A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning.

ABSTRACT: Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validat

SUBMITTER: Liu L 

PROVIDER: S-EPMC10808219 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature