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Since you cannot really define contingency based on common boundaries (using something like spdep::poly2nb), you could use the polygon centroids to build a k nearest neighbor relationship. This will unfortunately not account for polygon size but is a good place to start. require(spdep) require(rgdal) polys <- readOGR(system.file("etc/shapes/", ...


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May be duplicated. This is open java source for Delaunay Triangulation and interpolation


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You cannot use standard point pattern statistics because the assumed spatial process is being constrained to a linear process. The assumption of a Poisson distributed Complete Spatial Randomness (CSR) does not hold. The entire problem becomes one-dimensional and expectations based on area need to be redefined to distance thus, the single dimension of the ...


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As @whuber states: "Unless the study region was determined a priori, this "area" input is arbitrary, making the tool practically worthless--and even deceiving". This is advice work heeding. The nearest neighbor index is the ratio of the observed and expected mean neighbor distances. The expected is a function of the area and the number of observations. ...


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You should look at the output. In the toolbox window click on the results tab at the bottom (and if necessary, uncollapse the Average Nearest Neighbor entry). The NNI ratio, p value, expected and observed are all reported. You need to interpret the actual statistic and not rely in ESRI's GUI interpretation. A random or uniform distribution would be near ...



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