bandicoot

an open-source Python toolbox to analyze mobile phone metadata

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Data visualization

bandicoot has built-in visualization tools. Load a user's file and visualize his social graph, mobility pattern, and interactions.

Check out our IPython notebook for live examples.

Data verification

We detect and warn you of potential missing data (no location, wrong date…). bandicoot automatically exports more than 40 reporting metrics to help you detect issues.

Bandicoot interactive visualization

They use bandicoot

Orange
Telenor
MIT
FBK
ENS de Lyon
You Technology

Research papers

Flying, phones and flu: Anonymized call records suggest that Keflavik International Airport introduced pandemic H1N1 into Iceland in 2009
Combining disparate data sources for improved poverty prediction and mapping
Predicting Personality Using Novel Mobile Phone-Based Metrics
Big Data-Driven Marketing: How machine learning outperforms marketers' gut-feeling
Improving official statistics in emerging markets using machine learning and mobile phone data
Modeling the Temporal Nature of Human Behavior for Demographics Prediction

If you use bandicoot in your research please cite it as: de Montjoye, Y. A., Rocher, L., & Pentland, A. S. (2016). bandicoot: a Python Toolbox for Mobile Phone Metadata. Journal of Machine Learning Research, 17(175), 1-5.

Our team

Yves-Alexandre de Montjoye

Imperial College London

Luc Rocher

Université catholique de Louvain

Alex ‘Sandy’ Pentland

MIT Media Lab

As well as Florent Robic, Kevin Mustelier, Walter Menendez, Zachary Neeley, Keeley Erhardt, William Navarre, Brian Sweatt, and Bjarke Felbo.

Newsletter

Sign-up to our newsletter to receive updates (2-3 times a year) about bandicoot.

Contact

Feel free to ask questions and report issues on our GitHub page or to contact us at X@Y where X=demontjoye, Y=imperial.ac.uk.