First International Summer School on Data Science for Mobility 2022, Santorini, Greece
October 5th, 2022
This tutorial is supported by EU H2020 projects MASTER (Grant Agreement 777695) and SoBigData++ RI (Grant Agreement 871042)
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific production on various applications of human mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. Despite the increasing importance of human mobility analysis for many scientific and industrial domains, a view on state of the art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. This hands-on mini-course presents an overview of both analytical principles and modeling principles of human mobility through scikit-mobility, a Python library to analyze mobility data and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends pandas, a popular Python library for data analysis. In the mini-course, we will show how to use the library for many practical tasks, from visualizing trajectories to generating synthetic data, from analyzing the statistical patterns of trajectories to assessing the privacy risk related to the analysis of mobility data sets.
- Python programming, and knowledge of Python library Pandas
- installation of library scikit-mobility
- basic knowledge of machine learning (classification, regression, clustering)
fundamental_concepts.ipynb
- introduction to python libraries for human mobility analysis (shapely, geopandas, folium, scikit-mobility)
mobility_data.ipynb
- loadining and managing mobility data in python
measures.ipynb
- measuring human mobility patterns
flow_prediction.ipynb
- solving the problem of flow generation with python
Luca Pappalardo, ISTI-CNR, Pisa, Italy