Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
Data description paper
 | 
16 Aug 2024
Data description paper |  | 16 Aug 2024

A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data

Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou

Viewed

Total article views: 3,749 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,773 870 106 3,749 88 86
  • HTML: 2,773
  • PDF: 870
  • XML: 106
  • Total: 3,749
  • BibTeX: 88
  • EndNote: 86
Views and downloads (calculated since 06 Feb 2024)
Cumulative views and downloads (calculated since 06 Feb 2024)

Viewed (geographical distribution)

Total article views: 3,749 (including HTML, PDF, and XML) Thereof 3,626 with geography defined and 123 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Read more
Altmetrics
Final-revised paper
Preprint