It would sure be better if it had some concrete explanations and examples why vincenty is preferred. It's also barely any more computational load on modern computers so very little reason not to use it.
Haversine is strongly deprecated in even short distance applications like planning a 35 km point to point licensed band radio microwave link, because you risk having both slightly the wrong distance and the azimuth figure being off by a tenth of a degree.
Vincenty in python form is quite compact. Karney by comparison is something like 1000 lines of code. And the difference is going between like a few mm precision to nanometers.
I've actually implemented Haversine in a SQL query to find what can be summed up as "which of these moving things is closest to this thing right now". It's not the tidiest, and I had to ask someone better at SQL than I am to make it entirely work, but it looks like it is easier than doing Vincenty.
I should try it, right?
Edit: I also wonder what's to be gained by being accurate to nanometres over the surface of a fairly inaccurate planet.
My application was to locate movable things to identify which was nearest to a moorland wildfire, where you need to be accurate to within about 1km at best - you don't need to drop the pin right on it, you'll know where you're going when you see it ;-)
For that application you needn't have bothered. On a sphere arc-length and Euclidean distance are monotonically related, so the minimizer would not have changed.
Haversine is strongly deprecated in even short distance applications like planning a 35 km point to point licensed band radio microwave link, because you risk having both slightly the wrong distance and the azimuth figure being off by a tenth of a degree.
Vincenty in python form is quite compact. Karney by comparison is something like 1000 lines of code. And the difference is going between like a few mm precision to nanometers.