Moran's I

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The white and black squares are perfectly dispersed so Moran's I would be −1. If the white squares were stacked to one half of the board and the black squares to the other, Moran's I would be close to +1. A random arrangement of square colors would give Moran's I a value that is close to 0.

In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran.[1][2] Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional (i.e. 2 or 3 dimensions of space) and multi-directional.


Moran's I is defined as

 I = \frac{N} {\sum_{i} \sum_{j} w_{ij}} \frac {\sum_{i} \sum_{j} w_{ij}(X_i-\bar X) (X_j-\bar X)} {\sum_{i} (X_i-\bar X)^2}

where N is the number of spatial units indexed by i and j; X is the variable of interest; \bar X is the mean of X; and w_{ij} is an element of a matrix of spatial weights.

The expected value of Moran's I under the null hypothesis of no spatial autocorrelation is

 E(I) = \frac{-1} {N-1}

Its variance equals

 \operatorname{Var}(I) = \frac{NS_4-S_3S_5} {(N-1)(N-2)(N-3)(\sum_{i} \sum_{j} w_{ij})^2} - (E(I))^2


 S_1 = \frac {1} {2} \sum_{i} \sum_{j} (w_{ij}+w_{ji})^2
 S_2 = \sum_{i} ( \sum_{j} w_{ij} + \sum_{j} w_{ji})^2
 S_3 = \frac {N^{-1} \sum_{i} (x_i - \bar x)^4} {(N^{-1} \sum_{i} (x_i - \bar x)^2)^2}
 S_4 = (N^2-3N+3)S_1 - NS_2 + 3 (\sum_{i} \sum_{j} w_{ij})^2
 S_5 = (N^2-N) S_1 - 2NS_2 + 6(\sum_{i} \sum_{j} w_{ij})^2

[3] Negative values indicate negative spatial autocorrelation and the inverse for positive values. Values range from −1 (indicating perfect dispersion) to +1 (perfect correlation). A zero value indicates a random spatial pattern. For statistical hypothesis testing, Moran's I values can be transformed to Z-scores in which values greater than 1.96 or smaller than −1.96 indicate spatial autocorrelation that is significant at the 5% level.

Moran's I is inversely related to Geary's C, but it is not identical. Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation.


Moran's I values is widely used in the analysis of geographic differences in health variables.[4] It has been used to characterize the impact of lithium concentrations in public water on mental health.[5] It has also recently been used in dialectology to measure the significance of regional language variation.[6]


  1. ^ Moran, P. A. P. (1950). "Notes on Continuous Stochastic Phenomena". Biometrika 37 (1): 17–23. doi:10.2307/2332142. JSTOR 2332142. 
  2. ^ Li, Hongfei; Calder, Catherine A.; Cressie, Noel (2007). "Beyond Moran's I: Testing for Spatial Dependence Based on the Spatial Autoregressive Model". Geographical Analysis 39 (4): 357–375. doi:10.1111/j.1538-4632.2007.00708.x. 
  3. ^ Cliff and Ord (1981), Spatial Processes, London
  4. ^ "The Analysis of Spatial Association by Use of Distance Statistics". Geographical Analysis 24 (3): 189–206. 3 Sep 2010. doi:10.1111/j.1538-4632.1992.tb00261.x. 
  5. ^ "Geospatial examination of lithium in drinking water and suicide mortality". Int J Health Geogr. 11 (1): 19. 2012. doi:10.1186/1476-072X-11-19. 
  6. ^ Grieve, Jack (2011). "A regional analysis of contraction rate in written Standard American English". International Journal of Corpus Linguistics 16 (4): 514–546. doi:10.1075/ijcl.16.4.04gri. 

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