scikit-mobility 1.1.2

A toolbox for analyzing and processing mobility data.

[](https://zenodo.org/badge/latestdoi/184337448) [](http://hits.dwyl.io/scikit-mobility/scikit-mobility)

# scikit-mobility - mobility analysis in Python

<img src=”logo_skmob.png” width=300/>

###### Try scikit-mobility without installing it in a MyBinder notebook: [](https://mybinder.org/v2/gh/scikit-mobility/scikit-mobility/master)

scikit-mobility is a library for human mobility analysis in Python. The library allows to:

• represent trajectories and mobility flows with proper data structures, TrajDataFrame and FlowDataFrame.

• manage and manipulate mobility data of various formats (call detail records, GPS data, data from social media, survey data, etc.);

• extract mobility metrics and patterns from data, both at individual and collective level (e.g., length of displacements, characteristic distance, origin-destination matrix, etc.)

• generate synthetic individual trajectories using standard mathematical models (random walk models, exploration and preferential return model, etc.)

• generate synthetic mobility flows using standard migration models (gravity model, radiation model, etc.)

• assess the privacy risk associated with a mobility data set

## Table of contents 1. [Documentation](#documentation) 2. [Citing](#citing) 3. [Collaborate with us](#collaborate) 4. [Installation](#installation)

• [with conda](#installation_conda)

• [without conda](#installation_no_conda)

• [test installation](#test_installation)

5. [Tutorials](#tutorials)

6. [Examples](#examples)

   • [TrajDataFrame](#trajdataframe)

   • [FlowDataFrame](#flowdataframe)

   • [Preprocessing](#preprocessing)

   • [Measures](#measures)

   • [Collective generative models](#collective_models)

   • [Individual generative models](#individual_models)

   • [Privacy](#privacy)

<a id=’documentation’></a> ## Documentation The documentation of scikit-mobility’s classes and functions is available at: https://scikit-mobility.github.io/scikit-mobility/

<a id=’citing’></a> ## Citing

if you use scikit-mobility please cite the following paper:

> Luca Pappalardo, Filippo Simini, Gianni Barlacchi and Roberto Pellungrini. > scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data, > 2019, https://arxiv.org/abs/1907.07062

Bibtex: `bibtex @misc{pappalardo2019scikitmobility, title={scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data}, author={Luca Pappalardo and Filippo Simini and Gianni Barlacchi and Roberto Pellungrini}, year={2019}, eprint={1907.07062}, archivePrefix={arXiv}, primaryClass={physics.soc-ph} } `

<a id=’collaborate’></a> ## Collaborate with us scikit-mobility is an active project and any contribution is welcome.

If you would like to include your algorithm in scikit-mobility, feel free to fork the project, open an issue and contact us.

<a id=’installation’></a> ## Install First, clone the repository - this creates a new directory ./scikit_mobility.

git clone https://github.com/scikit-mobility/scikit-mobility scikit_mobility

<a id=’installation_conda’></a> ### with conda - miniconda

1. Create an environment skmob and install pip

   conda create -n skmob pip python=3.7

2. Activate

   source activate skmob

3. Install skmob

   cd scikit-mobility python setup.py install

   If the installation of a required library fails, reinstall it with conda install.

4. OPTIONAL to use scikit-mobility on the jupyter notebook

   • Install the kernel

     conda install ipykernel

   • Open a notebook and check if the kernel skmob is on the kernel list. If not, run the following:

     • On Mac and Linux

       env=$(basename echo $CONDA_PREFIX) python -m ipykernel install –user –name “$env” –display-name “Python [conda env:”$env”]”

     • On Windows

       python -m ipykernel install –user –name skmob –display-name “Python [conda env: skmob]”

exclamation:

You may run into dependency issues if you try to import the package in Python. If so, try installing the following packages as followed.

` conda install -n skmob pyproj urllib3 chardet markupsafe ` <a id=’installation_no_conda’></a> ### without conda (python >= 3.6 required)

1. Create an environment skmob

   python3 -m venv skmob

2. Activate

   source skmob/bin/activate

3. Install skmob

   cd scikit-mobility python setup.py install

4. OPTIONAL to use scikit-mobility on the jupyter notebook

   • Activate the virutalenv:

     source skmob/bin/activate

   • Install jupyter notebook:

     pip install jupyter

   • Run jupyter notebook

     jupyter notebook

   • (Optional) install the kernel with a specific name

     ipython kernel install –user –name=skmob

# Related packages [movingpandas](https://github.com/anitagraser/movingpandas) is a similar package that deals with movement data. Instead of implementing new data structures tailored for trajectories (TrajDataFrame) and mobility flows (FlowDataFrame), movingpandas describes a trajectory using a geopandas GeoDataFrame. There is little overlap in the covered use cases and implemented functionality (comparing [scikit-mobility tutorials](https://github.com/scikit-mobility/tutorials) and [movingpandas tutorials](https://github.com/anitagraser/movingpandas/tree/master/tutorials)): scikit-mobility focuses on computing human mobility metrics, generating synthetic trajectories and assessing privacy risks of mobility datasets. movingpandas on the other hand focuses on spatio-temporal data exploration with corresponding functions for data manipulation and analysis.