# How to interpret spatial random permutations against the observed value of the statistic (PySAL)

I have a map for which I calculate the value of the Global Moran's I. With the use of PySAL, there is also an option to do a computational inference. The result of such analysis is shown below.

From the videos I've seen and some papers I've read, what I understood from this is that random spatial permutations work like this:

1. I take my original map, shuffle the arrangement of the polygons
2. For each iteration, calculate the Moran's statistic
3. Do N times
4. Fit a curve to see the distribution of the simulated statistics
5. compare with the observed value of the statistic

In the figure above, my observed statistic is far to the right of the reference distribution.

Does this mean that, by simulating randomness, none of the configurations were able to come up with a value similar to that of the observed statistic? If so, does this mean that my map presents non-random patterns? Or does it mean that it is not probable to even have such a configuration/pattern?

Thanks for any info on this!