Individual mobility metrics

Requirements, dependencies, and installation

This code has been tested on

Python 3.10, trackintel 1.2.2, geopandas 0.14.0

To create a virtual environment and install the required dependencies, please run the following:

git clone https://github.com/irmlma/mobility-metrics.git
cd mobility-metrics
conda env create -f environment.yml
conda activate metrics

in your working folder. You can then install the package in edit mode using:

pip install -e .

Evaluate mobility behavior using mobility metrics

Dataset distribution plots will be shown after metric calculation, and stored in the .\data\output folder (will be created if not existing). Input data of location visit sequences should be stored in the .\data\input folder. We implement basic mobility metrics as follows:

Basic metrics

Run

python mobmetric/scripts/run_metrics.py

for examples of calculating basic mobility metrics. The metric can be specified with the input arguement metric, accepting one of the arguements [rg, locf, jump,wait]:

Location visitation frquency.
Radius of gyration. Radius of gyration calculation receives the following parameter:
- method of [duration, count]. count calculates with visitation frequency of locations, and duration calculates by additionally weighting the locations by their activity duration.
Jump length. Distance of moving between consecutive locations.
Wait time. Time of waiting between consecutive locations.

Entropy

Random Entropy
Uncorrelated Entropy
Real Entropy Run

python mobmetric/scripts/run_entropy.py

for examples of calculating entropy for location traces.

Mobility motifs

Run

python mobmetric/scripts/run_motifs.py

for examples of calculating mobility motifs. Motifs calculation receives the following parameter:

proportion_filter default 0.005. Filter to control how frequent a pattern could be considered a motifs, e.g., 0.005 means patterns occuring more than 0.5% of all the patterns are considered motifs.
time_format of [absolute, relative], default relative. Specify whether the input dataset is in absolute time format (e.g., time available as columns started_at and finished_at) or in relative time format (e.g., time available as columns started_at and duration) (obtained directly from mobility simulation).

TODO:

None

Citation

If you find this code useful for your work or use it in your project, please consider citing:

@misc{hong_revealing_2023,
    title={Revealing behavioral impact on mobility prediction networks through causal interventions},
    author={Hong, Ye and Xin, Yanan and Dirmeier, Simon and Perez-Cruz, Fernando and Raubal, Martin},
    publisher={arXiv},
    year={2023},
    url = {https://arxiv.org/abs/2311.11749},
    doi = {10.48550/arXiv.2311.11749},
}

Contact

If you have any questions, open an issue or let me know:

Ye Hong {hongy@ethz.ch}