I am working with an imbalanced (5%/95%) presence/absence dataset. I'vecreated 25x25 m. raster cells that are categorized '1' or '0'. My Geary's Cresult (~0.993) and Moran's I result (~0.0045) are each nearly ideal- theexpectation for each, respectively, would be 1 and 0 in the absence ofspatial 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 constantsemivariance across all distances, save for a few outliers near the nuggetand sparsely dotted across a few other sections. These outliers near thenugget do suggest there may be some autocorrelation present, but I'm reallynot sure- we're talking perhaps 2-3 dozen dots in relation to about 10,000others.
My question is, why would my Moran's I and Geary's C statistics indicatevery strongly against extant autocorrelation if there truly is someremaining in the data? Should I weigh each result (semivariogram, MI, GC)equally, or trust one result more than the others?
Any insight would be sincerely appreciated.
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I also created a spherical semivariogram which displays pretty constantsemivariance across all distances, save for a few outliers near the nuggetand sparsely dotted across a few other sections. These outliers near thenugget do suggest there may be some autocorrelation present, but I'm reallynot sure- we're talking perhaps 2-3 dozen dots in relation to about 10,000others.
My question is, why would my Moran's I and Geary's C statistics indicatevery strongly against extant autocorrelation if there truly is someremaining in the data? Should I weigh each result (semivariogram, MI, GC)equally, or trust one result more than the others?
Any insight would be sincerely appreciated.
أكثر...