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A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning
Luling Liu1,2, Xin Cao3,4, Shijie Li1,2
1State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Scientific Data
|January 24, 2024
Summary
This study introduces GlobPOP, a new continuous global gridded population dataset. GlobPOP offers accurate, consistent population estimates for time-series analysis, aiding sustainable development policies.
Area of Science:
- Geospatial analysis
- Demography
- Sustainable development
Background:
- Effective global policies require continuous monitoring of population spatial dynamics.
- Existing gridded population data lack consistency for time-series analysis.
Purpose of the Study:
- To develop a continuous global gridded population dataset (GlobPOP).
- To enable accurate time-series analysis for sustainable development, epidemiology, and urban planning.
Main Methods:
- A data fusion framework integrating cluster analysis and statistical learning.
- Generation of the GlobPOP dataset in population count and density formats.
Main Results:
- GlobPOP dataset demonstrated high accuracy through spatial validation.
- Temporal validation confirmed consistent performance across diverse countries and cities.
Conclusions:
- The GlobPOP dataset provides a reliable tool for time-series population analysis.
- Enables exploration of population development patterns at multiple scales for informed policymaking.
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