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Since you cannot really define contingency based on common boundaries (using something like spdep::poly2nb), you could use the polygon centroids to build a k nearest neighbor relationship. This will unfortunately not account for polygon size but is a good place to start. require(spdep) require(rgdal) polys <- readOGR(system.file("etc/shapes/", ...


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The Shapely distributions I am making for OS X (https://pypi.python.org/pypi/Shapely#downloads) have GEOS included and you won't have to think about library paths at all. If you're using Python 2.7, 3.4, or 3.5 and OS X 10.6+, pip install shapely is the best way to get it.


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You have to point your system to where the GEOS Framework is located. See this post: DYLD_LIBRARY_PATH="/Library/Frameworks/GEOS.framework/Versions/3/unix/lib" export DYLD_LIBRARY_PATH If it works, add it to your $PATH!



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