Trajectory simulations in 2-D

In eRTG3D: Empirically Informed Random Trajectory Generation in 3-D

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(knitr.table.format = "html", rmarkdown.html_vignette.check_title = FALSE)

library(eRTG3D)
library(ggplot2)
set.seed(123)

2-D simulations as special case of 3-D

Since movement in 2-D is a special case of movement in 3-D, the eRTG3D algorithm also supports two-dimensional simulations. The underlying data structure of the algorithm remains in three-dimensional, with the third dimension (z) being constant, as for example zero. This approach guarantees a seamless transition between 2-D and 3-D simulations. Therefore, two P and Q probabilities are be extracted from 2D and 3D trajectories, then a combined simulation can take place.

Example trajectory

trajectory.3D <- sim.crw.3d(nStep = 100, rTurn = 0.99, rLift = 0.99, meanStep = 0.1)

Constant z-dimension

To simulate in 2-D the third dimension of the trajectory is set to zero:

trajectory.2D <- trajectory.3D
trajectory.2D$z <- 0
head(trajectory.2D)

If the original trajectory is already two-dimensional, a third column z has to be added: trajectory.2D$z <- 0.

Simulations in 2-D

Now the workflow is the same as in 3-D, described in the standard workflow vignette:

simulation.2D <- reproduce.track.3d(trajectory.2D)

Note: Since it is not feasible to use a DEM (DEM = demRaster) in 2-D simualtions, the adding of a DEM in the somulations will result in dead ends. A BG layer (BG = bgRaster) with a binary mask or continous probabilities for the simulation area can be passed (e.g. water bodies, nutrition sources, ...).

simulation.2D <- reproduce.track.3d(trajectory.2D)

And plotting the results:

plot2d(trajectory.2D, simulation.2D, titleText = "2-D trajectory simulation")

eRTG3D documentation built on March 18, 2022, 6:11 p.m.