mobilkit 0.2.7

A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data

mobilkit

A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data.

mobilkit provides a set of tools to analyze mobility traces to assess the users response to extreme events. Try mobilkit without installing it in a MyBinder notebook:

Table of contents

1. Documentation
2. Collaborate with us
3. Installation
   • with pip
   • with conda
   • test installation
4. Tutorials
5. Examples
   • Quickstart
6. Citing
7. Credits and contacts

Documentation

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.

You are encouraged to report any issue or problem encountered while using the software or to seek for support.

If you would like to contribute or add functionalities to mobilkit, feel free to fork the project, open an issue and contact us.

Installation

Install with pip

Start by creating an environment and install mobilkit there.

1. Create an environment mobilkit

    python3 -m venv mobilkit
    # or, on Windows
    python -m venv c:\path\to\mobilkit
   

2. Activate

    source mobilkit/bin/activate
    # or, on Windows
    c:\path\to\mobilkit\Scripts\activate.bat
   

3. Update pip

    pip install --upgrade pip
   

4. Install mobilkit (this will also install Dask and all the needed modules)

    pip install mobilkit
   

5. OPTIONAL to use mobilkit on the jupyter notebook

   • Activate the virutalenv:

       source mobilkit/bin/activate
     

   • Install jupyter notebook:

       pip install jupyter 
     

   • Run jupyter notebook

       jupyter notebook
     

   • (Optional) install the kernel with a specific name to your existing notebook server

       source mobilkit/bin/activate
       pip install ipykernel
       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.

mobilkit by default will only install core packages needed to run the main functions. There are three optional packages of dipendencies (the mobilkit[complete] installs everything):

• [viz] will install contextily, needed to visualize map backgrounds in certain viz functions;
• [doc] will install all the needed packages to build the docs;
• [skmob] will install scikit-mobility as well;
• [locations] will also install infostop to detect users' typical locations.

Install with conda

TODO

Test the installation

> source activate mobilkit
(mobilkit)> python
>>> import mobilkit
>>>

Examples

Several notebooks are found in the docs/examples folder, we resume here the most important ones.

Quickstart

We show the basic usage and functionalities in the mobilkit_tutorial.ipynb notebook.

Citing

If you use mobilkit please cite us:

Enrico Ubaldi, Takahiro Yabe, Nicholas K. W. Jones, Maham Faisal Khan, Satish V. Ukkusuri, Riccardo Di Clemente and Emanuele Strano Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data, 2021, KDD 2021 Humanitarian Mapping Workshop, https://arxiv.org/abs/2107.14297

Bibtex:

@misc{ubaldi2021mobilkit,
    title={Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data},
    author={Enrico Ubaldi and Takahiro Yabe and Nicholas K. W. Jones and Maham Faisal Khan and Satish V. Ukkusuri and Riccardo {Di Clemente} and Emanuele Strano},
    year={2021},
    eprint={2107.14297},
    primaryClass={cs.CY},
    archivePrefix={arXiv},
}

Credits and contacts

This code has been developed by Mindearth, the Global Facility for Disaster Reduction and Recovery (GFDRR) and Purdue University.

Funding was provided by the Spanish Fund for Latin America and the Caribbean (SFLAC) under the Disruptive Technologies for Development (DT4D) program.

The code is released under the MIT license (see the LICENSE file for details).