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If your data is in fact binary, please look at the math behind these statistics. Nither Moran's or Gearys are appropriate for binary data thus nullifying your results. For this problem you are somewhat limited to a joins-count statistic.


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Here are some functions to solve this issue. I use the "Cumulative Proportion" as a guide how many local principal components to keep. Just like global PCA, I define the percentage of variance and then select the local componets which cumulatively accounts for 85% and more variance on example data we would like to keep. cum..prop.var <- ...


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If you would like to calculate GWR in R, you should try GWmodel. If you need to do it in Python, you can also use pygwr. GWmodel contains many geographically-weighted (GW) models including gwr (GW regression), gwpca(GW principal components analysis), gwda(GW Discriminant Analysis), gwr.generalised(Generalised GWR models, including Poisson and Binomial), ...


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Based especially on your later description of how coverage generally works, you might be interested in computing the minimum bounding geometry for your point data. This will generate a fairly conservative estimate of your AIS coverage (in that it will probably be an understatement of the actual coverage) than your cell based aproach, but it will guarantee ...


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Spatial autocorrelation does look at whether two phenomena have similar spatial distribution, however I believe what you want goes a bit beyond that. You're looking at a regression analysis, which explores correlation between several independent or explanatory variables and an occurrence (dependent variable). In your case, why is this disease outbreak ...



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