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MovingPandas is a Python library for movement data exploration and analysis.

MovingPandas provides trajectory data structures and functions for handling movement data based on Pandas, GeoPandas, and HoloViz.

The official MovingPandas API documentation is hosted on ReadTheDocs.

For more information about individual releases, check out the Changelog.

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Here are some examples:

Trajectory plots View Jupyter Notebook

Create interactive trajectory plots, including multiple linked plots with ease …


For all types of tracking data, e.g. video-based trajectories

Bicycle tracks from object tracking in videos

Including plots in custom projections View Jupyter Notebook

MovingPandas Iceberg trajectory in custom SouthPolarStereo projection

Stop detection View Jupyter Notebook

Detect stops in trajectories, extract them as points or segments, and use them to split trajectories


Trajectory generalization View Jupyter Notebook

Generalize trajectories using spatial, temporal, and spatiotemporal methods


Trajectory cleaning & smoothing View Jupyter Notebook

Clean and sooth trajectories by removing outliers and applying Kalman filters

movingpandas smooth

Trajectory aggregation View Jupyter Notebook

Aggregate trajectories to explore larger patterns


What’s next?

MovingPandas is under active development and there are some exciting features coming up. If you’d like to contribute to this project, you’re welcome to head on over to the Github repo!

Citation information

Please cite [0] when using MovingPandas in your research and reference the appropriate release version. All releases of MovingPandas are listed on Zenodo where you will find citation information, including DOIs.

[0] Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum ‒ Journal of Geographic Information Science 2019, 1-2019, 54-68. doi:10.1553/giscience2019_01_s54.

If you are curious about who else is using MovingPandas, check out the list of publications citing MovingPandas.