I have a series of 2D maps with values ranging
1.0 spread all over. The algorithm that produces them is an application of the fractional Brownian motion. Each map measures
101x101 pixels or cells.
The algorithm producing these maps uses a discretized Inverse Fast Fourier Transform (FFT) in the spectral domain. The maps have a varying
mean but share a fixed variance which is
σ2 = 1. A visual example of such maps is below.
My question: it is said here that applying Moran's I to determine the clustering of a geographic dataset is sensitive to the dataset distribution, especially if the distribution is skewed. Since my algorithm produces maps which distribution is not consistent across all maps, what could be a robust algorithm for determining the clustering of my maps?
The aim is to find a method in which no influence is played by the shape of the distribution on the
p-value of the clustering coefficient. The clustering is of course intended to be referred to the red spots in the image below, which are to be seen as my hotspots.