Science Score: 13.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 7 DOI reference(s) in README -
○Academic publication links
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○Committers with academic emails
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○Scientific vocabulary similarity
Low similarity (16.9%) to scientific vocabulary
Keywords
Repository
Empirically Informed Random Trajectory Generator in 3-D.
Basic Info
- Host: GitHub
- Owner: munterfi
- License: gpl-3.0
- Language: R
- Default Branch: master
- Homepage: https://munterfi.github.io/eRTG3D/
- Size: 52.7 MB
Statistics
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
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
```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;
- P probability - The mover's behavior from its perspective
- 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.
Owner
- Name: Merlin Unterfinger
- Login: munterfi
- Kind: user
- Location: Zurich, Switzerland
- Company: SBB (Swiss Federal Railways)
- Website: maptic.ch
- Twitter: munterfi1
- Repositories: 29
- Profile: https://github.com/munterfi
Data Scientist at the Swiss Federal Railways (SBB), specialized in Geographic Information Science.
GitHub Events
Total
- Watch event: 1
- Fork event: 1
Last Year
- Watch event: 1
- Fork event: 1
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Merlin Unterfinger | i****o@m****h | 159 |
| GeoTech | g****s@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 16
- Total pull requests: 37
- Average time to close issues: 10 days
- Average time to close pull requests: about 1 hour
- Total issue authors: 2
- Total pull request authors: 1
- Average comments per issue: 0.25
- Average comments per pull request: 0.03
- Merged pull requests: 37
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- munterfi (15)
- alan-barzilay (1)
Pull Request Authors
- munterfi (37)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- cran 294 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
cran.r-project.org: eRTG3D
Empirically Informed Random Trajectory Generation in 3-D
- Homepage: https://munterfi.github.io/eRTG3D/
- Documentation: http://cran.r-project.org/web/packages/eRTG3D/eRTG3D.pdf
- License: GPL-3
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Latest release: 0.7.0
published almost 4 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 3.5.0 depends
- CircStats >= 0.2 imports
- ggplot2 >= 3.1.1 imports
- pbapply >= 1.4 imports
- plotly >= 4.9.0 imports
- raster >= 2.9 imports
- rasterVis >= 0.45 imports
- tiff >= 0.1 imports
- covr >= 3.2.1 suggests
- gridExtra >= 2.3 suggests
- knitr >= 1.23 suggests
- pander >= 0.6.3 suggests
- plyr >= 1.8.4 suggests
- rmarkdown >= 1.13 suggests
- sf >= 0.7 suggests
- sp >= 1.3 suggests
- testthat >= 2.1.0 suggests