ArticleNature communications2025
Global gridded population datasets systematically underrepresent rural population.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent trajectories of biometeorology in the Global South: a systematic review of biometeorology research in the International Journal of Biometeorology.International journal of biometeorology · 2026Pooled it
- Large cities lose their growth advantage as countries urbanize.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A mark and recapture perspective on vaccination touchpoints.Gates open research · 2026Article
- A high-resolution global annual city and town boundaries dataset (2000-2022) derived from GLC_FCS30D product.Scientific data · 2025Article
- The role of artificial intelligence for early warning systems: Status, applicability, guardrails, and ways forward.iScience · 2025Article
- Mapping spatiotemporal variations in lakeside populations for sustainable lake management.Scientific reports · 2025Article
- EpiGeoPop: a tool for developing spatially accurate country-level epidemiological models.Scientific reports · 2025Article
- Global gridded population datasets systematically underrepresent rural population.Nature communications · 2025Article
- Tackling public health data gaps through Bayesian high-resolution population estimation: A case study of Kasaï-Oriental, Democratic Republic of the Congo.PLOS global public health · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous initiatives towards sustainable development rely on global gridded population data. Such data have been calibrated primarily for urban environments, but their accuracy in the rural domain remains largely unexplored. This study systematically validates global gridded population datasets in rural areas, based on reported human resettlement from 307 large dam construction projects in 35 countries. We find large discrepancies between the examined datasets, and, without exception, significant negative biases of -53%, -65%, -67%, -68%, and -84% for WorldPop, GWP, GRUMP, LandScan, and GHS-POP, respectively. This implies that rural population is, even in the most accurate dataset, underestimated by half compared to reported figures. To ensure equitable access to services and resources for rural communities, past and future applications of the datasets must undergo a critical discussion in light of the identified biases. Improvements in the datasets' accuracies in rural areas can be attained through strengthened population censuses, alternative population counts, and a more balanced calibration of population models.
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