Spatial Analysis - Identifying Regions with Common Boundaries

I have the shape file for DC PSA's as defined in 2012 (see below). I would like to be able to identify in semi-automatic fashion which PSA's are neighbors - that is, which PSA's are nearest neighbors to one another (for example, 201 and 401 are nearest neighbors).

With this information, one could define a sort of spatial distance metric between PSA \$i\$ and PSA \$j\$, call it \$d(i,j)\$, populating a distance matrix, call it \$D\$. Then \$d(i,j)=0\$ is pure autocorrelation (it is the diagonal of \$D\$). If \$d(i,j)=1\$, you have nearest neighbor spatial autocorrelation. One can then handle \$d(i,j)>1\$ separately. With certain physical systems this seems like a simplistic but natural way of thinking about distance.

Does anyone know of a simple way to be able to calculate \$d(i,j)\$ as define here, without doing it manually?

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migrated from stats.stackexchange.comOct 17 '13 at 16:03

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