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作者

Wenjia Zhang;Chenyu Fang;Lin Zhou;Jiancheng Zhu

来源

CITIES,Vol.104,Issue1,Article 102809

语言

英文

关键字

Spatial structure;Megaregion;Commuting network;Big data;Weighted stochastic block model (WSBM);The Greater Bay Area of China

作者单位

Urban Future Laboratory of Peking University (Shenzhen), Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China;School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China;China Academy of Urban Planning & Design Shenzhen, Shenzhen, Guangdong 518055, China;Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany;Faculty of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich 80333, Germany;Urban Future Laboratory of Peking University (Shenzhen), Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China;School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China;China Academy of Urban Planning & Design Shenzhen, Shenzhen, Guangdong 518055, China;Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany;Faculty of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich 80333, Germany

摘要

Understanding the spatial structure of a megaregion, like the Pearl River Delta (PRD) or the emerging Guangdong-Hong Kong-Macao Greater Bay Area in China, is important for regional planning and governance. However, few studies have conceptualized varying regional spatial structure via the network perspective. This study thus develops a complex network approach, particularly by adopting a novel machine-learning-based weighted stochastic block model and visual analytics, to measuring potential spatial mesoscale structures in PRD. We build a finer-grained commuting network with 60 sub-city divisions as nodes by aggregating a large set of mobile phone signaling data collected in 2018. Results detect a hybrid polycentric configuration of two community components, each with a core-periphery structure inside. One community centered on Guangzhou has a semi-core commuting belt alongside the intercity railway and high-speed rails, while the other centered on Shenzhen exhibits a concentric commuting ring. Intercity transport infrastructure, spatial proximity, and regional integration policies appear to play important roles in shaping spatial and network structures in the megaregion, while the constraint of administrative boundary is dissolving. This study finally discusses the implication for regional coordination policies and spatial planning strategies in PRD and the Greater Bay Area.