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:
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Using the Concave Hull algorithm available here with the same distance tolerance produces a random polygon with a very pleasing appearance:
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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:
