A regular sample of the prediction area, as kNNDM's predpoints.

kNNDM needs the places the map will cover, not the places it was trained on. A few thousand regularly spaced pixels describe that well enough – the method compares DISTRIBUTIONS of distances, and a regular sample estimates one cheaply. A random sample would work too and is noisier for the same size.

Usage

prediction_sample(raster, size = 5000L)

Arguments

raster

A SpatRaster (terra) covering the prediction area.

size

About how many points to draw. Cells where the raster is NA – the sea around a continent – do not count: when too few land on data, the sample is drawn again, denser.

Value

A data frame with x and y, in the raster's own CRS.

Examples

ex <- example_landscape()
elevation <- terra::rast(file.path(ex$raster_dir, "elevation.tif"))
head(prediction_sample(elevation, size = 200))
#> # A tibble: 6 × 2
#>       x     y
#>   <dbl> <dbl>
#> 1 -49.6 -20.2
#> 2 -49.6 -20.2
#> 3 -49.6 -20.2
#> 4 -49.6 -20.2
#> 5 -49.5 -20.2
#> 6 -49.5 -20.2