Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Area of Science:

• Environmental Science
• Demography
• Geospatial Analysis

Background:

• Accurate spatially explicit population data is vital for climate change adaptation, resource management, and sustainable development.
• Existing global gridded population datasets often lack sufficient spatial resolution, particularly for future projections.
• There is a significant need for high-resolution, global-scale future population data to inform critical research and policy decisions.

Purpose of the Study:

• To develop a novel global gridded population dataset with high spatial resolution (approx. 1km) for the period 2020-2100.
• To provide population data at 5-year intervals, enabling temporal trend analysis and future scenario modeling.
• To create a valuable resource for diverse fields including climate change research, resource management, and sustainable development planning.

Main Methods:

• Utilized the Random Forest (RF) algorithm to generate the gridded population data.
• Leveraged the WorldPop dataset as a baseline for the modeling process.
• Ensured quantitative consistency with national population projections from the Shared Socioeconomic Pathways (SSPs).

Main Results:

• Generated a global gridded population dataset covering 248 countries/areas at 30 arc-seconds resolution.
• The dataset provides population estimates at 5-year intervals from 2020 to 2100.
• Validation against the WorldPop dataset at sub-national and grid levels confirmed the dataset's accuracy and reliability.

Conclusions:

• The developed high-resolution global population grid serves as a robust input for predictive research across various scientific disciplines.
• This dataset addresses the existing gap in fine-scale, future global population data.
• The findings support improved modeling and planning for global challenges such as climate change and resource allocation.