I am a GIS beginner. Usually we use Moran's I to evaluate spatial autocorrelation. Everyone knows the Moran's I coefficient ranges from -1 (uniform) to 1 (clustering); I = 0
means no correlation.
If I = 0.2, 0.3, 0.4
(suppose I
is statistically significant; i.e. p-value is small, like p < 0.05
), do we say there exists spatial correlation?
How large I coefficient we claim strong
spatial correlation empirically?
Or we should proceed to run a spatial regression model, and then observe how significant on lag y or lag error coefficients to make conclusion?
Best Answer
I myself am still learning as much as I can about Moran's I, but I think I help figure out the answer to this question. There is a great video on coursera about spatial correlation:
So like Frank mentioned you need to calculate a Z-score. Now to calculate the Z-score you need the mean which for Moran's I
-1/(N -1)
where N is the number of samples. This number serves as a baseline for what your correlation values should be like.From what I have read about spatial correlation generally most people either choose p-value of .10 or .05 to say that the autocorrelation is statistically significant. In the quote above the professor considers using a p-value of .05 for statistical significance, while in ARGIS's documentation you will find they use a p-value of .10.
Because this is slightly subjective, I have reproduced a more detailed table for Z-scores to P-values to Confidence Intervals for the Z-test.
Here is a brief table for Z-score assuming its just the basic Z-test:
P.S. I also learned a little myself, as I thought that strength rules for spatial correlation matched the strength rules for correlation (I >.8 being the very strong relationship and .6 < weak relationship ). Though Moran's I is a weighted Pearson correlation, it not true that you can interpret the values similar to regular correlations when you compare. Like Jeffery Evans mentioned, you need to consider the p and z-values to test statistical significance to really interpret the spatial autocorrelation because tails represent a different spatial process (vs. regular correlation). According to Yanguang Chen spatial auto-correlation is only one piece figuring the spatial relationship between two variables, you need to consider the spatial cross-correlation. In fact, the Pearson Correlation between any two spatial variables is the combination of the direct correlation and this spatial-cross correlation.