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

Spatially explicit population grids can play an important role in climate change, resource management, sustainable development, and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations, are largely lacking. 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 five-year intervals for the period 2020–2100 by implementing random forest (RF) algorithm. Our dataset is quantitatively consistent with the shared socioeconomic pathways (SSPs) national population. The spatially explicit population grid we predicted in this study is validated by comparison with the WorldPop dataset at both the sub-national and grid level. A total of 3,569 provinces (almost all provinces on the globe) and more than 480 thousand grids are verified, and the results show that our dataset can serve as an input for predictive research in various fields.空间显式人口网格（spatially explicit population grids）在气候变化、资源管理、可持续发展等诸多领域均可发挥重要作用。当前已存在多款格网数据集，但全球尺度的人口数据，尤其是未来人口高分辨率格网数据仍较为匮乏。本研究基于WorldPop（世界人口数据集），采用随机森林（RF）算法，构建了一套覆盖248个国家/地区的全球格网人口数据集，其空间分辨率为30角秒（约1千米），时间跨度为2020至2100年，时间间隔为5年。本数据集的人口数值与共享社会经济路径（SSPs）下的国家人口统计数据保持定量一致性。本研究预测得到的空间显式人口网格，通过在次国家级与格网级两个维度与WorldPop数据集进行对比，完成了精度验证。本次验证共覆盖全球3569个省级行政区（几乎涵盖全球所有省级行政单元）以及超过48万个格网单元，验证结果表明，本数据集可作为多领域预测研究的输入数据。