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>

空间显式人口数据集在气候变化、城市规划、资源管理等领域具有重要应用价值。目前已存在多套格网人口数据集，但全球范围的人口数据，尤其是未来人口高分辨率数据仍存在显著缺口。本研究基于2010至2018年开放获取的LandScan数据集与线性外推模型，通过土地利用依赖校正与总量约束两步法，构建了一套覆盖233个国家及地区、空间分辨率约1公里（30角秒）、时间步长为10年的2020-2100年人口数据集。该数据集在量化层面与共享社会经济路径（Shared Socioeconomic Pathways, SSPs）的国家人口及城市化预测结果保持一致。考虑到新冠疫情（COVID-19）的暴发，本研究额外增设SSP3-COV情景，用以刻画受疫情影响的全球人口分布格局。本研究通过将2010年、2015年的人口预测数据与次国家级尺度的LandScan人口数据进行对比，对全球格网人口预测数据开展了验证。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源。