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7

You can check the source code for the Nearest Neighbour Analysis tool from GitHub. More specifically, the following lines of code which shows how the different parameters are calculated: do = float(sumDist) / count de = float(0.5 / math.sqrt(count / A)) d = float(do / de) SE = float(0.26136 / math.sqrt(( count ** 2) / A)) zscore = float((do - de) / SE) ...


1

As you suggested in a comment to the question, convert each enrollment value in the GDB to an enrollment density by dividing it by the block's area. Convert this layer to a grid. Compute its focal sum using a 5 km radius. Multiply that, if you like, by the area of each grid cell (equal to the square of the cellsize). Notice that this cancels out the ...


3

I would suggest reading up on spatial statistics. In particular, I would look at spatial autocorrelation and clusters. The ArcGIS Resource page on the Spatial Statistics toolbox might be a good place to start.


2

You could have a look at the RasterStats plugin. It can compute histograms for zones (I don't know in what format, though), which could help you finding how much of each class is located in each zone. There are other zonal statistics plugins (ZonalStats, Zonal statistics), but I don't think they can give accurate enough statistics for what you're trying to ...


2

I am curious as to why you are not approaching this problem using a point pattern analysis? It is apparent that you are after a multiscale comparision but, it is not clear as to what end or what type of supported inference would be made. The type of standardization that your are attempting is hinting that a PPA would be a more supported methodology. ...


0

I also made a quick easy and holistic tutorial you folks can check out here https://github.com/mattjbayly/MapsProj. All you need is the R text script and all other material is downloaded remotely from R. My tutorial covered GIS data imports/exports, basic manipulation of vector & raster data, some of basic plotting and a brief overview of projections. ...



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