# eRTG3D

The **e**mpirically informed **R**andom **T**rajectory **G**enerator in three dimensions (eRTG3D) is an algorithm to generate realistic random trajectories in a 3-D space between two given fix points, so-called Conditional Empirical Random Walks. The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth’s surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.

The eRTG3D algorithm was developed and implemented as an R package within the scope of a Master’s thesis (Unterfinger, 2018) at the Department of Geography, University of Zurich. The development started from a 2-D version of the eRTG algorithm by Technitis et al. (2016).

## Getting started

```
# Install release version from CRAN
install.packages("eRTG3D")
# Install development version from GitHub
devtools::install_github("munterfi/eRTG3D")
```

## Features

The **eRTG3D** package contains functions to:

- calculate
**movement parameters of 3-D GPS tracking data**, turning angle, lift angle and step length
**extract distributions** from movement parameters;
**P probability** - The mover’s behavior from its perspective
**Q probability** - The pull towards the target

- simulate
**Unconditional Empirical Random Walks (UERW)**
- simulate
**Conditional Empirical Random Walks (CERW)**
- simulate conditional
**gliding and soaring behavior** of birds between two given points
**statistically test** the simulated tracks against the original input
**visualize** tracks, simulations and distributions in 3-D and 2-D
- conduct a basic
**point cloud analysis**; extract **3-D Utilization Distributions (UDs)** from observed or simulated tracking data by means of voxel counting
- project 3-D tracking data into different
**Coordinate Reference Systems (CRSs)**
- export data to
**sf package objects**; ‘sf, data.frames’
- manipulate
**extent of raster layers**

## References

Unterfinger M (2018). 3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk. Master’s thesis, University of Zurich.

Technitis G, Weibel R, Kranstauber B, Safi K (2016). “An algorithm for empirically informed random trajectory generation between two endpoints.” GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. doi: 10.5167/uzh-130652.