I am working with an imbalanced (5%/95%) presence/absence dataset. I've created 25x25 m. raster cells that are categorized '1' or '0'. My Geary's C result (~0.993) and Moran's I result (~0.0045) are each nearly ideal- the expectation for each, respectively, would be 1 and 0 in the absence of spatial autocorrelation. Previous research into these results tells me that this is pretty darn close to random enough.
I also created a spherical semivariogram which displays pretty constant semivariance across all distances, save for a few outliers near the nugget and sparsely dotted across a few other sections. These outliers near the nugget do suggest there may be some autocorrelation present, but I'm really not sure- we're talking perhaps 2-3 dozen dots in relation to about 10,000 others.
My question is, why would my Moran's I and Geary's C statistics indicate very strongly against extant autocorrelation if there truly is some remaining in the data? Should I weigh each result (semivariogram, MI, GC) equally, or trust one result more than the others?