Global 1 km-grid population distributions dataset from 2020 to 2100

Spatially explicit population grid 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 5-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 research is validated by comparing it with the WorldPop dataset both at the sub-national level and grid level. 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.

空间显式人口网格（spatially explicit population grid）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。当前已有多款网格化人口数据集问世，但全球尺度的人口数据，尤其是面向未来的高分辨率人口数据仍极度匮乏。本研究基于WorldPop数据集，采用随机森林（Random Forest, RF）算法，构建了一套覆盖全球248个国家及地区的全球网格化人口数据集，其空间分辨率为30弧秒（约1公里），时间跨度为2020年至2100年，时间间隔为5年。本数据集与共享社会经济路径（Shared Socioeconomic Pathways, SSPs）的国家级人口数据在定量层面保持一致。本研究预测的空间显式人口网格，通过在次国家级与网格单元两个层面与WorldPop数据集进行比对，完成了精度验证。本次验证共纳入全球3569个省级行政区（几乎覆盖全球所有省级行政单元）以及超过48万个网格单元，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。