Human mobility analysis and simulation with Python

13:30-17:00, January 25

Workshop / Overview

The availability of geo-spatial mobility data (e.g., GPS traces, call detail and social media records) is a trend that will grow in the near future.

For this reason, understanding human mobility is of paramount importance for many present and future applications, such as traffic forecasting, urban planning, and epidemic modeling, and hence for many actors, from urban planners to decision-makers and advertising companies.

In this hands-on tutorial we will present, with a strong focus on code implementation, an overview on the fundamental principles underlying the analysis of big mobility data.
Starting from mobility data describing the whereabouts of individuals on a territory for a large-enough observation window, we will drive the audience through the extraction of mobility patterns and measures by using scikit-mobility, a specific Python library designed by the tutorial presenters.

The library allows the user to: filter and clean raw mobility data by using standard techniques proposed in the mobility data mining literature; analyze mobility data by using the main measures characterizing human mobility patterns (e.g., radius of gyration, daily motifs, mobility entropy); simulate individual and collective mobility by executing the most common human mobility models (e.g., gravity and radiation models, exploration and preferential return model); assess the privacy risk related to the analysis of a real-world mobility data set.

Since it is supposed to be a practical hands-on tutorial, for every concept presented during the training we will show a practical code example presented through the Jupyter notebook. scikit-mobility is a starting point for the development of urban simulation and what-if analysis, e.g., simulating changes in urban mobility after the construction of a new infrastructure or when traumatic events occur like epidemic diffusion, terrorist attacks or international events.

Workshop / Outcome

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.

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • No specific knowledge about human mobility is required
  • Basic understanding of Python and the libraries
    (Pandas and Numpy are recommended)
  • Own laptop

Track / Co-organizers

Luca Pappalardo

Researcher, ISTI-CNR

Filippo Simini

Senior Lecturer, University of Bristol

AMLD EPFL 2020 / Workshops

A Conceptual Introduction to Reinforcement Learning

With Bram Vandendriessche, Kevin Smeyers & Katrien Van Meulder

09:00-12:30 January 25

Applied Machine Learning with R

With Dirk Wulff

09:00-17:00 January 25

Augmenting the Web browsing experience using machine learning

With Tudor Avram, Oleksandr Paraska, Vasily Kuznetsov & Levan Tsinadze

09:00-12:30 January 25

AMLD / Global partners