munterfinger/eRTG3D: Empirically Informed Random Trajectory Generation in 3-D

Creates 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. 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:https://doi.org/10.5167/uzh-130652>.

Getting started

Package details

MaintainerMerlin Unterfinger <[email protected]>
LicenseGPL (>=3)
Version0.6.0
URL https://munterfinger.github.io/eRTG3D/ https://github.com/munterfinger/eRTG3D
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("munterfinger/eRTG3D")
munterfinger/eRTG3D documentation built on July 13, 2019, 9:59 a.m.