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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 development version from GitHub


The eRTG3D package contains functions to:


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.


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.