terra (version 1.8-80)

interpolation: Spatial interpolation

Description

Make a SpatRaster with interpolated values using a fitted model object of classes such as "gstat" (gstat package) or "Krige" (fields package), or any other model that has location (e.g., "x" and "y", or "longitude" and "latitude") as predictors (independent variables). If x and y are the only predictors, it is most efficient if you provide an empty (no associated data in memory or on file) SpatRaster for which you want predictions. If there are more spatial predictor variables, provide these as a SpatRaster in the first argument of the function. If you do not have x and y locations as implicit predictors in your model you should use predict instead.

Usage

# S4 method for SpatRaster
interpolate(object, model, fun=predict, ..., xyNames=c("x", "y"), 
       factors=NULL, const=NULL, index = NULL, cores=1, cpkgs=NULL, 
	   na.rm=FALSE, filename="", overwrite=FALSE, wopt=list())

Value

SpatRaster

See Also

predict, interpIDW, interpNear

Examples

r <- rast(system.file("ex/elev.tif", package="terra"))
ra <- aggregate(r, 10)
xy <- data.frame(xyFromCell(ra, 1:ncell(ra)))
v <- values(ra)
i <- !is.na(v)
xy <- xy[i,]
v <- v[i]

if (FALSE) {
library(fields) 
tps <- Tps(xy, v)
p <- rast(r)

# use model to predict values at all locations
p <- interpolate(p, tps)
p <- mask(p, r)
plot(p)

### change "fun" from predict to fields::predictSE to get the TPS standard error
## need to use "rast(p)" to remove the values
se <- interpolate(rast(p), tps, fun=predictSE)
se <- mask(se, r)
plot(se)

### another predictor variable, "e"
e <- (init(r, "x") * init(r, "y")) / 100000000
names(e) <- "e"

z <- as.matrix(extract(e, xy)[,-1])

## add as another independent variable
xyz <- cbind(xy, z)
tps2 <- Tps(xyz, v)
p2 <- interpolate(e, tps2, xyOnly=FALSE)

## as a linear covariate
tps3 <- Tps(xy, v, Z=z)

## Z is a separate argument in Krig.predict, so we need a new function
## Internally (in interpolate) a matrix is formed of x, y, and elev (Z)

pfun <- function(model, x, ...) {
   predict(model, x[,1:2], Z=x[,3], ...)
}
p3 <- interpolate(e, tps3, fun=pfun)


#### gstat examples
library(gstat)
library(sp)
data(meuse)

### inverse distance weighted (IDW)
r <- rast(system.file("ex/meuse.tif", package="terra"))
mg <- gstat(id = "zinc", formula = zinc~1, locations = ~x+y, data=meuse, 
            nmax=7, set=list(idp = .5))
z <- interpolate(r, mg, debug.level=0, index=1)
z <- mask(z, r)

## with a model built with an `sf` object you need to provide custom function

library(sf)
sfmeuse <- st_as_sf(meuse, coords = c("x", "y"), crs=crs(r))
mgsf <- gstat(id = "zinc", formula = zinc~1, data=sfmeuse,  nmax=7, set=list(idp = .5))

interpolate_gstat <- function(model, x, crs, ...) {
	v <- st_as_sf(x, coords=c("x", "y"), crs=crs)
	p <- predict(model, v, ...)
	as.data.frame(p)[,1:2]
}

zsf <- interpolate(r, mgsf, debug.level=0, fun=interpolate_gstat, crs=crs(r), index=1)
zsf <- mask(zsf, r)

### kriging

### ordinary kriging
v <- variogram(log(zinc)~1, ~x+y, data=meuse)
mv <- fit.variogram(v, vgm(1, "Sph", 300, 1))
gOK <- gstat(NULL, "log.zinc", log(zinc)~1, meuse, locations=~x+y, model=mv)
OK <- interpolate(r, gOK, debug.level=0)

## universal kriging
vu <- variogram(log(zinc)~elev, ~x+y, data=meuse)
mu <- fit.variogram(vu, vgm(1, "Sph", 300, 1))
gUK <- gstat(NULL, "log.zinc", log(zinc)~elev, meuse, locations=~x+y, model=mu)
names(r) <- "elev"
UK <- interpolate(r, gUK, debug.level=0)

## co-kriging
gCoK <- gstat(NULL, 'log.zinc', log(zinc)~1, meuse, locations=~x+y)
gCoK <- gstat(gCoK, 'elev', elev~1, meuse, locations=~x+y)
gCoK <- gstat(gCoK, 'cadmium', cadmium~1, meuse, locations=~x+y)
gCoK <- gstat(gCoK, 'copper', copper~1, meuse, locations=~x+y)
coV <- variogram(gCoK)
plot(coV, type='b', main='Co-variogram')
coV.fit <- fit.lmc(coV, gCoK, vgm(model='Sph', range=1000))
coV.fit
plot(coV, coV.fit, main='Fitted Co-variogram')
coK <- interpolate(r, coV.fit, debug.level=0)
plot(coK)
}