toxicIntensity {briskaR}R Documentation

toxicIntendity function wrapping dispersal and exposure

Description

toxicIntendity function wrapping dispersal and exposure

Usage

toxicIntensity(
  object,
  sf,
  size_raster = 2^10,
  tolerance_square = 0.1,
  kernel = "NIG",
  kernel.options = list(a1 = 0.2073, a2 = 0.2073, b1 = 0.3971, b2 = 0.3971, b3 =
    0.0649, theta = 0),
  loss = NULL,
  beta = 0.4,
  nbr_cores = 1,
  squared_frame = NULL,
  quiet = FALSE
)

Arguments

object

sf or SpatialPolygonsDataFrame. A simple feature of class sf or SpatialPolygonsDataFrame

sf

sf. And object of class 'sf' on which exposure is computed from the previous list of raster by patch 'RasterStack_dispersal'. See sf for details.

size_raster

integer. Raster size (default = 2^10)

tolerance_square

numeric. Tolerance rate to test if an sf set is squared

kernel

string. Dispersion kernel, function name (default = NIG)

kernel.options

list. Parameters list for the kernel function

loss

numeric. Numeric vector to applied a loss on exposure cells.

beta

numeric. toxic adherence parameter between 0 and 1 (default = 0.4).

nbr_cores

integer. Parameters for parallel computing: the number of cores to use, i.e. at most how many child processes will be run simultaneously. Default is 1 (non parallel).

squared_frame

sf. Select the sf to be considered as frame to rasterized. Default is 'NULL', and 'object' is used.

quiet

boolean. Set 'TRUE' to remove progress bar.

Details

The dispersal of contaminants is implemented by rastering the landscape and by computing the convolution between sources emissions and a dispersal kernel.

The dispersion kernel by default is Normal Inverse Gaussian kernel ("NIG" function). Currently, two others are implemented "geometric" (with parameter a) and "2Dt" kernels (with parameters a, b, c1, c2).

Local intensity depends of beta and alpha parameters. Beta represents the toxic adherence between [0,1]. Alpha represents a list of parameters of the lost of toxic particules due to covariates (precipitation). There are two configurations to integrate the loss in the function : (i) simulating covariate (simulate=TRUE) or (ii) uploading covariate (simulate=FALSE). The covariate is linked to the loss by a linear regression with paramaters minalpha, maxalpha, covariate_threshold.


[Package briskaR version 1.0.3 Index]