A set of tools to analyze mobility traces to assess the users response to extreme events.
Table of contents
- Collaborate with us
- Credits and contacts
Full documentation with examples can be found online here, otherwise see the notebooks in docs/examples for a step-by-step coverage of the library or the ones in examples/ for a more detailed showcase of the package's capabilities.
Collaborate with us
mobilkit is an active project and any contribution is welcome.
If you would like to contribute or add functionalities to
mobilkit, feel free to fork the project, open an issue and contact us.
Install with pip
You need to have a running version of
Dask on your system. Once you have it you can create an environment and install mobilkit there.
Create an environment
python3 -m venv mobilkit
Clone the repo and install
git clone https://github.com/mindearth/mobilkit cd mobilkit python setup.py install
OPTIONAL to use
mobilkiton the jupyter notebook
Activate the virutalenv:
Install jupyter notebook:
pip install jupyter
Run jupyter notebook
(Optional) install the kernel with a specific name
ipython kernel install --user --name=mobilkit_env
If you already have
scikit-mobility installed, skip the environment creation and run these commands from the skmob anaconda environment.
Install with conda
Test the installation
> source activate mobilkit (mobilkit)> python >>> import mobilkit >>>
Several notebooks are found in the docs/examples folder, we resume here the most important ones.
We show the basic usage and functionalities in the mobilkit_tutorial.ipynb notebook.
Credits and contacts
This code has been originally developed by Mindearth and the World Bank's Global Facility for Disaster Reduction and Recovery, under the contract number 7194851 solicitation ECS1266750.
The code is released under the MIT license (see the
LICENCE file for details).