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

Spatially explicit population datasets can play an important role in climate change, urban planning, resource management and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future population are largely lacking. Based on the open-accessed LandScan datasets from 2010 to 2018 and linear extrapolation model, we present a population dataset covering 233 countries or areas at an approximately 1 km (30 arc-seconds) spatial resolution with 10-year intervals for the period 2020–2100 by implementing two steps: land-use dependence and total constraint. These data are quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population and urbanization projections. Considering the outbreak of COVID-19, we add SSP3-COV scenario to describe the global population distribution affected by the pandemic. The global gridded population projection data are validated by comparing 2010 and 2015 population projection data with the LandScan population data at the sub-national level. The verification results show that our dataset can serve as an input for predictive research in various fields.<br>