Decoding urban form through street networks: A nationwide analysis of Chinese cities

作     者：Yao, Lihao Peng, Ke Li, Chaosu Huang, Yuan 

作者机构：Guangdong Ocean Univ Coll Coastal Agr Sci Zhanjiang Peoples R China Hunan Univ Sch Architecture & Planning Dept Urban Planning Changsha Peoples R China Hong Kong Univ Sci & Technol Guangzhou Urban Governance & Design Thrust 1 Duxue Rd Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Div Publ Policy Hong Kong Peoples R China Georgia Inst Technol Div Comp Sci Atlanta GA USA 

出 版 物：《CITIES》 (Cities)

年 卷 期：2025年第158卷

核心收录：

学科分类：082803[工学-农业生物环境与能源工程] 08[工学] 0828[工学-农业工程] 0833[工学-城乡规划学] 

基　　金：The 2023 Guangdong Province General University Youth Innovation Talent Project [2023KQNCX026] National Natural Science Foundation of China Guangdong Basic and Applied Basic Research Foundation [2024A1515011609] Guangzhou Science and Technology Department [2023A03J0656] Hong Kong University of Science and Technology (Guangzhou) program for Scientific Research Startup Funds of Guangdong Ocean University 

主　　题：Street network Urban metrics Urban complexity Connectedness Urban planning 

摘      要：The street network presents complex urban characteristics and provides a valuable basis for exploring urban form. However, the street network measurement typically relies on highly correlated and redundant indicators. This study presents a novel approach to quantifying urban form through the analysis of street networks across 326 urbanized areas in China, utilizing a combination of physical structure and topological metrics derived from the OpenStreetMap data. Recognizing the complexity and variability in urban forms, this research identifies a streamlined set of metrics to effectively describe urban form. The results show that the complexity of the urban form can be evaluated by several dimensions of street network features, including aggregate characteristics, street form, connectedness, intensity, node importance, and network transitivity. Based on these dimensions, smaller subsets of the metrics are further identified to characterize the diverse urban form across city sizes. The findings enhance the current understanding of urban form by introducing a reduced set of metrics based on street networks that effectively captures the essence of diverse urban landscapes, providing useful insights for urban planners and policymakers.