标题: From grids to dendrites: Quantifying spatial heterogeneity in urban road networks

作者: Zhang, L (Zhang, Lin); Li, SH (Li, Shenhong); Liu, YL (Liu, Yaolin); Huang, HS (Huang, Haosheng); van de Weghe, N (van de Weghe, Nico)

来源出版物: COMPUTERS ENVIRONMENT AND URBAN SYSTEMS  卷: 121  文献号: 102309  DOI: 10.1016/j.compenvurbsys.2025.102309  Published Date: 2025 OCT  

摘要: Road network spatial heterogeneity significantly influences urban development and infrastructure efficiency. We present a novel approach using Relational Graph Convolutional Networks (RGCN) to analyze road networks across 58 global cities from 2020 to 2024, introducing Hits@1 as a comprehensive measure of spatial heterogeneity. When nodes (Road intersections) exhibit high spatial heterogeneity, they are more diverse and distinct from each other, making the embedding process more straightforward for the RGCN model. A higher Hits@1 score indicates RGCN can better differentiate between nodes, directly correlating with greater spatial heterogeneity in the road network. Our analysis demonstrates that Hits@1 can effectively distinguish four road network typologies (Dendritic, Grid, Mixed, and Polygonal), with Dendritic networks showing the highest heterogeneity (Hits@1 approximate to 0.57) and Grid networks the lowest (Hits@1 approximate to 0.42). Statistical analysis reveals strong correlations between heterogeneity and urban metrics, including traffic index (R = 0.36), CO2 emissions (R = 0.43), and road density (R = 0.48). Temporal analysis of road evolution shows distinct regional patterns: developing regions trend toward higher heterogeneity, while Western cities demonstrate increasing uniformity. Chinese coastal cities exhibit increasing complexity, contrasting with inland cities' movement toward organized patterns. These findings validate Hits@1 as an effective metric for understanding road network evolution and provide valuable insights for urban planning.

作者关键词: Spatial heterogeneity; Urban road networks; Relational graph convolutional networks; (RGCN); Road typologies; Carbon emissions; Urban planning; Urban morphology

KeyWords Plus: DYNAMICS; STREETS

地址: [Zhang, Lin; Liu, Yaolin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Zhang, Lin; Huang, Haosheng; van de Weghe, Nico] Univ Ghent, Dept Geog, Ghent, Belgium.

[Li, Shenhong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China.

通讯作者地址: Huang, HS (通讯作者)，Univ Ghent, Dept Geog, Ghent, Belgium.

电子邮件地址: haosheng.huang@ugent.be

影响因子：8.3