Teetool – a probabilistic trajectory analysis tool
Teetool – a probabilistic trajectory analysis tool
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.
Trajectory Analysis, Visual Statistics, Visual Analytics
1-6
Eerland, Willem
7f5826c3-536f-4fdc-955e-0f9870c96a0e
Box, Simon
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Fangohr, Hans
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Sobester, Andras
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17 May 2017
Eerland, Willem
7f5826c3-536f-4fdc-955e-0f9870c96a0e
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Eerland, Willem, Box, Simon, Fangohr, Hans and Sobester, Andras
(2017)
Teetool – a probabilistic trajectory analysis tool.
Journal of Open Research Software, 5 (1), , [1].
(doi:10.5334/jors.163).
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.
Text
163-1923-1-PB
- Version of Record
More information
Submitted date: 31 January 2017
Accepted/In Press date: 4 May 2017
Published date: 17 May 2017
Keywords:
Trajectory Analysis, Visual Statistics, Visual Analytics
Organisations:
Aeronautics, Astronautics & Comp. Eng, Computational Engineering & Design Group, Civil Maritime & Env. Eng & Sci Unit, Transportation Group, Education Hub
Identifiers
Local EPrints ID: 408279
URI: http://eprints.soton.ac.uk/id/eprint/408279
PURE UUID: 8b0aff4c-cb8a-4a39-9756-f5b6af7d3b2a
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Date deposited: 19 May 2017 04:02
Last modified: 16 Mar 2024 03:26
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Contributors
Author:
Willem Eerland
Author:
Simon Box
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