Global gridded population datasets systematically underrepresent rural population

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.