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。目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较为匮乏。本研究以WorldPop数据集（WorldPop）为基础，采用随机森林（Random Forest, RF）算法，构建了一套覆盖全球248个国家及地区的网格化人口数据集：该数据集空间分辨率为30角秒（约1千米），时间跨度为2020至2100年，时间间隔为5年。本数据集与共享社会经济路径（Shared Socioeconomic Pathways, SSPs）中的国家级人口数据在定量层面保持一致。本研究所预测的空间显式人口网格，通过在次国家级与网格级两个尺度上与WorldPop数据集开展对比验证。本次验证共涵盖3569个省级行政区（覆盖全球绝大多数省级单元）与超过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。

搜集汇总

背景与挑战

背景概述

该数据集提供2020-2100年全球1公里网格的高分辨率人口分布预测，基于WorldPop数据采用随机森林算法生成，并与SSPs国家人口数据保持一致，经过大规模次国家和网格级别验证，适用于气候变化、资源管理等多领域研究。