In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran. 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
=
N
∑
i
∑
j
w
i
j
∑
i
∑
j
w
i
j
(
X
i
−
X
¯
)
(
X
j
−
X
¯
)
∑
i
(
X
i
−
X
¯
)
2
where
N
is the number of spatial units indexed by
i
and
j
;
X
is the variable of interest;
X
¯
is the mean of
X
; and
w
i
j
is an element of a matrix of spatial weights.
The value of
I
can depend quite a bit on the assumptions built into the spatial weights matrix
w
i
j
. The idea is to construct a matrix that accurately reflects your assumptions about the particular spatial phenomenon in question. A common approach is to give a weight of 1 if two zones are neighbors, and 0 otherwise, though the definition of 'neighbors' can vary. Another common approach might be to give a weight of 1 to
k
nearest neighbors, 0 otherwise. An alternative is to use a distance decay function for assigning weights. Sometimes the length of a shared edge is used for assigning different weights to neighbors. The selection of spatial weights matrix should be guided by theory about the phenomenon in question.
The expected value of Moran's I under the null hypothesis of no spatial autocorrelation is
E
(
I
)
=
−
1
N
−
1
Its variance equals
Var
(
I
)
=
N
S
4
−
S
3
S
5
(
N
−
1
)
(
N
−
2
)
(
N
−
3
)
(
∑
i
∑
j
w
i
j
)
2
−
(
E
(
I
)
)
2
where
S
1
=
1
2
∑
i
∑
j
(
w
i
j
+
w
j
i
)
2
S
2
=
∑
i
(
∑
j
w
i
j
+
∑
j
w
j
i
)
2
S
3
=
N
−
1
∑
i
(
x
i
−
x
¯
)
4
(
N
−
1
∑
i
(
x
i
−
x
¯
)
2
)
2
S
4
=
(
N
2
−
3
N
+
3
)
S
1
−
N
S
2
+
3
(
∑
i
∑
j
w
i
j
)
2
S
5
=
(
N
2
−
N
)
S
1
−
2
N
S
2
+
6
(
∑
i
∑
j
w
i
j
)
2
Values of I range from −1 to +1. Negative values indicate negative spatial autocorrelation and positive values indicate positive spatial autocorrelation. A zero value indicates a random spatial pattern. For statistical hypothesis testing, Moran's I values can be transformed to z-scores.
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 is widely used in the fields of geography and GIScience. Some examples include:
The analysis of geographic differences in health variables.
It has been used to characterize the impact of lithium concentrations in public water on mental health.
It has also recently been used in dialectology to measure the significance of regional language variation.
