I am new to calculating these values and am having a hard time figuring out how to calculate a (global?) Moran's I value for an increasing neighbour distance between points. Specifically, I'm not really sure how to set this lag/neighbour distance so that I can plot a correlogram.
The data I have is for the variation of single parameter in a 2D list (matrix). This can be plotted simply as a colorplot where the axes represent the points/pixels in each direction of the image, and the colormap shows the value of this parameter for each box across the 2D surface. As they seem to be clumping, I would like to see how long this 'parameter clump length' is using a correlogram.
So far I have managed to create another colorplot which I don't know exactly how to interpret.
y = 2D_Array
w = pysal.lat2W(rows,cols,rook=False,id_type="float")
lm = pysal.Moran_Local(y,w)
moran_significance = np.reshape(lm.p_sim,np.shape(ListOrArray))
plt.imshow(moran_significance)
I have also managed to obtain the global Moran I value by converting the 2D_Array into a 1D list.
y = 1D_List
w = pysal.lat2W(rows,cols)
mi = pysal.Moran(y,w,two_tailed=False)
But what I am really looking for is, how does I change when looking at how the parameter changes for neighbour n = 1,2,3,4,... where n = 1 is the nearest neighbour and n = 2 is the next nearest, and so on. Here is an example of what I'd like: https://creativesciences.files.wordpress.com/2015/05/morins-i-e1430616786173.png
