Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis

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Abstract

The two prerequisites for monitoring SDG11.A “support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning” are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban–rural continuum for five typical urban agglomerations in China, including the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu–Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A.

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CITATION STYLE

APA

Li, S., Guo, H., Sun, Z., Liu, Z., Jiang, H., & Zhang, H. (2023). Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis. Remote Sensing, 15(4). https://doi.org/10.3390/rs15040920

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