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Create spatial pattern within a polygon

Source: R/create_spatial_pattern.R
create_spatial_pattern.Rd

This function creates a raster with a spatial pattern for the area of a polygon.

Usage

create_spatial_pattern(
  polygon,
  resolution,
  spatial_pattern = c("random", "clustered"),
  seed = NA,
  n_sim = 1
)

Arguments

polygon

An sf object with POLYGON geometry.

resolution

A numeric value defining the resolution of the raster cells.

spatial_pattern

Specifies the desired spatial pattern. It can be a character string ("random" or "clustered") or a numeric value ≥ 1 (1 means random distribution, larger values indicate more clustering). The default is "random". "clustered" corresponds to a value of 10. See Details.

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.

n_sim

Number of simulations. Each simulation is a different layer in the raster. Default is 1.

Value

An object of class SpatRaster with a spatial pattern for the area of the given polygon with n_sim layers sampling_p'n_sim' containing the sampling probabilities from the raster grid for each simulation.

Details

The spatial_pattern argument changes the range parameter of the spherical variogram model. spatial_pattern = 1 means the range has the same size as the grid cell, which is defined in the resolution argument. The function gstat::vgm() is used to implement the spherical variogram model.

See also

gstat::vgm() and its range argument

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

Examples

# Load packages
library(sf)
library(ggplot2)
library(tidyterra)
#> 
#> Attaching package: ‘tidyterra’
#> The following object is masked from ‘package:stats’:
#> 
#>     filter

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

# 1. Random spatial pattern
rs_pattern_random <- create_spatial_pattern(
  polygon = plgn,
  resolution = 0.1,
  spatial_pattern = "random",
  seed = 123)
#> [using unconditional Gaussian simulation]

ggplot() +
  geom_spatraster(data = rs_pattern_random) +
  scale_fill_continuous(type = "viridis") +
  theme_minimal()


# 2. Clustered spatial pattern
rs_pattern_clustered <- create_spatial_pattern(
  polygon = plgn,
  resolution = 0.1,
  spatial_pattern = "clustered",
  seed = 123)
#> [using unconditional Gaussian simulation]

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


# 3. User defined spatial pattern
# Large scale clustering
rs_pattern_large <- create_spatial_pattern(
  polygon = plgn,
  resolution = 0.1,
  spatial_pattern = 100,
  seed = 123)
#> [using unconditional Gaussian simulation]

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