https://twitter.com/allixender/status/1325532260171444229
Maus, V., Giljum, S., Gutschlhofer, J. et al. A global-scale data set of mining areas. Sci Data 7, 289 (2020). https://doi.org/10.1038/s41597-020-00624-w
https://www.nature.com/articles/s41597-020-00624-w
https://doi.pangaea.de/10.1594/PANGAEA.910894
Then I generated the ISEA4T DGGS at level 5, ca. 20000 triangles, and saved as GeoPackage. I did this with the help of a little library I am working on:
https://github.com/allixender/dggrid4py/
It uses GeoPandas and DGGRID - a free software program for creating and manipulating Discrete Global Grids. DGGRID version 7.0 was released in September, 2019.
https://www.discreteglobalgrids.org/software/
From GeoPandas I took the lowres_naturalearth dataset to retain only those triangles for the areas of landmass. Then I did a spatial left join (based on intersect) to aggregate the mining polygon data (esp. AREA) into the triangle grid.
Eventually I used GeoViews to provide a static orthographic (say globe) basemap view.
The Jupyter notebook (view here) is provided in my GitHub repo.