Help interpreting Moran’s I and Geary’s C results

How do you interpret Moran’s I results?

If the values in the dataset tend to cluster spatially (high values cluster near other high values; low values cluster near other low values), the Moran’s Index will be positive. When high values repel other high values, and tend to be near low values, the Index will be negative.

What is the interpretation of the Moran’s and Geary’s test statistics?

The Moran’s I and Geary’s c statistics are calculated for 50-mile increments from 50 to 250 miles. For each of these increments, the Geary’s c is less than 1, and the Moran’s I is greater than the expected value. These results indicate that there is positive spatial autocorrelation.

What is the difference between Moran I and Geary C?

Moran’s I is a measure of global spatial autocorrelation, while Geary’s C is more sensitive to local spatial autocorrelation. Geary’s C is also known as Geary’s contiguity ratio or simply Geary’s ratio.

What does Moran’s I tell you?

What is Moran’s I? Moran’s I is a correlation coefficient that measures the overall spatial autocorrelation of your data set. In other words, it measures how one object is similar to others surrounding it. If objects are attracted (or repelled) by each other, it means that the observations are not independent.

How do you interpret autocorrelation values?

Testing for Autocorrelation



Values closer to 0 indicate a greater degree of positive correlation, values closer to 4 indicate a greater degree of negative autocorrelation, while values closer to the middle suggest less autocorrelation.

How do you interpret positive autocorrelation?

Similar to correlation, autocorrelation can be either positive or negative. It ranges from -1 (perfectly negative autocorrelation) to 1 (perfectly positive autocorrelation). Positive autocorrelation means that the increase observed in a time interval leads to a proportionate increase in the lagged time interval.
 

What does positive spatial autocorrelation mean?

Positive spatial autocorrelation means that geographically nearby values of a variable tend to be similar on a map: high values tend to be located near high values, medium values near medium values, and low values near low values.

Is spatial autocorrelation good or bad?

“Everything is related to everything else, but near things are more related than distant things.” Positive spatial autocorrelation is when similar values cluster together on a map. Negative spatial autocorrelation is when dissimilar values cluster together on a map.
 

How do you assess spatial autocorrelation?

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What does this spatial autocorrelation report indicate?

Spatial autocorrelation indicates if there is clustering or dispersion in a map. While a positive Moran’s I hints at data is clustered, a negative Moran’s I implies data is dispersed.
 

What is a high-high cluster?

Only High-High Cluster. A location where the only statistically significant type throughout time has been High-High Clusters. Only High-Low Outlier. A location where the only statistically significant type throughout time has been High-Low Outliers.

What is the theoretical range of the global Moran’s I statistic?

Values of I usually range from −1 to +1. Values significantly below -1/(N-1) indicate negative spatial autocorrelation and values significantly above -1/(N-1) indicate positive spatial autocorrelation.