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:
