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The last decade has witnessed the emergence of massive mobility data sets,such as tracks generated by GPS devices, call detail records, and geo-taggedposts from social media platforms. These data sets have fostered a vastscientific production on various applications of mobility analysis, rangingfrom computational epidemiology to urban planning and transportationengineering. A strand of literature addresses data cleaning issues related toraw spatiotemporal trajectories, while the second line of research focuses ondiscovering the statistical "laws" that govern human movements. A significanteffort has also been put on designing algorithms to generate synthetictrajectories able to reproduce, realistically, the laws of human mobility. Lastbut not least, a line of research addresses the crucial problem of privacy,proposing techniques to perform the re-identification of individuals in adatabase. A view on state of the art cannot avoid noticing that there is nostatistical software that can support scientists and practitioners with all theaspects mentioned above of mobility data analysis. In this paper, we proposescikit-mobility, a Python library that has the ambition of providing anenvironment to reproduce existing research, analyze mobility data, and simulatehuman mobility habits. scikit-mobility is efficient and easy to use as itextends pandas, a popular Python library for data analysis. Moreover,scikit-mobility provides the user with many functionalities, from visualizingtrajectories to generating synthetic data, from analyzing statistical patternsto assessing the privacy risk related to the analysis of mobility data sets.

Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data