Spatial Autocorrelation
Correlation of a variable with itself across space (in different places in space).
Explanation
The Global Moran's I is the statistical measure used to analyse spatial autocorrelation. the relationship between variables in space can be positive, negative, or neutral. spatial autocorrelation helps to determine how important spatial characteristics are in affecting a given object in space and if there is a clear relationship between objects with spatial properties.
Positive spatial autocorrelation: Values are similar to their neighbours or other close objects.
No autocorrelation: Random values of close objects or neighbours.
Negative spatial autocorrelation: Values are dissimilar to their neighbors or close objects, dispersed patterns of values on the map.
Examples
A box map (Anselin 1994) is the mapping counterpart of the idea behind a box plot. The point of departure is again a quantile map, more specifically, a quartile map. But the four categories are extended to six bins, to separately identify the lower and upper outliers. 
Outgoing relations
- Spatial Autocorrelation is part of Exploratory Spatial Data Analysis
Incoming relations
- negative spatial autocorrelation is a kind of Spatial Autocorrelation
- neutral spatial autocorrelation is a kind of Spatial Autocorrelation
- Positive spatial autocorrelation is a kind of Spatial Autocorrelation