May 6st, 2016 > we released a new version (0.5) which includes an interactive visualization, support for mobile phone recharges, support for Python 3, and clustering algorithms to handle both antenna and GPS locations. The computations are significantly faster and the memory footprint is reduced. This release is available on GitHub and PyPI.
bandicoot provides a complete, easy-to-use environment for data-scientist to analyze mobile phone metadata. With only a few lines of code, load your datasets, visualize the data, perform analyses, and export the results.
bandicoot indicators fall into three categories: individual (e.g. number of calls, text response rate), spatial (e.g. radius of gyration, entropy of places), and social network (e.g. clustering coefficient).
pip install bandicoot
python >>> import bandicoot as bc
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.
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.
Our Android application let you export all your call and text logs into a CSV file. This file can then be imported into the bandicoot toolbox for analysis and visualization.
If you use bandicoot in your research please cite it as: de Montjoye, Y.-A., Quoidbach J., Robic F.*, Pentland A., Predicting people personality using novel mobile phone-based metrics. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction, Washington, USA (2013).
Yves-Alexandre de Montjoye
Imperial College London
Université catholique de Louvain
Alex ‘Sandy’ Pentland
MIT Media Lab