This function utilizes a regular, or nearly so in the case of existing collections, grid of points to develop a sampling scheme or n polygons.

Usage

OpportunisticSample(polygon, n = 20, collections, reps = 100, BS.reps = 9999)

Arguments

polygon: the input sf polygon, i.e. species range or administrative unit, where sampling is desired.
n: Numeric. The total number of desired collections. Defaults to 20.
collections: an sf point geometry data set of where existing collections have been made.
reps: further arguments passed to np.boot
BS.reps: number of bootstrap replicates for evaluating results.

Value

A list containing two sublists, the first of which 'SummaryData' details the number of voronoi polygons generated, and the results of the bootstrap simulations. The second 'Geometry', contains the final spatial data products, which can be written out on your end. See the vignette for questions about saving the two main types of spatial data models (vector - used here, and raster).

Examples

#' Design additional collections around already existing collections
ri <- spData::us_states |>
  dplyr::select(NAME) |>
  dplyr::filter(NAME == 'Rhode Island') |>
  sf::st_transform(32617)
existing_collections <- sf::st_sample(ri, size = 5) |>
  sf::st_as_sf() |>
  dplyr::rename(geometry = x)

system.time(
  out <- OpportunisticSample(polygon = ri, collections = existing_collections, BS.reps=999)
)
#>    user  system elapsed 
#>   6.588   0.034   6.623 
ggplot2::ggplot() +
  ggplot2::geom_sf(data = out$Geometry, ggplot2::aes(fill = ID)) +
  ggplot2::geom_sf(data = existing_collections)