Scikit Mobility

Abstract

1. Introduction

scikit-mobility: mobility analysis in Python

2. Objective

scikit-mobility: mobility analysis in Python

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

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

1. in a MyBinder notebook: [](https://mybinder.org/v2/gh/scikit-mobility/scikit-mobility/master)
2. on Jovian
3. represent trajectories and mobility flows with proper data structures, TrajDataFrame and FlowDataFrame.
4. manage and manipulate mobility data of various formats (call detail records, GPS data, data from social media, survey data, etc.);
5. extract mobility metrics and patterns from data, both at individual and collective level (e.g., length of displacements, characteristic distance, origin-destination matrix, etc.)
6. generate synthetic individual trajectories using standard mathematical models (random walk models, exploration and preferential return model, etc.)
7. generate synthetic mobility flows using standard migration models (gravity model, radiation model, etc.)
8. assess the privacy risk associated with a mobility data set

conda install -n skmob pyproj urllib3 chardet markupsafe

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

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by scikit-mobility and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.