eRTG3D

The empirically informed Random Trajectory Generator 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
remotes::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;
  1. P probability - The mover’s behavior from its perspective
  2. 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

Contributing

Contributions to this package are very welcome, issues and pull requests are the preferred ways to share them. Please see the Contribution Guidelines.

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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.