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Data Details

Dataset on the Synergistic Quantity-quality Development of New-Type Urbanization and Driving Factors in the Yangtze River Economic Belt (2000-2022)


CHEN Zehui1,2FANG Chuanglin*1,2
1 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 College of Resources and Environment，University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2026.03.09.V1

Published:Mar. 2026

Visitors：27       Data Files Downloaded：0      
Data Downloaded： 无      Citations：

Key Words：

new-type urbanization,urbanization level,urbanization quality,synergistic evolution, driving mechanism,Yangtze River Economic Belt

Abstract：

110 cities in the Yangtze River Economic Belt were taken as the study area according to the perspective of synergistic quantity-quality development. The dataset on the synergistic quantity-quality development of new-type urbanization and driving factors in the Yangtze River Economic Belt (2000-2022) was developed using the entropy method, kernel density estimation, the synergistic evolution model, and Geo-detector to measure and identify the spatiotemporal differentiation, synergistic evolution, and driving mechanisms of new-type urbanization from 2000 to 2022. The dataset includes the following data for 2000-2022 in the Yangtze River Economic Belt: (1) boundary data of the Yangtze River Economic Belt; (2) urbanization level and quality for 110 cities; (3) the synergistic evolution of urbanization level and quality; (4) development patterns of new-type urbanization and their transition matrices; and (5) data of driver identification and interactive detection. The dataset is archived in .xlsx format, and consists of 9 data files with data size of 1.28 MB (compressed into 1 file with 934 KB). The analytical paper based on the dataset will be published at Acta Geographica Sinica, Vol. 81, No. 6, 2026.

Foundation Item：

National Natural Science Foundation of China (42121001)

Data Citation：

CHEN Zehui, FANG Chuanglin*. Dataset on the Synergistic Quantity-quality Development of New-Type Urbanization and Driving Factors in the Yangtze River Economic Belt (2000-2022)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.03.09.V1.

Related Publication：

Chen, Z. H., Fang, C. L. Spatio-temporal patterns and driving mechanisms of new-type urbanization in the Yangtze River Economic Belt [J/OL]. Acta Geographica Sinica. https://link.cnki.net/urlid/11.1856.P.20260306.1242.004
     

References：


     [1] Liu, T., Zhuo, Y. X., Peng, R. X., et al. Urban-rural population change and the regional types evolution of China's urbanization [J]. Acta Geographica Sinica, 2022, 77(12): 3006-3022.
     [2] Chen, M. X., Ye, C., Lu, D. D., et al. Cognition and construction of the theoretical connotation for new-type urbanization with Chinese characteristics [J]. Acta Geographica Sinica, 2019, 74(4): 633-647.
     [3] Tang, Y., Xue, D. Q., Song, Y. Y., et al. Coupling coordination and influencing factors of economic scale-structure-efficiency of resource-based cities on the Loess Plateau [J]. Acta Geographica Sinica, 2025, 80(3): 793-810.
     [4] Deng, M. Y., Wei, X. L., Zhang, G. J. Research on the coordinated evaluation relationship between new urbanization and green development in China [J]. Journal of Natural Resources, 2024, 39(7): 1682-1697.
     [5] Li, Q. Z., Hu, X. J., Wei, B. J., et al. Coupling relationship between green space and urban expansion in Changsha [J]. Economic Geography, 2022, 42(11): 87-94.
     [6] He, X. R., Shi, C. X., Zhou, G. H. Spatio-temporal evolution and adaptation relationship between urban scale and urban livability in the Yangtze River Economic Belt [J]. Geographical Research, 2024, 43(7): 1769-1789.
     [7] Wang, J. F., Xu, C. D. Geodetector: principle and prospective [J]. Acta Geographica Sinica, 2017, 72(1): 116-134.
     [8] Wang, J. F., Haining, R., Zhang, T. L., et al. Statistical modeling of spatially stratified heterogeneous data [J]. Annals of the American Association of Geographers, 2024, 114(3): 499-519.
     [9] Wei, J., Li, Z., Wang, J., et al. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations [J]. Atmospheric Chemistry and Physics, 2023, 23(2): 1511-1532.
     [10] Peng, S. 1-km monthly precipitation dataset for China (1901-2023) [J/DB/OL]. National Tibetan Plateau/Third Pole Environment Data Center, 2020.

Data Product:

ID Data Name Data Size Operation
1 NewTypeUrbanizationYREB.rar 934.98KB
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