Year: 2026
Type: preprint
Abstract: Understanding fine-scale population distribution is essential for international development, public health planning, and humanitarian response.This Data Note presents the WorldPop Global2 datasets, a comprehensive set of 100 m and 1 km spatial resolution gridded population estimate datasets, broken down by age and sex classes, and covering 242 countries, dependencies and territories for each year from 2015 to 2030.To construct them, subnational population counts from circa-2010 and 2020 rounds of subnational census data, UN common operational dataset estimates and official projections were assembled for all countries.These were matched to their respective digital subnational boundaries and demographic projection modelling methods were applied to construct a set of more than 11 million administrative unit-based annual estimates for the 2015-2030 period, aligned with the UN World Population Prospects 2024 national estimates.These were then disaggregated to 100 m and 1 km resolution grid square estimates using a newly constructed library of geospatial covariates including satellite-derived building footprints within a machine learning modelling framework.The mean percentage of variance explained across all country and year models
Institutions: University of Southampton, Jade Hochschule, University of Milan
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
Awards: Constructing high spatial resolution population projections and supporting the provision, access and updates of WorldPop spatial demographic datasets, The University of Southampton - to support the creation, storage, and access of WorldPop global demographic data, The University of Southampton - To support improvement of available spatial demographic data and the use of these data for wide benefit in health and development applications.
OpenAlex ID: W7221582398