The global spatially explicit population grids generated by various methods also include LandScan 12, WorldPop 5, GHS-POP 13, and WPE 14, which have different time spans and spatial resolutions (Table 1). The latest version of it, GPWv4, was released in 2015 11. 10 produced the earliest spatially explicit population grid for the globe with a resolution at 5 arc-minutes in 1997, and this work has been continuously updated by the Center for International Earth Science Information Network (CIESIN) ever since. Since the 1990s, there have been growing attempts to decompose national level population datasets into regular spatially distributed grids 9. Therefore, research on the future trends of global population distribution is a worthy topic for scientists to further explore 7, 8. In addition, it can support studies in multiple fields, such as economic development, resource management, and urban and rural development 3, 4, 5, 6. Global spatially explicit gridded population data is the key to achieving the two-carbon goals (carbon neutrality and peak carbon dioxide emissions) and SDGs. Human activities have contributed to major greenhouse gas emissions and resource consumption at both regional and global levels 2. Global climate change and sustainable development are receiving increasing attention both from researchers and policymakers 1. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020–2100 by implementing Random Forest (RF) algorithm. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields.
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