Wednesday 15 July 2015

Even-distribution Random Point and Polygons in JTS

Recently I fixed the JTS KD-Tree implementation so that it works as advertised with a distance tolerance to provide point snapping.  This gives a fast way to produce random point fields with even distribution (i.e. no points too close together).

First, generate a batch of random points using RandomPointsBuilder.  As is well known, this produces a very "lumpy" distribution of points:

Then, put them in a KD-Tree using a snapping distance tolerance.  Querying all points in the final tree produces a nice even distribution of points:

Using the Concave Hull algorithm available here with the same distance tolerance produces a random polygon with a very pleasing appearance:
I suspect that these kinds of polygons might be useful for generating stress tests for geometric algorithms.

UPDATE: Adding a bit of Bezier Smoothing produces an even cooler-looking polygon: