Hello,
I have a point dataset and I want to make an algorithm that operates like this:
(1) Select a point;
(2) Remove all surrounding points within a distance, entered as a fixed parameter (eventually this can be done using a bufferer + spatial filter);
(3) Store the first initial point in a new dataset and repeat the process for the remaining dataset from step (2) to obtain a second point that will be stored with the first one and so on
The final result is to obtain a point dataset where no distance below a threshold exist ALONG WITH preserving the maximum number possible of points
Thank you very much in advance !
Best answer by ctredinnick
View original
I generated a random set of points (below), it does a good job with it, they're all relatively evenly spaced. It doesn't achieve the goal of preserving the max number of points though. Maybe you can iterate to get a higher number, maybe there is an algorithm to do it, but given that the ShortestPathFinder performs random iterations until it stops improving, I suspect this may require the same approach.
