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>.
|Maintainer||Merlin Unterfinger <[email protected]>|
|Package repository||View on GitHub|
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