If you downloaded Trajectools 2.1 and ran into troubles due to the introduced scikit-mobility and gtfs_functions dependencies, please update to Trajectools 2.2.
This new version makes it easier to set up Trajectools since MovingPandas is pip-installable on most systems nowadays and scikit-mobility and gtfs_functions are now truly optional dependencies. If you don’t install them, you simply will not see the extra algorithms they add:
If you encounter any other issues with Trajectools or have questions regarding its usage, please let me know in the Trajectools Discussions on Github.
Today marks the 2.1 release of Trajectools for QGIS. This release adds multiple new algorithms and improvements. Since some improvements involve upstream MovingPandas functionality, I recommend to also update MovingPandas while you’re at it.
If you have installed QGIS and MovingPandas via conda / mamba, you can simply:
Afterwards, you can check that the library was correctly installed using:
import movingpandas as mpd mpd.show_versions()
Trajectools 2.1
The new Trajectools algorithms are:
Trajectory overlay — Intersect trajectories with polygon layer
Privacy — Home work attack (requires scikit-mobility)
This algorithm determines how easy it is to identify an individual in a dataset. In a home and work attack the adversary knows the coordinates of the two locations most frequently visited by an individual.
Furthermore, we have fixed issue with previously ignored minimum trajectory length settings.
Scikit-mobility and gtfs_functions are optional dependencies. You do not need to install them, if you do not want to use the corresponding algorithms. In any case, they can be installed using mamba and pip:
There are a couple of existing plugins that deal with GTFS. However, in my experience, they either don’t integrate with Processing and/or don’t provide the functions I was expecting.
So far, we have two GTFS algorithms to cover essential public transport analysis needs:
The “Extract shapes” algorithm gives us the public transport routes:
The “Extract segments” algorithm has one more options. In addition to extracting the segments between public transport stops, it can also enrich the segments with the scheduled vehicle speeds:
Here you can see the scheduled speeds:
To show the stops, we can put marker line markers on the segment start and end locations:
The segments contain route information and stop names, so these can be extracted and used for labeling as well:
This is the first version without the “experimental” flag. If you look at the plugin release history, you will see that the previous release was from 2020. That’s quite a while ago and a lot has happened since, including the development of MovingPandas.
Let’s have a look what’s new!
The old “Trajectories from point layer”, “Add heading to points”, and “Add speed (m/s) to points” algorithms have been superseded by the new “Create trajectories” algorithm which automatically computes speeds and headings when creating the trajectory outputs.
“Day trajectories from point layer” is covered by the new “Split trajectories at time intervals” which supports splitting by hour, day, month, and year.
“Clip trajectories by extent” still exists but, additionally, we can now also “Clip trajectories by polygon layer”
There are two new event extraction algorithms to “Extract OD points” and “Extract OD points”, as well as the related “Split trajectories at stops”. Additionally, we can also “Split trajectories at observation gaps”.
Trajectory outputs, by default, come as a pair of a point layer and a line layer. Depending on your use case, you can use both or pick just one of them. By default, the line layer is styled with a gradient line that makes it easy to see the movement direction:
while the default point layer style shows the movement speed:
How to use Trajectools
Trajectools 2.0 is powered by MovingPandas. You will need to install MovingPandas in your QGIS Python environment. I recommend installing both QGIS and MovingPandas from conda-forge:
The plugin download includes small trajectory sample datasets so you can get started immediately.
Outlook
There is still some work to do to reach feature parity with MovingPandas. Stay tuned for more trajectory algorithms, including but not limited to down-sampling, smoothing, and outlier cleaning.
I’m also reviewing other existing QGIS plugins to see how they can complement each other. If you know a plugin I should look into, please leave a note in the comments.
I’m continuously testing the algorithms integrated so far to see if they work as GIS users would expect and can to ensure that they can be integrated in Processing model seamlessly.
Because naming things is tricky, I’m currently struggling with how to best group the toolbox algorithms into meaningful categories. I looked into the categories mentioned in OGC Moving Features Access but honestly found them kind of lacking:
… but I’m not convinced yet. So take the above listed three categories with a grain of salt. Those may change before the release. (Any inputs / feedback / recommendation welcome!)
Let me close this quick status update with a screencast showcasing stop detection in AIS data, featuring the recently added trajectory styling using interpolated lines:
