Estimating the boundary of arbitrarily distributed data
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Published on 2010-05-18T10:07:29Z
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I have two dimensional discrete spatial data. I would like to make an approximation of the spatial boundaries of this data so that I can produce a plot with another dataset on top of it.
Ideally, this would be an ordered set of (x,y) points that matplotlib can plot with the plt.Polygon() patch.
My initial attempt is very inelegant: I place a fine grid over the data, and where data is found in a cell, a square matplotlib patch is created of that cell. The resolution of the boundary thus depends on the sampling frequency of the grid. Here is an example, where the grey region are the cells containing data, black where no data exists.
OK, problem solved - why am I still here? Well.... I'd like a more "elegant" solution, or at least one that is faster (ie. I don't want to get on with "real" work, I'd like to have some fun with this!). The best way I can think of is a ray-tracing approach - eg:
- from xmin to xmax, at y=ymin, check if data boundary crossed in intervals dx
- y=ymin+dy, do 1
- do 1-2, but now sample in y
An alternative is defining a centre, and sampling in r-theta space - ie radial spokes in dtheta increments.
Both would produce a set of (x,y) points, but then how do I order/link neighbouring points them to create the boundary?
A nearest neighbour approach is not appropriate as, for example (to borrow from Geography), an isthmus (think of Panama connecting N&S America) could then close off and isolate regions. This also might not deal very well with the holes seen in the data, which I would like to represent as a different plt.Polygon.
The solution perhaps comes from solving an area maximisation problem. For a set of points defining the data limits, what is the maximum contiguous area contained within those points To form the enclosed area, what are the neighbouring points for the nth point? How will the holes be treated in this scheme - is this erring into topology now?
Apologies, much of this is me thinking out loud. I'd be grateful for some hints, suggestions or solutions. I suspect this is an oft-studied problem with many solution techniques, but I'm looking for something simple to code and quick to run... I guess everyone is, really!
Cheers,
David
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