alt Hacker NewsFor what it's worth, you _can't_ use spherical approaches for most data. They're only used for points, in practice. Your spatial data is inherently stored/generated in ways that don't allow spherical approaches as soon as you start working with polygons, let alone things like rasters.
Yes, spherical representations of polygon data exist, but the data you import has already been "split" and undoing that is often impossible, or at best non-trivial. And then rasters are fundamentally impossible to represent that way.
Analysis uses projections for that reason. Spherical approaches aren't fundamentally "better" for most use cases. They're only strictly better if everything you're working with is a point.
There's more to geospatial than point datasets.
A good point. Certainly for raster analysis it doesn't make sense.
But any type of vector data could be modeled on a sphere, right? Points, shapes, lines. And I saw "better" because even the best suited projection will have some small amount of distortion.
Either way, most things use planer geometry so projections are necessary, and you need to have some understanding of how all that works