Function to generate Kernel Density plot from Beaver Forage Data

This function allows users to generate a kernel density raster from a collection of points.

Usage

forage_density(
  forage_points,
  impact_cat,
  grid_size = 20,
  kern_bw = 250,
  kd_extent,
  kd_weights = c(1, 1000, 1e+06),
  low_thresh = 1e-12,
  standardise = FALSE
)

Arguments

forage_points: The foraging sign point data - must be either an sf object or an sf-readable file. See sf::st_drivers() for available drivers
impact_cat: A character vector of length one containing the column name which describes the feeding impact category (i.e. Low, Medium, or High). If not provided kernel density is not weighted
grid_size: The raster grid cell size desired.
kern_bw: The bandwidth for the kernel denisty search radius.
kd_extent: The desired extent of the output raster.
kd_weights: A numeric vector of length equal to the number of unique impact categories.
low_thresh: A lower threshold for setting the minimum desired value. Values < low_thresh are set to NA.
standardise: Boolean to specify if densities shoul be standardised between 0-1.

Value

"RasterLayer" object with kernel denisity estimates for feeding signs

Examples

# Here we filter the filter the built in 2019-2020 ROBT feeding sign data `RivOtter_FeedSigns`
# Then pipe this 'sf' object to forage_density.

RivOtter_FeedSigns %>%
  dplyr::filter(SurveySeason == "2019 - 2020")%>%
  forage_density(., 'FeedCat')
#> No value supplied for "kd_extent" argument: default extent will be used
#> 
#> calculating weighted kde
#> class       : SpatRaster 
#> size        : 910, 1573, 1  (nrow, ncol, nlyr)
#> resolution  : 11.57761, 34.60946  (x, y)
#> extent      : 304974.2, 323185.8, 82922.7, 114417.3  (xmin, xmax, ymin, ymax)
#> coord. ref. : OSGB 1936 / British National Grid (EPSG:27700) 
#> source(s)   : memory
#> name        :            z 
#> min value   : 1.000000e-10 
#> max value   : 3.242415e+02