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Subject: Re: [geometry] rtree nearest polygon to a point
From: Adam Wulkiewicz (adam.wulkiewicz_at_[hidden])
Date: 2014-12-02 18:25:58


Andrew Hundt wrote:
> On Mon, Dec 1, 2014 at 9:01 PM, Adam Wulkiewicz
> <adam.wulkiewicz_at_[hidden] <mailto:adam.wulkiewicz_at_[hidden]>> wrote:
> Hi Andrew,
> Andrew Hundt wrote:
> I'm considering using rtree to store a set of triangles (I
> will use polygons), and query for the closet triangle to a
> point. Are there any examples I could use to help with setting
> up the appropriate objects and types?
> You could store pairs of triangle bounding box and index and then
> iterate over the nearest boxes using iterative nearest query and
> check distance to corresponding triangles. If the distance to the
> next box was greater than to the nearest triangle or the distance
> to the triangle was equal to 0 you could break the loop.
> Hmm, I'm implementing iterative closest point for sensor data
> registration. Most of the time the distance won't be 0, but typically
> the next box will be greater. There would be some strange corner cases
> however if I only search for a few results and keep increasing the
> size... Maybe the current rtree implementation isn't the right tool
> for this since I can't directly check for the closest polygon?

Any spatial index (or space partinioning data structure) would do it the
same way. Indexing would be done using some bounding regions, not
polygons, for fast pruning. Then the searching for the nearest Polygon
would be done similar way as I described above. If the data structure
allowed to store Polygons the above procedure would be done internally.

What do you mean by "keep increasing the size"? I didn't mention it
explicitly but you could pass rtree.size() or some big number (as
k-number of nearest elements) to the nearest predicate passed to the
qbegin(). Then the query iterator would iterate over all of the elements
stored in the rtree, the nearest ones first. You then would be able to
stop iteration at some point - after it'd be not possible to find a
closer Polygon, when a bounding box was further than the nearest
distance. So if the data was sparsely distributed it'd require 2 iterations.


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