R

I am sitting on a train back to Prague after two days of discussing tooling for spatial data science available in the Python, R and Julia ecosystems, with occasional excursions to the worlds of Rust, JavaScript or ESRI. I am coming back from the Spatial Data Science across Languages (SDSL) workshop and I’d like to share a few thoughts1 while they’re fresh. Different maturity of ecosystems As a Python developer, I must admit that what the R-Spatial community managed to create is impressive and is in some aspects further that where we are. The most notable is the support of spherical geometries. Where geopandas warns that a computation of area or a distance in geographic (longitude, latitude) coordinates is incorrect (more on this behavior below), {sf} returns a correct value. The same support is on the geopandas roadmap but it will not be done by tomorrow.2 Same could be said about the extent of support of spatial statistics but that is the same as with any statistics. R was built for it. ...