This tool can be used to perform nieghbourhood-based (i.e. using roving search windows applied to each grid cell) correlation analysis on two input rasters (`input1` and `input2`). The tool outputs a correlation value raster (`output1`) and a significance (p-value) raster (`output2`). Additionally, the user must specify the size of the search window (`filter`) and the correlation statistic (`stat`). Options for the correlation statistic include `pearson`, `kendall`, and `spearman`. Notice that Pearson's r is the most computationally efficient of the three correlation metrics but is unsuitable when the input distributions are non-linearly associated, in which case, either Spearman's Rho or Kendall's tau-b correlations are more suited. Both Spearman and Kendall correlations evaluate monotonic associations without assuming linearity in the relation. Kendall's tau-b is by far the most computationally expensive of the three statistics and may not be suitable to larger sized search windows.

## See Also

image_correlation, image_regression

## Function Signature

`def image_correlation_neighbourhood_analysis(self, raster1: Raster, raster2: Raster, filter_size: int = 11, correlation_stat: str = "pearson") -> Tuple[Raster, Raster]: ...`

## Project Links

WbW Homepage User Manual Support WbW