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Background for the Model:

https://stats.stackexchange.com/questions/268201/how-to-increase-mcfadden-score-and-choose-proper-predictor-for-binary-logistic-r

Data I have:

  1. Forest loss points
  2. Forest boundary
  3. Randomly selected non loss points

(1 & 3 are response variable)

What I need: I need to minimize the effect of spatial autocorrelation in the regression. For this purpose I need to take points while there is least autocorrelation among the taken points, I think.For this purpose I need to take grids, I think, represent autocorrelation among grid-inside points and least among the points of different grids.So now I need creating those grids for which I need to know the dimension of the grids.

How can I create those grids.

What I have tried so far: I generated square grids at different spacings and spatially aggregated points by those grids. Finally I ran incremental spatial autocorrelation among those grids at different increment steps but this does not give me a peak point what I would take as grid dimension. Another problem this process creates it gives me a peak point at ~25000m but if I take this distance as the grid dimension then the grid number will be too small and thereby the sampled points would be also too small for fitting the logistic regression.

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