Geary's C

Geary's C is a measure of spatial autocorrelation that attempts to determine if observations of the same variable are spatially autocorrelated globally (rather than at the neighborhood level).

is the number of spatial units indexed by

Values significantly lower than 1 demonstrate increasing positive spatial autocorrelation, whilst values significantly higher than 1 illustrate increasing negative spatial autocorrelation.

Geary's C is inversely related to Moran's I, but it is not identical.

While Moran's I and Geary's C are both measures of global spatial autocorrelation, they are slightly different.

Geary's C uses the sum of squared distances whereas Moran's I uses standardized spatial covariance.

By using squared distances Geary's C is less sensitive to linear associations and may pickup autocorrelation where Moran's I may not.

[3] Like Moran's I, Geary's C can be decomposed into a sum of Local Indicators of Spatial Association (LISA) statistics.

LISA statistics can be used to find local clusters through significance testing, though because a large number of tests must be performed (one per sampling area) this approach suffers from the multiple comparisons problem.

As noted by Anselin,[4] this means the analysis of the local Geary statistic is aimed at identifying interesting points which should then be subject to further investigation.

is given by[5] where then, Local Geary's C can be calculated in GeoDa and PySAL.

Geary's C statistic computed for different spatial patterns. Using ' rook ' neighbors for each grid cell, setting for neighbours of and then row normalizing the weight matrix. Top left shows gives indicating anti-correlation. Top right shows a spatial gradient giving indicating correlation. Bottom left shows random data giving a value of indicating no correlation. Bottom right shows a spreading pattern with positive autocorrelation.