{"id":24182067,"url":"https://github.com/munterfi/ertg3d","last_synced_at":"2025-09-21T03:31:37.817Z","repository":{"id":43706515,"uuid":"114632575","full_name":"munterfi/eRTG3D","owner":"munterfi","description":"Empirically Informed Random Trajectory Generator in 3-D.","archived":false,"fork":false,"pushed_at":"2022-02-23T14:41:18.000Z","size":55214,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-06-11T16:04:51.435Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"https://munterfi.github.io/eRTG3D/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/munterfi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":".github/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-12-18T11:22:07.000Z","updated_at":"2023-10-10T06:38:02.000Z","dependencies_parsed_at":"2022-08-21T06:50:36.677Z","dependency_job_id":null,"html_url":"https://github.com/munterfi/eRTG3D","commit_stats":null,"previous_names":[],"tags_count":11,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/munterfi%2FeRTG3D","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/munterfi%2FeRTG3D/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/munterfi%2FeRTG3D/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/munterfi%2FeRTG3D/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/munterfi","download_url":"https://codeload.github.com/munterfi/eRTG3D/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":233707211,"owners_count":18717388,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["3d","birds","conditional-empirical-random-walk","gliding-and-soaring","machine-learning","movement-ecology","random-trajectory-generator","random-walk","rstats","rstats-package","simulation","trajectory-generation"],"created_at":"2025-01-13T07:27:17.681Z","updated_at":"2025-09-21T03:31:32.367Z","avatar_url":"https://github.com/munterfi.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# eRTG3D \u003cimg src=\"man/figures/logo.png\" align=\"right\" alt=\"\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n\n[![CRAN status](https://www.r-pkg.org/badges/version/eRTG3D)](https://CRAN.R-project.org/package=eRTG3D)\n[![CRAN downloads](https://cranlogs.r-pkg.org/badges/last-month/eRTG3D?color=brightgreen)](https://CRAN.R-project.org/package=eRTG3D)\n[![R build status](https://github.com/munterfi/eRTG3D/workflows/R-CMD-check/badge.svg)](https://github.com/munterfi/eRTG3D/actions)\n[![pkgdown](https://github.com/munterfi/eRTG3D/workflows/pkgdown/badge.svg)](https://github.com/munterfi/eRTG3D/actions)\n[![Codecov test coverage](https://codecov.io/gh/munterfi/eRTG3D/branch/master/graph/badge.svg)](https://app.codecov.io/gh/munterfi/eRTG3D?branch=master)\n\n\u003c!-- badges: end --\u003e\n\nThe **e**mpirically informed **R**andom **T**rajectory **G**enerator in three dimensions (eRTG3D)\nis an algorithm to generate realistic random trajectories in a 3-D space\nbetween 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.\n\nThe 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)).\n\n## Getting started\n\n```r\n# Install release version from CRAN\ninstall.packages(\"eRTG3D\")\n\n# Install development version from GitHub\nremotes::install_github(\"munterfi/eRTG3D\")\n```\n\n## Features\n\nThe **eRTG3D** package contains functions to:\n\n* calculate **movement parameters of 3-D GPS tracking data**, turning angle, lift angle and step length\n* **extract distributions** from movement parameters;\n  1. **P probability** - The mover's behavior from its perspective\n  2. **Q probability** - The pull towards the target\n* simulate **Unconditional Empirical Random Walks (UERW)**\n* simulate **Conditional Empirical Random Walks (CERW)**\n* simulate conditional **gliding and soaring behavior** of birds between two given points\n* **statistically test** the simulated tracks against the original input\n* **visualize** tracks, simulations and distributions in 3-D and 2-D\n* conduct a basic **point cloud analysis**; extract **3-D Utilization Distributions (UDs)** from observed or simulated tracking data by means of voxel counting\n* project 3-D tracking data into different **Coordinate Reference Systems (CRSs)**\n* export data to **sf package objects**; 'sf, data.frames'\n* manipulate **extent of raster layers**\n\n## Contributing\n\nContributions 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).\n\nThis 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.\n\n## References\n\nUnterfinger 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.\n\nTechnitis 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmunterfi%2Fertg3d","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmunterfi%2Fertg3d","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmunterfi%2Fertg3d/lists"}