Location Representation Learning for Urban Management with Heterogeneous Graph

Data-driven approaches to urban management and planning have attracted significant attention within interdisciplinary research spanning civil engineering and spatial data mining. Urban computing methods, particularly those leveraging graph representation learning techniques developed over the past decade, provide great potential in advancing urban management planning. In this paper, we adopt the perspective that cities can be effectively represented as network-structured graphs, wherein locations such as buildings and amenities are interconnected through road networks. These urban networks are further partitioned into subgraphs, and we introduce a novel model designed to learn effective representations of these subgraphs as distinct urban areas by utilizing heterogeneous graph neural networks. Specifically, our contributions include: firstly, developing a graph representation learning model that captures the geospatial characteristics of urban locations within dynamically-sized graphs; secondly, implementing an innovative graph pooling-based Graph Convolution Network; and finally, establishing a new benchmark for urban location representation learning tailored to urban management and planning tasks. Experimental results on various graph types demonstrate that our proposed model significantly outperforms existing methods in graph representation learning, particularly in applications involving urban location analysis for public safety and dense, heterogeneous social media networks.