| autocorrelation {terra} | R Documentation |
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).
## 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. "moran" for Moran's I and "geary" for Geary's C |
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
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)