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Currently some functions (notably the generate_arcs and sparse_selection functions) work on the data using its raw longitude and latitude.
However, the results of these functions depend on some minimum and/or maximum distance. They don't always give nice results for non-Euclidean coordinate systems (e.g. where the axes are not straight or normalized to each other).
This can be solved by adding coordinate transform functionality. This way (global) arc coordinates can be transformed into (local) Euclidean coordinates before performing functions that prefer Euclidean metrics.
Currently some functions (notably the generate_arcs and sparse_selection functions) work on the data using its raw longitude and latitude.
However, the results of these functions depend on some minimum and/or maximum distance. They don't always give nice results for non-Euclidean coordinate systems (e.g. where the axes are not straight or normalized to each other).
This can be solved by adding coordinate transform functionality. This way (global) arc coordinates can be transformed into (local) Euclidean coordinates before performing functions that prefer Euclidean metrics.
A recommended approach would be to use GeoPandas coordinate transforms internally
geopandas.GeoDataFrame.to_crs — GeoPandas
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