The VARIOGRAM Procedure

Interpretation

For Moran’s I coefficient, upper I greater-than normal upper E left-bracket upper I right-bracket indicates positive autocorrelation. Positive autocorrelation suggests that neighboring values bold-italic s Subscript i and bold-italic s Subscript j tend to have similar feature values z Subscript i and z Subscript j, respectively. When upper I less-than normal upper E left-bracket upper I right-bracket, this is a sign of negative autocorrelation, or dissimilar values at neighboring locations. A measure of strength of the autocorrelation is the size of the absolute difference bar upper I minus normal upper E left-bracket upper I right-bracket bar.

Geary’s c coefficient interpretation is analogous to that of Moran’s I. The only difference is that c greater-than normal upper E left-bracket c right-bracket indicates negative autocorrelation and dissimilarity, whereas c less-than normal upper E left-bracket c right-bracket signifies positive autocorrelation and similarity of values.

The VARIOGRAM procedure uses the mathematical definitions in the preceding section to provide the observed and expected values, and the standard deviation of the autocorrelation coefficients in the autocorrelation statistics table. The Z scores for each type of statistics are computed as

upper Z Subscript upper I Baseline equals StartFraction upper I minus normal upper E left-bracket upper I right-bracket Over StartRoot normal upper V normal a normal r left-bracket upper I right-bracket EndRoot EndFraction

for Moran’s I coefficient, and

upper Z Subscript c Baseline equals StartFraction c minus normal upper E left-bracket c right-bracket Over StartRoot normal upper V normal a normal r left-bracket c right-bracket EndRoot EndFraction

for Geary’s c coefficient. PROC VARIOGRAM also reports the two-sided p-value for each coefficient under the null hypothesis that the sample values are not autocorrelated. Smaller p-values correspond to stronger autocorrelation for both the I and c statistics. However, the p-value does not tell you whether the autocorrelation is positive or negative. Based on the preceding remarks, you have positive autocorrelation when upper Z Subscript upper I Baseline greater-than 0 or upper Z Subscript c Baseline less-than 0, and you have negative autocorrelation when upper Z Subscript upper I Baseline less-than 0 or upper Z Subscript c Baseline greater-than 0.

Last updated: December 09, 2022