# Conservative Region Aggregation with Xarray, Geopandas and Sparse

**URL:** <https://discourse.pangeo.io/t/conservative-region-aggregation-with-xarray-geopandas-and-sparse/2715>\
**Category:** Uncategorized\
**Created:** [September 7, 2022, 2:41pm UTC](https://discourse.pangeo.io/t/conservative-region-aggregation-with-xarray-geopandas-and-sparse/2715 "2022-09-07T14:41:26Z")\
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**Author:** ![benbovy](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pangeo.io/benbovy/32/592_2.png) [@benbovy](https://discourse.pangeo.io/u/benbovy)\
**Post date:** [September 8, 2022, 8:14am UTC](https://discourse.pangeo.io/t/conservative-region-aggregation-with-xarray-geopandas-and-sparse/2715/11 "2022-09-08T08:14:09Z")

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If it is acceptable to perform conservative aggregation of arbitrary polygons at a given finite resolution, I think that something based on s2geometry (or H3) cells may offer great speed-ups. Both libraries support large to very fine spatial resolutions (cf. @darothen’s [comment](https://discourse.pangeo.io/t/discrete-global-grid-systems-dggs-use-with-pangeo/2274/22)).

Like xESMF, a solution based on s2geometry or h3 would also introduce some heavy dependencies, but not heavier than GEOS used by shapely.

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