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

Author:

Ma Ding1ORCID,He Fangning2,Yue Yang1,Guo Renzhong1,Zhao Tianhong3,Wang Mingshu4ORCID

Affiliation:

1. Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University and Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, PR China

2. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA

3. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, PR China

4. School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK

Funder

National Natural Science Foundation of China

Guangdong Science and Technology Strategic Innovation Fund

Guangdong-Hong Kong-Macau Joint Laboratory Program

Shenzhen Key Laboratory of Digital Twin Technologies for Cities

Publisher

Informa UK Limited

Reference59 articles.

1. A city is not a tree;Alexander C.;Architectural Forum,1965

2. Adelson E.H. et al. 1984. Pyramid methods in image processing. RCA Engineer 29 (6) 33–41.

3. Diffusion-convolutional neural networks;Atwood J.;In: Advances in neural information processing systems,2016

4. The Size, Scale, and Shape of Cities

5. Representation Learning: A Review and New Perspectives

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