QGIS Planet

The latest releases of MovingPandas and Trajectools come with many “under the hood” changes that aim to make your movement analytics faster:

1. Instead of immediately creating a GeoPandas GeoDataFrame and populating the geometry column with Point objects, MovingPandas now has “lazy geometry column creation” that holds off on this operation until / if the geometries are actually needed. This way, for many operations, no geometry objects have to be generated at all.
2. MovingPandas TrajectorySplitters now support parallel processing and Trajectools uses parallel processing whenever available (e.g. for adding speed & direction metrics, detecting stops, splitting trajectories).
3. When a minimum length is specified for trajectories, MovingPandas now avoids computing the total trajectory length and, instead, immediately stops once the threshold value has been reached (“early skip”).
4. Trajectools now offers the option to skip computation of movement metrics (speed & direction). This way, we can skip unnecessary computations and leverage the lazy geometry column creation, wherever applicable.

Let’s have a look at some example performance measurements!

Example 1: MovingPandas ValueChangeSplitter

The ValueChangeSplitter splits trajectories when it detects a value change in the specified column. This is useful, for example, to split up public trajectories that contain a “next_stop” column.

The following graph shows ValueChangeSplitter runtimes for different minimum trajectory length settings (from 0 to 1km, 100km, and 10,000km):

We see that the new, lazy geometry column initialization outperforms the old original code in all cases (e.g. 57% runtime reduction for 1km), except for the worst-case scenario, when the original implementation discards all trajectories as too short right from the start. (For most use cases, min_length will be set to rather small values to avoid creation of undesired short trajectory fragments, similar to sliver polygons in classic geometry operations.)

Additionally, we can engage multiprocessing by setting the n_processes parameter, e.g. to the number of CPUs to achieve further speedup:

Example 2: Trajectools

By applying all above-mentioned speedup techniques, Trajectools is now considerably faster. For example, the following runtime reductions can be achieved by deactivating the “Add movement metrics (speed, direction)” option in the algorithm dialog:

• Create trajectories: 62%
• Spatiotemporal generalization (TDTR): 78%
• Temporal generalization: 81%
• Split trajectories at stops: 53%

I have also updated the default trajectory points output style. It now uses a graduated renderer to visualize the speed values (if they have been calculated) instead of the previously used data-defined override. This makes the style faster to customize and provides a user-friendly legend:

For more infos, have a look at:

• MovingPandas 0.22 release notes
• Trajectools 2.6 release notes

Enjoy the latest performance increases!

Sorry, this entry is only available in French.

As long time sponsors of FOSSGIS, we stepped up the game this year and became Platinum Sponsors for FOSSGIS 2025. We are proud to be part of a thriving open-source GIS community and to contribute to such a great conference. Here’s a recap of everything we were involved in:

🚀 Talks & Presentations

🌍 QField: New Strategy and Application Potential
Berit and Marco presented how QField, with over 1 million downloads and 350,000 active users, is now recognized as Digital Public Good aligned with the UN Sustainable Development Goals. Marco also shared the vision and mission behind QField’s development — highlighting our commitment to empowering field teams across the globe with open, user-friendly tools for data collection.
Real-world stories illustrated how QField helps bridge data gaps to support informed, sustainable decision-making.
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⚙️ QField in Practice: Fieldwork Made Easy
Berit and Michael led an interactive workshop demonstrating how to develop a QField project from scratch. The goal was for each participant to create and sync their own field study project using QFieldCloud, focused on collecting data on flowering plants in the picturesque “Schlussgarten.”
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🌐 When Web Meets Desktop
Matthias demonstrated how Django can be used to build consumable geodata layers via OGC API - Features endpoints. His talk covered how to use Python and Django ORM to elegantly define data models and business logic, offering an alternative to complex database logic.
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☁️ Extending QFieldCloud – Ideas and Practical Examples
Michael showed how QFieldCloud can be extended with Django apps, sharing practical implementations such as automated project generation and integration of remote sensing workflows.
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🔌 QField Plugins – Examples and Possibilities
In a lightning talk, Michael introduced useful QField plugins, explained how to install and use them, and explored how they can enhance your mobile GIS workflows.
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🧪 Hands-on qgis-js: Building Interactive QGIS-Based Web Maps
In this practical workshop, Michael guided participants through using qgis-js, an exciting new project that brings QGIS functionality directly into the browser.
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💬 QGIS AMA Expert Session
Matthias and Marco hosted a live Q&A session where attendees could ask everything about QGIS development, best practices, organisation and real-world applications.

🤝 At the Booth

Our QField booth was buzzing with activity all week – from plugin demos and project showcases to deep dives into QFieldCloud and field mapping workflows. We had great conversations, received valuable feedback, and met many enthusiastic users.

💚 Supporting Open Source

We were proud to be Platinum Sponsors of FOSSGIS 2025. Supporting open-source events like this is essential for fostering innovation, collaboration, and community-driven growth in the GIS world.

👋 Looking Ahead

Thank you to the organisers, speakers, and everyone who joined us in Münster. We left the event full of ideas, motivation, and appreciation for this community – and we’re already looking forward to the next FOSSGIS!

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