The Voronoi polygon algorithm propagates the polygons to the extent of the input layer (the points), which means some massive polygons. The majority of those massive polygons are nonsense data - we can't have any confidence that the values there are reasonable just because the value at a point a long way away is X (and that happens to be the nearest point sampled).
- set a single universal maximum distance from node point, so the polygons would be rounded out instead of continue.
- set a dynamic maximum distance based on nearby points... but I suspect that'd get complicated quick.
I'm not sure how to do this though. The Voronois are created by an algorithm in QGis, using the points as input. But the resulting voronoi polygons layer doesn't know where the points are. There doesn't look to be an option for this in the algorithm (Python function), sadly.
Possibly I could attach the input points to the output Voronoi somehow, to act as centroids for step 2. Step 2: apply some kind of buffering algorithm to each polygon, i.e. "set extent to be the current bounds or a circle of Y radius, whichever's smallest.
Has anyone ever done this?
Voronois being untrustable at a certain projection distance seems like something others would have had to deal with.
This picture shows points around Florida, USA. Easy to remove the centre by masking land, but I need ocean points to append to a grid and lots of them are going to be unreasonable/wrong.