Tag: movingpandas

Today marks the release of Trajectools 2.3 which brings a new set of algorithms, including trajectory generalizing, cleaning, and smoothing.

To give you a quick impression of what some of these algorithms would be useful for, this post introduces a trajectory preprocessing workflow that is quite general-purpose and can be adapted to many different datasets.

We start out with the Geolife sample dataset which you can find in the Trajectools plugin directory’s sample_data subdirectory. This small dataset includes 5908 points forming 5 trajectories, based on the trajectory_id field:

We first split our trajectories by observation gaps to ensure that there are no large gaps in our trajectories. Let’s make at cut at 15 minutes:

This splits the original 5 trajectories into 11 trajectories:

When we zoom, for example, to the two trajectories in the north western corner, we can see that the trajectories are pretty noisy and there’s even a spike / outlier at the western end:

If we label the points with the corresponding speeds, we can see how unrealistic they are: over 300 km/h!

Let’s remove outliers over 50 km/h:

Better but not perfect:

Let’s smooth the trajectories to get rid of more of the jittering.

(You’ll need to pip/mamba install the optional stonesoup library to get access to this algorithm.)

Depending on the noise values we chose, we get more or less smoothing:

Let’s zoom out to see the whole trajectory again:

Feel free to pan around and check how our preprocessing affected the other trajectories, for example:

This release is the first to support GeoPandas 1.0.

Additionally, this release adds multiple new features, including:

• New explore() function adds interactive folium / leaflet maps
• New support for mfjson trajectory

For the full change log, check out the release page.

We have also revamped the documentation at https://movingpandas.readthedocs.io/ using the PyData Sphinx Theme:

On a related note: if you know what I need to change to get all Trajectory functions listed in the TOC on the right, please let me know.

Last week, I had the pleasure to meet some of the people behind the OGC Moving Features Standard Working group at the IEEE Mobile Data Management Conference (MDM2024). While chatting about the Moving Features (MF) support in MovingPandas, I realized that, after the MF-JSON update & tutorial with official sample post, we never published a complete tutorial on working with MF-JSON encoded data in MovingPandas.

The current MovingPandas development version (to be release as version 0.19) supports:

• Reading MF-JSON MovingPoint (single trajectory features and trajectory collections)
• Reading MF-JSON Trajectory
• Writing MovingPandas Trajectories and TrajectoryCollections to MF-JSON MovingPoint

This means that we can now go full circle: reading — writing — reading.

Reading MF-JSON

Both MF-JSON MovingPoint encoding and Trajectory encoding can be read using the MovingPandas function read_mf_json(). The complete Jupyter notebook for this tutorial is available in the project repo.

Here we read one of the official MF-JSON MovingPoint sample files:

traj = mpd.read_mf_json('data/movingfeatures.json')

Writing MF-JSON

To write MF-JSON, the Trajectory and TrajectoryCollection classes provide a to_mf_json() function:

The resulting Python dictionary in MF-JSON MovingPoint encoding can then be saved to a JSON file, and then read again:

import json
with open('mf1.json', 'w') as json_file:
    json.dump(mf_json, json_file, indent=4)

Similarly, we can read any arbitrary trajectory data set and save it to MF-JSON.

For example, here we use our usual Geolife sample:

gdf = gp.read_file('data/demodata_geolife.gpkg')
tc = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
mf_json = tc.to_mf_json(temporal_columns=['sequence'])

And reading again

import json
with open('mf5.json', 'w') as json_file:
    json.dump(mf_json, json_file, indent=4)
tc = mpd.read_mf_json('mf5.json', traj_id_property='trajectory_id' )

Conclusion

The implemented MF-JSON support covers the basic usage of the encodings. There are some fine details in the standard, such as the distinction of time-varying attribute with linear versus step-wise interpolation, which MovingPandas currently does not support.

If you are working with movement data, I would appreciate if you can give the improved MF-JSON support a spin and report back with your experiences.

With the release of GeoPandas 1.0 this month, we’ve been finally able to close a long-standing issue in MovingPandas by adding support for the explore function which provides interactive maps using Folium and Leaflet.

Explore() will be available in the upcoming MovingPandas 0.19 release if your Python environment includes GeoPandas >= 1.0 and Folium. Of course, if you are curious, you can already test this new functionality using the current development version.

This enables users to access interactive trajectory plots even in environments where it is not possible to install geoviews / hvplot (the previously only option for interactive plots in MovingPandas).

I really like the legend for the speed color gradient, but unfortunately, the legend labels are not readable on the dark background map since they lack the semi-transparent white background that has been applied to the scale bar and credits label.

Speaking of reading / interpreting the plots …

You’ve probably seen the claims that AI will help make tools more accessible. Clearly AI can interpret and describe photos, but can it also interpret MovingPandas plots?

ChatGPT 4o interpretations of MovingPandas plots

Not bad.

And what happens if we ask it to interpret the animated GIF from the beginning of the blog post?

So it looks like ChatGPT extracts 12 frames and analyzes them to answer our question:

Its guesses are not completely off but it made up the facts such as that the view shows “how traffic speeds vary over time”.

The problem remains that models such as ChatGPT rather make up interpretations than concede when they do not have enough information to make a reliable statement.

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:

conda activate qgis
mamba install movingpandas=0.18

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.
• GTFS — Extract segments (requires gtfs_functions)
• GTFS — Extract shapes (requires gtfs_functions)
  • These algorithms extract public transport routes (GTFS shapes) and route segments between stops (GTFS segments) from GTFS ZIP files using gtfs_functions.Feed.shapes and .segments, respectively.

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:

mamba install scikit-mobility
pip install gtfs_functions

MovingPandas 0.18

This release adds multiple new features, including

• Method chaining support for add_speed(), add_direction(), and other functions
• New TrajectoryCollection.get_trajectories(obj_id) function
• New trajectory splitter based on heading angle
• New TrajectoryCollection.intersection(feature) function
• New plotting function hvplot_pts()
• Faster TrajectoryCollection operations through multi-threading
• Added moving object weights support to trajectory aggregator

For the full change log, check out the release page.