scikit-mobility - mobility analysis in Python

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 Location Based Social Networks, survey data, etc.);
extract human 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 dataset

Documentation

if you use scikit-mobility please cite the following paper: https://arxiv.org/abs/1907.07062

First, clone the repository - this creates a new directory ./scikit_mobility.

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

Create an environment skmob and install pip
```
 conda create -n skmob pip
```
Activate
```
 conda activate skmob
```
Install skmob
```
 cd scikit_mobility
 python setup.py install
```
If the installation of a required library fails, reinstall it with conda install.
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:
```
env=$(basename `echo $CONDA_PREFIX`)
python -m ipykernel install --user --name "$env" --display-name "Python [conda env:"$env"]"
```

Install skmob

 cd scikit_mobility
 python setup.py install

> source activate skmob
(skmob)> python
>>> import skmob
>>>