Repo for the material used during lessons, conference tutorials, and talks

At the end of the workshop, the participants will acquire practical skills related to the analysis of real-world mobility data. In particular, they will be able to use scikit-mobility to filter and clean mobility data, compute standard mobility metrics, generate their own synthetic mobility data, and assess the privacy risk of each user described in the analyzed mobility data set.

• event --> https://appliedmldays.org/events/amld-epfl-2020/workshops/human-mobility-analysis-and-simulation-with-python
• material --> https://github.com/scikit-mobility/tutorials

These hands-on lessons are part of the course Mobility Data Analysis (MDA) of the Master in Big Data Analytics & Social Mining. We present an overview on the fundamental principles underlying the analysis of mobility data, using scikit-mobility. In particular, we see how to visualize trajectories, flows and tessellations, how to clean raw mobility data by using standard techniques proposed in the mobility data mining literature, and how to analyze mobility data by using the main measures characterizing human mobility patterns (e.g., radius of gyration, daily motifs, mobility entropy).