Spatial autocorrelation
Compute Moran's I or Geary's C measures of global spatial autocorrelation in a RasterLayer, or compute the the local Moran or Geary index (Anselin, 1995).
Geary(x, w=matrix(c(1,1,1,1,0,1,1,1,1), 3,3)) Moran(x, w=matrix(c(1,1,1,1,0,1,1,1,1), 3,3)) MoranLocal(x, w=matrix(c(1,1,1,1,0,1,1,1,1), 3,3)) GearyLocal(x, w=matrix(c(1,1,1,1,0,1,1,1,1), 3,3))
x |
RasterLayer |
w |
Spatial weights defined by or a rectangular matrix with odd length (3, 5, ...) sides (as in |
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.
A single value (Moran's I or Geary's C) or a RasterLayer (Local Moran or Geary values)
Robert J. Hijmans and Babak Naimi
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
The spdep package for additional and more general approaches for computing indices of spatial autocorrelation
r <- raster(nrows=10, ncols=10) values(r) <- 1:ncell(r) Moran(r) # Rook's case f <- matrix(c(0,1,0,1,0,1,0,1,0), nrow=3) Moran(r, f) Geary(r) x1 <- MoranLocal(r) # Rook's case x2 <- MoranLocal(r, w=f)
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