scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
Dublin Core
Title
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
Subject
: data science, human mobility, mobility analysis, spatio-temporal analysis, big data,
network science, data mining, Python, mathematical modeling, migration models, privacy
network science, data mining, Python, mathematical modeling, migration models, privacy
Description
The last decade has witnessed the emergence of massive mobility datasets, such as
tracks generated by GPS devices, call detail records, and geo-tagged posts from social
media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues
related to raw spatiotemporal trajectories, while the second line of research focuses on
discovering the statistical “laws” that govern human movements. A significant effort has
also been put on designing algorithms to generate synthetic trajectories able to reproduce,
realistically, the laws of human mobility. Last but not least, a line of research addresses
the crucial problem of privacy, proposing techniques to perform the re-identification of
individuals in a database. 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. In this paper, we propose scikit-mobility, a
Python library that has the ambition of providing an environment to reproduce existing
research, 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.
Moreover, scikit-mobility provides the user with many functionalities, from visualizing
trajectories to generating synthetic data, from analyzing statistical patterns to assessing
the privacy risk related to the analysis of mobility datasets.
tracks generated by GPS devices, call detail records, and geo-tagged posts from social
media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues
related to raw spatiotemporal trajectories, while the second line of research focuses on
discovering the statistical “laws” that govern human movements. A significant effort has
also been put on designing algorithms to generate synthetic trajectories able to reproduce,
realistically, the laws of human mobility. Last but not least, a line of research addresses
the crucial problem of privacy, proposing techniques to perform the re-identification of
individuals in a database. 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. In this paper, we propose scikit-mobility, a
Python library that has the ambition of providing an environment to reproduce existing
research, 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.
Moreover, scikit-mobility provides the user with many functionalities, from visualizing
trajectories to generating synthetic data, from analyzing statistical patterns to assessing
the privacy risk related to the analysis of mobility datasets.
Creator
Luca Pappalardo
Source
https://www.jstatsoft.org/article/view/v103i04
Publisher
ISTI-CNR
Date
July 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Luca Pappalardo, “scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8260.