geoCorrection(x, type, ...)
type
argument accordingly. In the case of least-cost distances, the correction is only done in East-West direction. In the case of random walks there should be an additional correction which reduces the conductance in North-South direction (type="r"
).
The correction is done by dividing conductance values by the inter-cell distance. Distances are calculated as great-circle distances for lonlat grids (see function isLonLat()) and Euclidean distances for all other grids.
In the case of randomised shortest paths, the need for correction is somewhat in between these two correction methods. We have not developed an analytical solution for this problem yet. With very low values for theta, you may choose the correction for random walks, and for high values the one for least-cost paths. Users who want to work with intermediate values of theta are encouraged to experiment with different solutions.
The values are scaled to get values near 1 if the argument scl
is set to TRUE. This is desirable for subsequent calculations involving random walk calculations. Values are divided by the W-E inter-cell distance (at the centre of the grid).r <- raster(ncol=36,nrow=18)
r <- setValues(r,rep(1,times=ncell(r)))
tr <- transition(r, mean, directions=8)
#directly
tr1 <- geoCorrection(tr, type="c", multpl=FALSE)
#the same, but with a separate correction matrix
trCorr <- geoCorrection(tr, type="c", multpl=TRUE)
tr2 <- tr * trCorr
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