Sampling

Let’s start by attaching the necessary packages and setting the seed.

library(simsam)
library(terra)
#> terra 1.9.11
set.seed(2024-11-20)

Next, we define the raster grid to sample points/locations from.

rast_grid = terra::rast(xmin = 0, xmax = 200, ymin = 0, ymax = 200,
                        ncols = 200, nrows = 200)

Alternatively, we can read a raster from a file.

Sample

The simsam package supports several types of sampling, including jittered, clustered, and random with the sam_field() function. All these types of sampling require the rast_grid object, the number of samples to create (size), and a sampling method function created by sample_*() functions.

The jittered sampling is a regular sample moved by an amount of noise. The amount argument controls the amount of jitter. Importantly, this sampling type ensures that all the samples fall within the sampling window.

sample_j1 = sam_field(rast_grid, size = 100, method = sample_jittered(amount = 5))
plot(sample_j1)

sample_j2 = sam_field(rast_grid, size = 100, method = sample_jittered(amount = 20))
plot(sample_j2)

The clustered sampling creates samples forming groups of points with similar locations. The outcome is controlled by the nclusters and radius arguments, where the former controls the number of clusters and the latter the radius of the buffer around each cluster.

sample_c1 = sam_field(rast_grid, 100, method = sample_clustered(nclusters = 5, radius = 5))
plot(sample_c1)

sample_c2 = sam_field(rast_grid, 100, method = sample_clustered(nclusters = 5, radius = 10))
plot(sample_c2)

Random sampling creates samples with no spatial structure.

sample_r = sam_field(rast_grid, 100, method = sample_random())
plot(sample_r)

Jakub Nowosad

2026-04-30

Sample