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https://github.com/munterfi/ertg3d
Empirically Informed Random Trajectory Generator in 3-D.
https://github.com/munterfi/ertg3d
3d birds conditional-empirical-random-walk gliding-and-soaring machine-learning movement-ecology random-trajectory-generator random-walk rstats rstats-package simulation trajectory-generation
Last synced: about 1 month ago
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Empirically Informed Random Trajectory Generator in 3-D.
- Host: GitHub
- URL: https://github.com/munterfi/ertg3d
- Owner: munterfi
- License: gpl-3.0
- Created: 2017-12-18T11:22:07.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-02-23T14:41:18.000Z (almost 3 years ago)
- Last Synced: 2024-06-11T16:04:51.435Z (8 months ago)
- Topics: 3d, birds, conditional-empirical-random-walk, gliding-and-soaring, machine-learning, movement-ecology, random-trajectory-generator, random-walk, rstats, rstats-package, simulation, trajectory-generation
- Language: R
- Homepage: https://munterfi.github.io/eRTG3D/
- Size: 52.7 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE_OF_CONDUCT.md
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README
# eRTG3D 
[](https://CRAN.R-project.org/package=eRTG3D)
[](https://CRAN.R-project.org/package=eRTG3D)
[](https://github.com/munterfi/eRTG3D/actions)
[](https://github.com/munterfi/eRTG3D/actions)
[](https://app.codecov.io/gh/munterfi/eRTG3D?branch=master)
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](https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf)) 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](https://doi.org/10.5167/uzh-130652)).
## Getting started
```r
# 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](https://github.com/munterfi/eRTG3D/blob/master/.github/CONTRIBUTING.md).
This project is released with a [Contributor Code of Conduct](https://github.com/munterfi/eRTG3D/blob/master/.github/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.
## References
Unterfinger M ([2018](https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf)). 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](https://doi.org/10.5167/uzh-130652)). “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](https://doi.org/10.5167/uzh-130652).