Mobility Data Science: Perspectives and Challenges

IF 1.2 Q4 REMOTE SENSING ACM Transactions on Spatial Algorithms and Systems Pub Date : 2024-05-07 DOI:10.1145/3652158

M. Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, W. Aref, G. Andrienko, N. Andrienko, Yang Cao, Sanjay Chawla, R. Cheng, P. Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, D. Gunopulos, C. S. Jensen, Joon-Seok Kim, Peer Kröger Kyoung-Sook Kim, John Krumm, Johannes Lauer, A. Magdy, Mario A. Nascimento, S. Ravada, Matthias Renz, Dimitris Sacharidis, Flora Salim, Mohamed Sarwat, M. Schoemans, Cyrus Shahabi, Bettina Speckmann, E. Tanin, Xu Teng, Y. Theodoridis, Kristian Torp, Goce Trajcevski, Mar van Kreveld, C. Wenk, Martin Werner, Raymond E. Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang

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Abstract

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art, and describe open challenges for the research community in the coming years.

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移动数据科学：视角与挑战

移动数据捕捉移动物体（如人类、动物和汽车）的位置。随着配备 GPS 的移动设备和其他廉价定位跟踪技术的出现，移动数据的收集无处不在。近年来，移动数据的使用已在交通管理、城市规划和健康科学等多个领域产生了重大影响。在本文中，我们将介绍移动数据科学领域。为了实现移动数据科学的统一方法，我们提出了一个包含以下组件的管道：移动数据收集、清理、分析、管理和隐私。针对每个组成部分，我们解释了移动数据科学与一般数据科学的不同之处，调查了当前的技术水平，并描述了未来几年研究界面临的挑战。

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来源期刊

ACM Transactions on Spatial Algorithms and Systems REMOTE SENSING-

CiteScore

4.40

自引率

5.30%

发文量

期刊介绍： ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.

期刊最新文献

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