Faisal Abdullah Al, Kaye Maxwell, Ahmed Maimoonah, Galbraith Eric D
Department of Earth and Planetary Sciences, McGill University, Montreal, Québec, H3A 0E8, Canada.
Department of Mathematics and Statistics, McGill University, Montreal, Québec, H3A 0B9, Canada.
Sci Data. 2025 May 12;12(1):775. doi: 10.1038/s41597-025-05087-5.
Human activities such as food production, mining, transportation, and construction have extensively modified Earth's land and marine environments, causing biodiversity loss, water pollution, soil erosion, and climate change. However, studying spatial aspects of the relationships that link the global human system with non-human parts of the Earth-system is hampered by data fragmentation. Here we present the Surface Earth System Analysis and Modeling Environment (SESAME) Human-Earth Atlas, which includes hundreds of variables capturing both human and non-human aspects of the Earth system on two common spatial grids of 1- and 0.25-degree resolution. The Atlas is structured by common spheres, and many variables resolve changes over time. Machine learning is used selectively to interpolate data in undersampled regions. Many of the national-level tabular human system variables are downscaled to spatial grids using dasymetric mapping, accounting for country boundary changes over time. Raster, point, line, polygon, and tabular jurisdictional (i.e., country) data were mapped onto a standardized spatial grid at the desired resolution. The Atlas facilitates data discovery and modeling of human-Earth system dynamics.
诸如粮食生产、采矿、运输和建设等人类活动已对地球的陆地和海洋环境造成了广泛改变，导致生物多样性丧失、水污染、土壤侵蚀和气候变化。然而，将全球人类系统与地球系统的非人类部分联系起来的关系的空间方面研究，因数据碎片化而受到阻碍。在此，我们展示了地表地球系统分析与建模环境（SESAME）人类-地球地图集，其中包含数百个变量，这些变量在1度和0.25度分辨率的两个常见空间网格上捕捉了地球系统的人类和非人类方面。该地图集按共同领域构建，许多变量解析随时间的变化。机器学习被选择性地用于在采样不足的区域内插补数据。许多国家级表格形式的人类系统变量通过使用密度制图法下采样到空间网格，同时考虑了国家边界随时间的变化。栅格、点、线、多边形和表格形式的管辖（即国家）数据被映射到所需分辨率的标准化空间网格上。该地图集有助于人类-地球系统动态的数据发现和建模。
