This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.

File naming and required software

Chinese urban expansion and urban growth boundary data are stored in the form of grid files, including two folders. The folder "AnnualUrbanLand_China" includes the Chinese urban expansion. The file name is "SSP Scenario_mode_yyyy. tif", where "SSP Scenario" represents the shared socio-economic pathway, "mode" represents the urban expansion mode, and "yyyy" represents the year, For example, "SSP1_0.0_2021. tif" means that this grid file describes the urban built-up area simulated with the urban expansion mode index of 0 under the SSP1 scenario in China in 2021. The folder "China_UGBs_SPPs_2100" includes the urban growth boundary. The file name is "SSP Scenario_mode_ 2100", in which "SSP Scenario" represents the shared socio-economic pathway, and "mode" represents the urban expansion mode. For example, "SSP1_0.0_2100. tif" represents the grid file describing the urban growth boundary in China with the urban expansion mode index of 0 under the SSP1 scenario.
Data reading mode: users can use geographic information systems and remote sensing related software such as ESRI ArcGIS software, ERDAS software and ENVI software to read data.

Data Citations Data citation guideline What's data citation?

Cite as:

Huang, M., Wang, Z., Pan, X., Gong, B., Tu, M., Liu, Z. (2022). Dataset of China's urban expansion and urban growth boundaries (2021-2100) V1.0. A Big Earth Data Platform for Three Poles, DOI: 10.11888/HumanNat.tpdc.272852. CSTR: 18406.11.HumanNat.tpdc.272852. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Huang, M., Wang, Z., Pan, X., Gong, B., Tu, M., & Liu, Z. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. https://doi.org/10.1029/2021EF002572( View Details | Download | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

National Key R&D Program of China (Grant No. 2019YFA0607203)

National Natural Science Foundation of China (41871185)

Copyright & License

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)