terra (version 1.0-10)

autocorrelation: Spatial autocorrelation

Description

Compute spatial autocorrelation for a numeric vecor or a SpatRaster. You can compute standard (global) Moran's I or Geary's C, or the local variations thereof (Anselin, 1995).

Usage

# S4 method for numeric
autocor(x, w, method="moran", global=TRUE)
# S4 method for SpatRaster
autocor(x, w=matrix(c(1,1,1,1,0,1,1,1,1),3), method="moran", global=TRUE)

Arguments

x

numeric or SpatRaster

w

Spatial weights defined by or a rectangular matrix. For a SpatRaster this matrix must the sides must have an odd length (3, 5, ...)

global

logical. If TRUE global autocorrelation is computed instead of local autocorrelation

method

character. "moran" for Moran's I and "geary" for Geary's C

Value

numeric or SpatRaster

Details

The default setting uses a 3x3 neighborhood to compute "Queen's case" indices. You can use a filter (weights matrix) to do other things, such as "Rook's case", or different lags.

References

Moran, P.A.P., 1950. Notes on continuous stochastic phenomena. Biometrika 37:17-23

Geary, R.C., 1954. The contiguity ratio and statistical mapping. The Incorporated Statistician 5: 115-145

Anselin, L., 1995. Local indicators of spatial association-LISA. Geographical Analysis 27:93-115

https://en.wikipedia.org/wiki/Indicators_of_spatial_association

See Also

The spdep package for additional and more general approaches for computing spatial autocorrelation

Examples

# NOT RUN {
r <- rast(nrows=10, ncols=10, xmin=0)
values(r) <- 1:ncell(r)

autocor(r)

# rook's case neighbors
f <- matrix(c(0,1,0,1,0,1,0,1,0), nrow=3)
autocor(r, f)

## local 
x1 <- autocor(r, global=FALSE)
x2 <- autocor(r, w=f, global=FALSE)

#autocor(r, geary=TRUE)
# }