Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Ma, D., He, F., Yue, Y., Guo, R., Zhao, T. and Wang, M. (2024) Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities. International Journal of Geographical Information Science, (doi: 10.1080/13658816.2024.2321229)

Abstract

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale.

• Item Type: Articles
• Additional Information: This study is supported and funded by the National Natural Science Foundation of China (No. 42001180 and No. 42071357). We would like also to acknowledge the financial support from the Guangdong Science and Technology Strategic Innovation Fund (the Guangdong-Hong Kong-Macau Joint Laboratory Program, No. 2020B1212030009) and Shenzhen Key Laboratory of Digital Twin Technologies for Cities (No. ZDSYS20210623101800001).
• Keywords: graph convolutional network; deep learning; street network; urban structure.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Dr Mingshu
• Authors: Ma, D., He, F., Yue, Y., Guo, R., Zhao, T., and Wang, M.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: International Journal of Geographical Information Science
• Publisher: Taylor & Francis
• ISSN: 1365-8816
• ISSN (Online): 1365-8824
• Published Online: 29 February 2024
• Copyright Holders: Copyright © 2024 The Author(s)
• First Published: First published in International Journal of Geographical Information Science 2024
• Publisher Policy: Reproduced under a Creative Commons license