These releases are a huge step forward towards making MovingPandas easier to install with fewer mandatory dependencies. All interactive plotting libraries are now optional. So if you are using MovingPandas for trajectory data processing in the background and don’t need the interactive visualization features, the number of necessary libraries is now much lower. This (and the fact that GeoPandas is now shipped with OSGeo4W) will also make it easier to use MovingPandas in QGIS plugins.
We have further improved our repo setup by adding an action that automatically creates and publishes packages from releases, heavily inspired by the work of the GeoPandas team.
Last but not least, we’ve created a Twitter account for the project. (And might soon add a Mastodon account as well.)
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.
New functions to add a timedelta column and get the trajectory sampling interval #233
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
The new distance measures are covered in tutorial #11:
Computing distances between trajectories, as illustrated in tutorial #11Computing distances between a trajectory and other geometry objects, as illustrated in tutorial #11
But don’t miss the great features covered by the other notebooks, such as outlier cleaning and smoothing:
Trajectory cleaning and smoothing, as illustrated in tutorial #10
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.
Speed and direction column names can now be customized
If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our recently opened discussion forum.
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
Besides others examples, the movingpandas-examples repo contains the following tech demo: an interactive app built with Panel that demonstrates different MovingPandas stop detection parameters
To start the app, open the stopdetection-app.ipynb notebook and press the green Panel button in the Jupyter Lab toolbar:
The Kalman filter in action on the Geolife sample: smoother, less jiggly trajectories.Top-Down Time Ratio generalization aka trajectory compression in action: reduces the number of positions along the trajectory without altering the spatiotemporal properties, such as speed, too much.
Behind the scenes, Ray Bell took care of moving testing from Travis to Github Actions, and together we worked through the steps to ensure that the source code is now properly linted using flake8 and black.
Being able to work with so many awesome contributors has made this release really special for me. It’s great to see the project attracting more developer interest.
As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:
In the last few days, there’s been a sharp rise in interest in vessel movements, and particularly, in understanding where and why vessels stop. Following the grounding of Ever Given in the Suez Canal, satellite images and vessel tracking data (AIS) visualizations are everywhere:
Using movement data analytics tools, such as MovingPandas, we can dig deeper and explore patterns in the data.
The MovingPandas.TrajectoryStopDetector is particularly useful in this situation. We can provide it with a Trajectory or TrajectoryCollection and let it detect all stops, that is, instances were the moving object stayed within a certain area (with a diameter of 1000m in this example) for a an extended duration (at least 3 hours).
The resulting stop segments include spatial and temporal information about the stop location and duration. To make this info more easily accessible, let’s turn the stop segment TrajectoryCollection into a point GeoDataFrame:
stop_pts = gpd.GeoDataFrame(columns=['geometry']).set_geometry('geometry')
stop_pts['stop_id'] = [track.id for track in stops.trajectories]
stop_pts= stop_pts.set_index('stop_id')
for stop in stops:
stop_pts.at[stop.id, 'ID'] = stop.df['ID'][0]
stop_pts.at[stop.id, 'datetime'] = stop.get_start_time()
stop_pts.at[stop.id, 'duration_h'] = stop.get_duration().total_seconds()/3600
stop_pts.at[stop.id, 'geometry'] = stop.get_start_location()
Indeed, I think the next version of MovingPandas should include a function that directly returns stops as points.
Now we can explore the stop information. For example, the map plot shows that stops are concentrated in three main areas: the northern and southern ends of the Canal, as well as the Great Bitter Lake in the middle. By looking at the timing of stops and their duration in a scatter plot, we can clearly see that the Ever Given stop (red) caused a chain reaction: the numerous points lining up on the diagonal of the scatter plot represent stops that very likely are results of the blockage:
Before the grounding, the stop distribution nicely illustrates the canal schedule. Vessels have to wait until it’s turn for their direction to go through:
You can see the full analysis workflow in the following video. Please turn on the captions for details.
Huge thanks to VesselsValue for supplying the data!
After writing “Towards a template for exploring movement data” last year, I spent a lot of time thinking about how to develop a solid approach for movement data exploration that would help analysts and scientists to better understand their datasets. Finally, my search led me to the excellent paper “A protocol for data exploration to avoid common statistical problems” by Zuur et al. (2010). What they had done for the analysis of common ecological datasets was very close to what I was trying to achieve for movement data. I followed Zuur et al.’s approach of a exploratory data analysis (EDA) protocol and combined it with a typology of movement data quality problems building on Andrienko et al. (2016). Finally, I brought it all together in a Jupyter notebook implementation which you can now find on Github.
There are two options for running the notebook:
The repo contains a Dockerfile you can use to spin up a container including all necessary datasets and a fitting Python environment.
Alternatively, you can download the datasets manually and set up the Python environment using the provided environment.yml file.
The dataset contains over 10 million location records. Most visualizations are based on Holoviz Datashader with a sprinkling of MovingPandas for visualizing individual trajectories.
Point density map of 10 million location records, visualized using Datashader
Line density map for detecting gaps in tracks, visualized using Datashader
Example trajectory with strong jitter, visualized using MovingPandas & GeoViews
I hope this reference implementation will provide a starting point for many others who are working with movement data and who want to structure their data exploration workflow.
(If you don’t have institutional access to the journal, the publisher provides 50 free copies using this link. Once those are used up, just leave a comment below and I can email you a copy.)
Free and Open Source GIS Ramblings 