Spatiotemporal Evolution Characteristics and Influencing Factors of the Pearl River Delta Urban Agglomeration

Zehui Sun; Mengrui Yang; Zhenfen Dong; Haoyu Wu; Ziming Wang; Yihan Gao; Sheng Li

doi:10.54691/rf2w6y19

Authors

Zehui Sun School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Mengrui Yang School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Zhenfen Dong School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Haoyu Wu School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Ziming Wang School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Yihan Gao School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China
Sheng Li School of Mathematics and Physics, Suqian University, Suqian, Jiangsu, China

DOI:

https://doi.org/10.54691/rf2w6y19

Keywords:

Pearl River Delta Urban Agglomeration; GTWR; Box Diagram; Grey Correlation Analysis.

Abstract

Pinpointing the driving forces and spatiotemporal variances in the evolution of urban agglomeration landscape patterns is key for enhancing territorial spatial development and attaining high quality regional growth.Taking Guangzhou and Shenzhen, the core cities of the Pearl River Delta, as cases, this study constructs a four-dimensional driving factor system. The system includes natural constraints, socioeconomic factors, infrastructure, and policy planning. Based on 2015-2023 panel data, the Geographically and Temporally Weighted Regression (GTWR) model is employed to analyze their spatiotemporal impacts. The results show that: (1) Topographic relief is the core natural constraint: Guangzhou has long been under high-intensity topographic constraints, while Shenzhen remains at an extremely low level. (2) Socioeconomic driving forces have strengthened overall but show structural differentiation: In the later period, Shenzhen's driving force outpaced Guangzhou's, with its export-oriented economy contributing significantly. GTWR uncovers notable spatiotemporal heterogeneity: Per capita GDP had an inverted U-shaped effect on Shenzhen, but a steadily strengthening one on Guangzhou. By 2023, natural constraints and policy guidance shaped Guangzhou's landscape evolution, while technological innovation and rail transit steered Shenzhen's. (4) The comprehensive effect evolved from socioeconomic dominance to infrastructure guidance and then to policy planning drive, reflecting the transformation from extensive expansion to intensive improvement. These findings provide a scientific basis for differentiated territorial spatial governance of urban agglomerations.

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