R and Python both have state of the art mapping tools and libraries that you can either script or use interactively in a notebook to accomplish literally any co

R and Python both have state of the art mapping tools and libraries that you can either script or use interactively in a notebook to accomplish literally any conceivable task using vector or raster data, from a simple choropleth map to geographically weighted regression to analysing satellite imagery using pre-trained models. Making publication-quality maps using point data is absolutely trivial, and there are a number of high-quality learning resources available (https://geo-python.github.io/site/ https://geo-python.github.io/site/, https://automating-gis-processes.github.io/site/ https://automating-gis-processes.github.io/site/) I started working with spatial data on the Mac platform before Python 3 had gained much traction, and I can assure you that Python 3 really didn’t break much to speak of. Every major library and tool works as well as ever, and in most cases is vastly improved.