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Sample occurrences from spatial random field

Source: R/sample_occurrences_from_raster.R
sample_occurrences_from_raster.Rd

This function draws point occurrences from a spatial random field represented by a raster. Points are sampled based on the values in the raster, with the number of occurrences specified for each time step.

Usage

sample_occurrences_from_raster(raster, time_series, seed = NA)

Arguments

raster

A SpatRaster object (see terra::rast()).

time_series

A vector with the number of occurrences per time point.

seed

A positive numeric value setting the seed for random number generation to ensure reproducibility. If NA (default), then set.seed() is not called at all. If not NA, then the random number generator state is reset (to the state before calling this function) upon exiting this function.

Value

An sf object with POINT geometry containing the locations of the simulated occurrences, a time_point column indicating the associated time point for each occurrence and columns used as weights for sampling. If the raster is created with create_spatial_pattern(), the column sampling_p1 is used.

See also

Other occurrence: create_spatial_pattern(), simulate_random_walk(), simulate_timeseries()

Examples

# Load packages
library(sf)
library(ggplot2)
library(tidyterra)

# Create polygon
plgn <- st_polygon(list(cbind(c(5, 10, 8, 2, 3, 5), c(2, 1, 7, 9, 5, 2))))

## Medium scale clustering
# Create the random field
rs_pattern_clustered <- create_spatial_pattern(
  polygon = plgn,
  resolution = 0.1,
  spatial_pattern = "clustered",
  seed = 123)
#> [using unconditional Gaussian simulation]

# Sample 200 occurrences from random field
pts_occ_clustered <- sample_occurrences_from_raster(
  raster = rs_pattern_clustered,
  time_series = 200,
  seed = 123)

ggplot() +
  geom_spatraster(data = rs_pattern_clustered) +
  geom_sf(data = pts_occ_clustered) +
  scale_fill_continuous(type = "viridis") +
  theme_minimal()


## Large scale clustering
# Create the random field
rs_pattern_large <- create_spatial_pattern(
  polygon = plgn,
  resolution = 0.1,
  spatial_pattern = 100,
  seed = 123)
#> [using unconditional Gaussian simulation]

# Sample 200 occurrences from random field
pts_occ_large <- sample_occurrences_from_raster(
  raster = rs_pattern_large,
  time_series = 200,
  seed = 123)

ggplot() +
  geom_spatraster(data = rs_pattern_large) +
  geom_sf(data = pts_occ_large) +
  scale_fill_continuous(type = "viridis") +
  theme_minimal()