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Human Mobility Science: Data, Measures, Generative models and Predictive Algorithms



The rapid inclusion of tracking technologies in our personal devices opened the doors to the analysis and visualization of large sets of geo-spatial mobility data, in particular GPS traces. In this tutorial we will present a concise and intuitive overview on both fundamental modeling principles of human mobility, and machine learning models applicable to specific mobility-related problems. In particular, we will review the state of the art of four main aspects in human mobility: (1) the human mobility data landscape; (2) the privacy issues with human mobility data; (3) key metrics and measures; (4) generative and phenomenological models at the level of individual, population and mixture of the two; (5) machine learning models for next location prediction. Link to the tutorial page:
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  • Noulas
• A tale of many cities: Universal Patterns in Human Urban Mobility (Noulas et al., PLoS One, 2012).
An analytical framework to nowcast well-being with mobile phone data
al., An analytical framework to nowcast well-being with mobile phone data, JDSA, 2016.