that integrates detailed spatial entity data for more effective urban region representation. Then, we develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of interactions among entities. To further enhance the adaptability of these embeddings to different tasks, we design two graph-based prompting methods to incorporate explicit/hidden task knowledge. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our GURPP framework.

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