Dataset List

Vol.|Area

No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Global Urban Expansion Simulation Dataset (1992-2050, V1.0)


LIU Zhifeng1,2YING Jiahe1,2HE Chunyang*1,3,4HUANG Qingxu1,2BAI Qiaoxian1,2PAN Xinhao1,2
1 State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE)，Faculty of Geographical Science，Beijing Normal University，Beijing 100875，China2 School of Natural Resources，Faculty of Geographical Science，Beijing Normal University，Beijing 100875，China3 Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education，Beijing Normal University，Beijing 100875，China4 Academy of Disaster Reduction and Emergency Management，Ministry of Emergency Management and Ministry of Education，Beijing 100875，China

DOI:10.3974/geodb.2024.06.05.V1

Published:Jun. 2024

Visitors：307       Data Files Downloaded：20      
Data Downloaded：475.65 MB      Citations：

Key Words：

urban expansion,global,1992-2050,built-up area,urbanization

Abstract：

The authors developed the yearly data of global urban expansion from 1992 to 2020 integrating the global built-up area data and the global urban center location data. The accuracy evaluation shows that the Kappa coefficient of 0.88. Then, authors simulated the global urban expansion from 2020 to 2050 under the SSPs using Land Use Scenario Dynamics-urban (LUSD-urban) model. Global Urban Expansion Simulation Dataset (1992-2050) V1.0 is consisted of: (1) the global yearly urban built-up area from 1992 to 2020; (2) the global future urban built-up area for every five years from 2025 to 2050. The spatial resolution of the dataset is 1 km. The dataset is archived in .tif format, and consists of 383 data files with data size of 498 MB (Compressed into one file with 23.7 MB). The data paper will be published at Journal of Global Change Data & Discovery, Vol. 8, 2024.

Foundation Item：

Ministry of Science and Technology of P. R. China (2019YFA0607203)

Data Citation：

LIU Zhifeng, YING Jiahe, HE Chunyang*, HUANG Qingxu, BAI Qiaoxian, PAN Xinhao. Global Urban Expansion Simulation Dataset (1992-2050, V1.0)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.06.05.V1.

References：


     [1] Seto, K. C., Güneralp, B., Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools [J]. Proceedings of the National Academy of Sciences. 2012, 109: 16083-16088.
     [2] Wu J., Urban ecology and sustainability: the state-of-the-science and future directions [J]. Landscape Urban Planning, 2014, 125: 209-221.
     [3] Klein, G, K., Beusen, A., Doelman, J., et al. Anthropogenic land use estimates for the holocene - HYDE 3.2 [J]. Earth System Science Data, 2017, 9(2): 927-953.
     [4] Chen, G, Z., Li, X., Liu, X, P., et al. Global projections of future urban land expansion under shared socioeconomic pathways [J]. Nature Communication, 2020, 11(1): 12.
     [5] Gao, J., O’Neill, B. C. Mapping global urban land for the 21st century with data-driven simulations and shared socioeconomic pathways. Nature Communication, 2020: 2302.
     [6] He, C., Liu, Z., Gou, S., et al. Detecting global urban expansion over the last three decades using a fully convolutional network [J]. Environmental Research Letters, 2019, 14: 034008.
     [7] He, C., Liu, Z., Wu, J., et al. Future global urban water scarcity and potential solutions. Nature Communications, 2021, 12: 4667.
     [8] ESA. Land Cover CCI Product User Guide Version 2 [J]. Technical Report, 2017.
     [9] Florczyk, A.; Corbane, C., et al. GHS-UCDB R2019A - GHS Urban Centre Database 2015, multitemporal and multidimensional attributes [Z]. European Commission, Joint Research Centre (JRC), 2019, https://doi.org/10.2905/53473144-b88c-44bc-b4a3-4583ed1f547e.
     [10] Department of Economic and Social Affairs Population Dynamics. 2018 Revision of World Urbanization Prospects [Z]. United Nations, 2018, https://population.un.org/wup.
     [11] Chen, S., Huang, Q., Muttarak, R. et al. Updating global urbanization projections under the Shared Socioeconomic Pathways [J]. Scientific Data, 9, 2022, 137. https://doi.org/10.1038/s41597-022-01209-5.
     [12] Jeffrey, J. D., Dean B. G., Global multi-resolution terrain elevation data 2010 [Z]. Earth Resources Observation and Science (EROS) Center, 2018. https://lta.cr.usgs.gov/GMTED2010.
     [13] Wieder, W. R., Boehnert, J., Bonan, G. B., et al. Regridded harmonized world soil database v1.2 [Z]. ORNL DAAC, Oak Ridge, Tennessee, USA.
     [14] Global Self-consistent, Hierarchical, High-resolution geography database [Z]. National Centers for Environmental Information, 2017. https://www.ngdc.noaa.gov/mgg/shorelines/.
     [15] Schneider, A., Jost, A., Coulon, C., et al. Global scale river network extraction based on high-resolution topography and constrained by lithology, climate, slope, and observed drainage density [J], Geophysical Research Letter, 2017, 44. DOI: 10.1002/2016GL071844.
     [16] Global road database [DB]. Resource and Environmental Data Cloud Platform (REDCP), http://www.resdc.cn/data.aspx?DATAID=207.
     [17] Global surface meteorological observations historical database [DB]. National Climate Information Center (NCIC), http://data.cma.cn/data/detail/dataCode.
     [18] He, C., Okada, N., Zhang, Q., et al. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata [J]. Landscape and Urban Planning, 2020, 86, 79-91.
     [19] Huang, M., Wang, Z. C., Pan, X. H., et al. Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and variou s urban expansion modes [J]. Earth's Future, 2022, 10, e2021EF002572
     

Data Product:

ID Data Name Data Size Operation
1 GlobalUrbanExpansion1992-2050_1.0.rar 24353.05KB
Co-Sponsors
Superintend