I have some observations collected at sea, and that we managed to classify in 2 clusters (blue and red), based on their properties. As you see in my example below, when projected, the classification looks as "spatially coherent", or at least, clusters don't look like randomly distributed. I'm looking for an statistic that tells about this spatial coherence, for each class, or for the full classification. I have seen examples in PYSAL or ESDA modules, but none with this type of data, a two-dimensional labeled array (1 and 2 values) with missing data (zero values). I don't know how to proceed.
This is the code example:
import matplotlib as mpl
import matplotlib.pyplot as plt
# DATA EXAMPLE
# I have a regular grid, with not-sampled (d==0) and sampled (d>0) areas.
# Sampled areas were classified as '1' and '2', based on some measurements
# that we collected at each location.
d = [[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0],
[1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2],
[0, 1, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2],
[0, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2],
[0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2]]
# VISUAL
# class 1 (blue) and class 2 (red)
cmap = mpl.colors.ListedColormap(['w','b','r'])
plt.pcolormesh(d, cmap=cmap)
That's what you see when running the example:
Any advice on how to proceed? Thanks in advance!
