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Global population datasets underrepresent rural population
This research reveals that widely used global population data heavily underestimate rural population. If not carefully considered, these errors in the data can cause inequitable access to resources and services for rural communities around the globe.
The models behind these products have varying degrees of complexity, ranging from simple areal disaggregation of census counts (as in GWP 14) to dasymetric mapping approaches involving numerous auxiliary data sources, such as satellite-based detection of infrastructures and nightlights (as in WorldPop 18). The findings from this study hold significant implications for a wide array of research and policy fields that consider rural areas and their populations, including disaster preparedness, public health planning, environmental conservation, and, ultimately, sustainable development. Alternative population counts include for instance representative household surveys in selected rural areas, such as demonstrated by Boo et al. 39 in the Democratic Republic of Congo, but also reported resettlement from surface mining or large infrastructure projects, such as the data used for validation in this study.
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