scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
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DOI：
10.18637/jss.v103.i04
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Luca Pappalardo;F. Simini;Gianni Barlacchi;Roberto Pellungrini
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文献类型：
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作者：
Luca Pappalardo;F. Simini;Gianni Barlacchi;Roberto Pellungrini
在过去的十年中，出现了大量的移动数据集，例如GPS设备生成的轨迹，呼叫详细记录以及来自社交媒体平台的地理标记帖子。这些数据集促进了从计算流行病学到城市规划和交通工程等各种流动性分析应用的大量科学成果。一系列文献涉及与原始时空轨迹相关的数据清理问题，而第二系列研究则侧重于发现管理人类运动的统计“定律”。一个重大的努力也放在设计算法，以产生合成轨迹能够再现，现实的，法律的人的流动性。最后但并非最不重要的是，一系列研究解决了隐私的关键问题，提出了在数据库中重新识别个人的技术。对最新技术水平的看法不能不注意到，没有统计软件可以支持科学家和从业人员进行上述所有方面的流动数据分析。在本文中，我们提出了scikit-mobility，这是一个Python库，其目标是提供一个环境来重现现有的研究，分析移动数据，并模拟人类的移动习惯。scikit-mobility是高效且易于使用的，因为它扩展了pandas，一个流行的Python数据分析库。此外，scikit-mobility为用户提供了许多功能，从可视化轨迹到生成合成数据，从分析统计模式到评估与移动数据集分析相关的隐私风险。
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 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 data sets.