Teetool – A Probabilistic Trajectory Analysis Tool. Issue 1 (17th May 2017)
- Record Type:
- Journal Article
- Title:
- Teetool – A Probabilistic Trajectory Analysis Tool. Issue 1 (17th May 2017)
- Main Title:
- Teetool – A Probabilistic Trajectory Analysis Tool
- Authors:
- Eerland, Willem
Box, Simon
Fangohr, Hans
Sóbester, András - Abstract:
- Teetool is a Python package which models and visualises motion patterns found in two- and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel. The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected. Teetool is available as a Python package on GitHub, and includes Jupyter Notebooks, showing examples for two- and three-dimensional trajectory data.
- Is Part Of:
- Journal of open research software. Volume 5:Issue 1(2017)
- Journal:
- Journal of open research software
- Issue:
- Volume 5:Issue 1(2017)
- Issue Display:
- Volume 5, Issue 1 (2017)
- Year:
- 2017
- Volume:
- 5
- Issue:
- 1
- Issue Sort Value:
- 2017-0005-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-05-17
- Subjects:
- Python -- Gaussian process -- motion patterns -- trajectory patterns -- confidence region
Computer software -- Reusability -- Periodicals
Open source software -- Periodicals
005 - Journal URLs:
- http://openresearchsoftware.metajnl.com/ ↗
- DOI:
- 10.5334/jors.163 ↗
- Languages:
- English
- ISSNs:
- 2049-9647
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 14751.xml