50-Year Urban Expansion Patterns in Shanghai: Analysis Using Impervious Surface Data and Simulation Models

Chen Gao, Yongjiu Feng (), Rong Wang, Zhenkun Lei, Shurui Chen, Xiaoyan Tang and Mengrong Xi
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Chen Gao: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Yongjiu Feng: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Rong Wang: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Zhenkun Lei: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Shurui Chen: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Xiaoyan Tang: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China
Mengrong Xi: College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China

Land, 2023, vol. 12, issue 11, 1-19

Abstract: Megacities serve as crucial catalysts for national economic and social development, and Shanghai, one of China’s most prominent metropolitan areas, exemplifies this transformative urbanization. To study Shanghai’s urban expansion, we extracted urban land cover data from 1985 to 2020 using impervious area products and simulated urban expansion dynamics from 2021 to 2035 by employing the cellular automata model. Leveraging these data, we analyzed a 50-year period of urban expansion and investigated the drivers, including economic factors, population growth, and transportation infrastructure. Our findings indicate that the size of Shanghai’s urban area in 2035 will be nearly 13 times that of 1985. Over these five decades, Shanghai’s urban centroid shifted from the northeast to the southwest, with early urban expansion concentrated in the northeast and later expansion in the southwest. New urban patches primarily emerged at the edges of the initial urban area. As time progressed, areas with higher urban expansion intensity moved outward from the city center, mirroring the trend of urban expansion hotspots. Landscape indicators also demonstrated a trend of urban patches initially spreading and subsequently clustering. Overall, the development of Shanghai’s metropolitan area exhibits substantial spatiotemporal heterogeneity. By integrating correlation analysis and generalized additive models, we quantified the impact of urban expansion drivers. The results show that economic and population factors had high correlation coefficients (over 0.97) with urban area, and proximity to the city center and road network greatly contributed to urban expansion. Our research amalgamates various theories and methods to analyze the spatiotemporal dynamics of urban expansion in metropolitan areas. This work provides a valuable data foundation to aid policymakers in designing effective metropolitan development policies.

Keywords: urban expansion; spatiotemporal dynamics; remote sensing monitoring; cellular automata (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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