Human mobility: an Introduction (4 lectures by Luca Pappalardo)

Human Mobility: an Introduction

By prof. Luca Pappalardo from
Institute of Information Science and Technologies – Italian National Research Council
and co-creator/co-manager of scikit-mobility
 (4 lectures of 3h)
 

The classes with take place in the Alain Turing building (Inria Saclay, https://www.inria.fr/en/how-get-inria-saclay-centre, rooms hereafer) in the following time slots:

– June, 2nd, 2:30-5:30 pm, at Gilles Kahn room,
– June, 9th; 2:30-5:30 pm;  Sophie Germain auditorium (new room),
– June, 16th; 2:30-5:30 pm, Sophie Germain auditorium,
– June, 23rd; 2:30-6:00 pm (lab), at Gilles Kahn room.
The syllabus and Luca’s short bio are presented below.
Contact: Aline Carneiro Viana aline.viana@inria.fr, Andrea Araldo andrea.araldo@telecom-sudparis.eu, Luca Pappalardo luca.pappalardo@isti.cnr.it, Natalia Kushik natalia.kushik@telecom-sudparis.eu 

Looking forward to your participation. 

Title:  Human Mobility: an introduction
Hours 12h of lectures, including a practical part in Python
Lecturer: Luca Pappalardo
 
Brief program description:
The analysis of geographic information, such as those describing human movements, is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. This course will teach the fundamental concepts and techniques underlying the analysis of mobility data, presenting data sources (e.g., mobile phone records, GPS traces, geotagged social media posts), data preprocessing techniques, and statistical laws.
 
Syllabus:
Introductory lesson
  • Introduction to the course 
Spatial and Mobility Data
  • Trajectory and Flows
  • Spatial Tessellations
  • Mobility data
    • Mobile Phone Data
    • GPS data
    • Social media data
    • Other data (POIs, Road Networks, etc.)
  • Preprocessing mobility data
    • filtering
    • compression
    • stop detection
    • trajectory segmentation
    • trajectory similarity and clustering
Patterns and Laws
  • individual mobility laws
  • predictability of individual mobility
  • collective mobility laws
Practice
  • human mobility analysis in Python
Evaluation Two alternatives:
  • Implementation of a method within a mobility library
  • An analysis of a dataset using a mobility library
Short Bio:

Google Scholar: https://bit.ly/3p2s4pu

Short-bio: Luca Pappalardo (PI) is a full-time researcher at the Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR) in Pisa (since 2017). Luca is a member of the KDD Lab – Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa, the Italian National Research Council (CNR), and Scuola Normale Superiore of Pisa.
Luca’s research focuses on data science, AI, computational social science and their impact on society, with a particular focus on the (privacy-preserving) analysis of human mobility and the design of mechanistic and AI models for the prediction and generation of human mobility. Luca is also part of SoBigData.eu, the European H2020 Research Infrastructure “Big Data Analytics and Social Mining Ecosystem”, in which he is responsible for coordinating the research that is conducted within the infrastructure. Luca has been a visiting scientist at Barabasi Lab (Center for Complex Network Research) of Northeastern University, Boston, and at the Central European University (CEU) in Budapest, Hungary, and at the Pontifícia Universidade do Rio de Janeiro, Brazil. In 2014, Luca received a grant from Google and the Italian National Statistics Bureau (ISTAT) for the most innovative ideas in using big data sources to study complex economic phenomena.

 

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