Let's assume a crowd-sourced tree location use case where a research team asks hikers to GPS the location of a specific tree type in case they encounter it.
This will lead to a large number of longitude-latitude points each identifying the measured (noisy) GPS location of a tree. The researchers would now like to compute from those measurements a map of those trees.
That means they have to differentiate location point estimates for close trees and after assigning the estimates to a specific tree calculate the location of that tree.
Now trees don't move and GPS measurements can be pretty inacurate. What clustering algorithm would be the canoncial choice for this application?
(If you have an R package::function to suggest please tell me)