Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

Area of Science:

• Geographic Information Systems (GIS) and Spatial Analysis
• Demography and Population Studies
• Sustainable Development and Global Health

Background:

• High-resolution, multi-temporal population data are crucial for understanding sub-national disparities in health, wealth, and resource access.
• Accurate population metrics at sub-national and temporal scales are essential for monitoring progress towards UN Sustainable Development Goals (SDGs).

Purpose of the Study:

• To develop and describe a harmonized, open-access archive of geospatial datasets for constructing detailed population distribution layers.
• To provide globally consistent, annual time series population data suitable for sub-national and temporal analyses.
• To demonstrate the utility of the archive for deriving health and development metrics.

Main Methods:

• Assembled and harmonized a unique archive of open-access geospatial datasets.
• Integrated sub-national census-based population estimates with administrative boundary layers.
• Utilized co-registered gridded geospatial factors correlating with population density for data refinement.
• Developed a production workflow for creating global, annual time series population datasets.

Main Results:

• Created a comprehensive, harmonized geospatial archive of population data.
• Generated multi-temporal gridded population outputs for Africa.
• Demonstrated the application of the archive in deriving health and development indicators.

Conclusions:

• The developed geospatial archive provides a valuable resource for detailed population distribution mapping and analysis.
• The harmonized datasets enable improved measurement and monitoring of SDGs and related global agendas.
• The archive facilitates the derivation of critical health and development metrics at sub-national scales.