The Trajectools plugin adds mobility data analysis algorithms to the QGIS Processing toolbox.

Trajectools requires MovingPandas >= 0.22.3 and optionally integrates scikit-mobility (for privacy tests), stonesoup (for smoothing), and gtfs_functions (for GTFS data support).

The recommended way to install these dependencies is through conda/mamba:

(base) conda create -n qgis -c conda-forge python=3.12 (base) conda activate qgis (qgis) mamba install -c conda-forge qgis movingpandas scikit-mobility stonesoup (qgis) pip install gtfs_functions==2.5 h3==3.7.7 

Note: Do not upgrade to Python 3.13 if you want to use the GTFS functions. (See https://codeberg.org/movingpandas/trajectools/issues/103 for details.)

If you cannot use conda, you may try installing from the QGIS Python Console:

import pip pip.main(['install', 'movingpandas']) pip.main(['install', 'scikit-mobility']) pip.main(['install', 'stonesoup']) pip.main(['install', 'gtfs_functions']) 

The Trajectools plugin can be installed directly in QGIS using the built-in Plugin Manager:

Figure 1: QGIS Plugin Manager with Trajectools plugin installed.

Figure 2: Trajectools (v2.4) algorithms in the QGIS Processing toolbox

The individual Trajectools algorithms are flexible and modular and can therefore be used on a wide array on input datasets, including, for example, the open Microsoft Geolife dataset a sample of which is included in the plugin repo:

Trajectools: analyzing anything that moves. QGIS User Conference 2025, 2-3 June 2025, Norrköping, Sweden.

Please cite [0] when using Trajectools in your research and reference the appropriate release version using the Zenodo DOI: https://doi.org/10.5281/zenodo.13847642

[0] Graser, A., & Dragaschnig, M. (2024, June). Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics. In 2024 25th IEEE International Conference on Mobile Data Management (MDM) (pp. 235-238). IEEE.

@inproceedings{graser2024trajectools, title = {Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics}, author = {Graser, Anita and Dragaschnig, Melitta}, booktitle = {2024 25th IEEE International Conference on Mobile Data Management (MDM)}, pages = {235--238}, year = {2024}, organization = {IEEE}, doi = {10.1109/MDM61037.2024.00048}, } 

This work was supported in part by the Horizon Framework Programme of the European Union under grant agreement No. 101093051 (EMERALDS).