Tag: movement data in gis

In this new release, you will find new algorithms, default output styles, and other usability improvements, in particular for working with public transport schedules in GTFS format, including:

• Added GTFS algorithms for extracting stops, fixes #43
• Added default output styles for GTFS stops and segments c600060
• Added Trajectory splitting at field value changes 286fdbd
• Added option to add selected fields to output trajectories layer, fixes #53
• Improved UI of the split by observation gap algorithm, fixes #36

Note: To use this new version of Trajectools, please upgrade your installation of MovingPandas to >= 0.21.2, e.g. using

import pip; pip.main(['install', '--upgrade', 'movingpandas'])

or

conda install movingpandas==0.21.2

tldr; Tired of working with large CSV files? Give GeoParquet a try!

“Parquet is a powerful column-oriented data format, built from the ground up to as a modern alternative to CSV files.” https://geoparquet.org/

(Geo)Parquet is both smaller and faster than CSV. Additionally, (Geo)Parquet columns are typed. Text, numeric values, dates, geometries retain their data types. GeoParquet also stores CRS information and support in GIS solutions is growing.

I’ll be giving a quick overview using AIS data in GeoPandas 1.0.1 (with pyarrow) and QGIS 3.38 (with GDAL 3.9.2).

File size

The example AIS dataset for this demo contains ~10 million rows with 22 columns. I’ve converted the original zipped CSV into GeoPackage and GeoParquet using GeoPandas to illustrate the huge difference in file size: ~470 MB for GeoParquet and zipped CSV, 1.6 GB for CSV, and a whopping 2.6 GB for GeoPackage:

Reading performance

Pandas and GeoPandas both support selective reading of files, i.e. we can specify the specific columns to be loaded. This does speed up reading, even from CSV files:

	Whole file	Selected columns
CSV	27.9 s	13.1 s
Geopackage	2min 12s	20.2 s
GeoParquet	7.2 s	4.1 s

Indeed, reading the whole GeoPackage is getting quite painful.

Here’s the code I used for timing the read times:

As you can see, these times include the creation of the GeoPandas.GeoDataFrame.

If we don’t need a GeoDataFrame, we can read the files even faster:

Non-spatial DataFrames

GeoParquet files can be read by non-GIS tools, such as Pandas. This makes it easier to collaborate with people who may not be familiar with geospatial data stacks.

And reading plain DataFrames is much faster than creating GeoDataFrames:

But back to GIS …

GeoParquet in QGIS

In QGIS, GeoParquet files can be loaded like any other vector layer, thanks to GDAL:

Loading the GeoParquet and GeoPackage files is pretty quick, especially if we zoom into a small region of interest (even though, unfortunately, it doesn’t seem possible to restrict the columns to further speed up loading). Loading the CSV, however, is pretty painful due to the lack of spatial indexing, which becomes apparent very quickly in the direct comparison:

As far as I can tell, my QGIS 3.38 ‘Grenoble’ does not support writing to or editing of GeoParquet files. So I’m limited to reading GeoParquet for now.

However, seeing how much smaller GeoParquets are compared to GeoPackages (and also faster to write), I hope that we will soon get the option to export to GeoParquet.

For now, I’ll start by converting my large CSV files to GeoParquet using GeoPandas.

More reading

If you’re into GeoJSON and/or PyGeoAPI, check out Joana Simoes’ post: “Navigating GeoParquet: Lessons Learned from the eMOTIONAL Cities Project”

And if you want to see a global dataset example, have a look at Matt Travis’ presentation using Overture data:

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:

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.