autocorrelation | R Documentation |
Compute spatial autocorrelation for a numeric vector or a SpatRaster. You can compute standard (global) Moran's I or Geary's C, or local indicators of spatial autocorrelation (Anselin, 1995).
## S4 method for signature 'numeric'
autocor(x, w, method="moran")
## S4 method for signature 'SpatRaster'
autocor(x, w=matrix(c(1,1,1,1,0,1,1,1,1),3), method="moran", global=TRUE)
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 |
method |
character. If |
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.
numeric or SpatRaster
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
The spdep
package for additional and more general approaches for computing spatial autocorrelation
### raster
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
rc <- autocor(r, w=f, global=FALSE)
### numeric (for vector data)
f <- system.file("ex/lux.shp", package="terra")
v <- vect(f)
w <- relate(v, relation="touches")
# global
autocor(v$AREA, w)
# local
v$Gi <- autocor(v$AREA, w, "Gi")
plot(v, "Gi")
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