scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
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 fr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2022-07-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3942 |
| _version_ | 1797813964386598912 |
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| author | Luca Pappalardo Filippo Simini Gianni Barlacchi Roberto Pellungrini |
| author_facet | Luca Pappalardo Filippo Simini Gianni Barlacchi Roberto Pellungrini |
| author_sort | Luca Pappalardo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T08:00:30Z |
| format | Article |
| id | doaj.art-cde8ae0756cb4107b29e5efd1224bfd5 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-03-13T08:00:30Z |
| publishDate | 2022-07-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-cde8ae0756cb4107b29e5efd1224bfd52023-06-01T18:48:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602022-07-0110313810.18637/jss.v103.i043750scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility DataLuca Pappalardo0https://orcid.org/0000-0002-1547-6007Filippo Simini1https://orcid.org/0000-0001-8675-3529Gianni Barlacchi2https://orcid.org/0000-0002-9896-0610Roberto Pellungrini3https://orcid.org/0000-0003-3268-9271ISTI-CNRArgonne National LaboratoryAmazon, Alexa AIUniversity of PisaThe 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.https://www.jstatsoft.org/index.php/jss/article/view/3942data sciencehuman mobilitybig datanetwork sciencedata miningpythonmathematical modellingmigration modelsprivacysoftwareopen source |
| spellingShingle | Luca Pappalardo Filippo Simini Gianni Barlacchi Roberto Pellungrini scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data Journal of Statistical Software data science human mobility big data network science data mining python mathematical modelling migration models privacy software open source |
| title | scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data |
| title_full | scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data |
| title_fullStr | scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data |
| title_full_unstemmed | scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data |
| title_short | scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data |
| title_sort | scikit mobility a python library for the analysis generation and risk assessment of mobility data |
| topic | data science human mobility big data network science data mining python mathematical modelling migration models privacy software open source |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3942 |
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