https://github.com/dbvis-ukon/movekit

Simple and effective tools for the analysis of movement data
https://github.com/dbvis-ukon/movekit

collective-behaviour movement-data spatio-temporal-analysis spatio-temporal-data

Last synced: 4 months ago
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Simple and effective tools for the analysis of movement data

• Host: GitHub
• URL: https://github.com/dbvis-ukon/movekit
• Owner: dbvis-ukon
• License: gpl-3.0
• Created: 2019-07-30T11:29:12.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-03-06T12:09:09.000Z (over 3 years ago)
• Last Synced: 2025-11-27T18:38:07.423Z (8 months ago)
• Topics: collective-behaviour, movement-data, spatio-temporal-analysis, spatio-temporal-data
• Language: Python
• Homepage: https://movekit.readthedocs.io/en/latest/
• Size: 50.7 MB
• Stars: 27
• Watchers: 3
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.rst
  • License: LICENSE

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README

          
MOVEKIT

======



    

    

    

    

    

    



`movekit` is an open-source software package for the processing and analysis of movement data.

__`movekit` supports different tasks:__

* Data pre-processing

  * Clean data (remove duplicates, drop missing values, etc.)

  * Normalize and filter the data

  * ...

* Feature extraction:

  * Extract different features such as the distance covered, average speed, the average acceleration, etc. 

  * Apply time series analysis on these extracted features

  * Check different distances (euclidean & hausdorff) between movers

  * Detect outliers in data

  * ...

* Group-level analysis

  * Calculate centroids and medoids of the group of movers for different time steps

  * Compute polarization of movers

  * Identify different clusters/groups

  * Obtain dynamic time warping of all mover trajectories 

  * ...

* Spatial data analysis:

  * Create convex hull, voronoi diagram and delaunay triangulation for all movers at each time step

  * Extract areas of the created objects

* Network analysis with networkX

  * Create networks created for each time step and examine their attributes (centroid, polarization, total distance, mean speed, ...)

  * Investigate individual nodes of each time steps network graph

  * Investigate individual edges of each time steps network graph

  * Track development of network graphs over time

  * ...

* Plotting analysis results:

  * Create basic plots for features such as acceleration or speed

  * Plot movement of movers in static or animated images

  * Create interactive map to plot geo data

`movekit` provides support for movement data and trajectories in different format:

__Data:__

* 2-dimensional data in the Euclidean space

* 3-dimensional data in the Euclidean space

* GPS coordinates (latitude and longitude)

* Data with different time formats

* Data in (Geo)JSON format

* Data from Movebank data base

---

## Installation

The easiest way to install *movekit* is by using `pip` :

    pip install movekit

---

## Docs & Demo

The following website contains the [documentation](https://movekit.readthedocs.io/en/latest/)

You can view a demo of common features here:

[Jupyter Notebooks](examples/).

---

### License

Released under a GNU General Public License. See the [LICENSE](LICENSE) file for details. List of [Authors](AUTHORS.rst)

The package is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117 – 422037984.