This is due to an arithmetic robustness issue in the computation of the
centroid location. The polygon centroid algorithm implemented in JTS
involves a division by the area of the polygons. In this case the area is
very, very small, and hence leads to the classic loss-of-precision problem
when dividing by small numbers in finite-precision floating point. The
loss of precision is so great in this case that the computed point lies far
away from the true value.
There's probably a clever way to rewrite the calculation to avoid the loss
of precision issue, but I don't know of a way to do this at this point.
Ideas are welcome.
I note that computing the centroid of the individual polygons produces an
accurate result. The problem is having several very small polygons spread
over a large area. There might be a way to compute a weighted average of
the individual centroids, which would probably produce a better result.
On Thu, Oct 31, 2013 at 10:54 AM, Daniel Baston <[email protected]>wrote:
> Hello,****
>
> ** **
>
> JTS 1.13 is reporting what seems to be a curious result when computing the
> centroid of this tiny geometry:****
>
> ** **
>
> MULTIPOLYGON (((-92.661322 36.58994900000003, -92.66132199999993
> 36.58994900000005, -92.66132199999993 36.589949000000004, -92.661322
> 36.589949, -92.661322 36.58994900000003)), ((-92.65560500000008
> 36.58708800000005, -92.65560499999992 36.58708800000005, -92.65560499998745
> 36.587087999992576, -92.655605 36.587088, -92.65560500000008
> 36.58708800000005)), ((-92.65512450000065 36.586800000000466,
> -92.65512449999994 36.58680000000004, -92.65512449998666 36.5867999999905,
> -92.65512450000065 36.586800000000466)))****
>
> ** **
>
> The centroid result I receive in code and in JTS TestBuilder is as follows:
> ****
>
> ** **
>
> POINT (-287.27272727272725 153.3090909090909)****
>
> ** **
>
> Any thoughts on what might be causing this?****
>
>
> Thanks,****
>
> Dan****
>
>
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