Analyze Mobile Phone Metadata with bandicoot
bandicoot (http://bandicoot.mit.edu ) is Python toolbox to analyze mobile phone metadata. It 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. It includes an interactive visualization, support for mobile phone recharges, support for Python 3, and clustering algorithms to handle both antenna and GPS locations.
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. There are 1400+ behavioral indicators that are falling 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). bandicoot also 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.
Try bandicoot on your phone ?
You can use Android application to 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.
Research papers
Dependencies
bandicoot has no dependencies, which allows users to easily compute indicators on a production machine. To run tests and compile the visualization, optional dependencies are needed:
The source code is currently hosted on Github at https://github.com/yvesalexandre/bandicoot. Binary installers for the latest released version are available at the Python package index:
http://pypi.python.org/pypi/bandicoot/
And via
easy_install:easy_install bandicoot
or
pip:pip install bandicoot
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