Abstract
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (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 a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, 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.
Original language | English |
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Article number | 10 |
Number of pages | 35 |
Journal | ACM Transactions on Spatial Algorithms and Systems |
Volume | 10 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jul 2024 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Funding
Funders | Funder number |
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Helmholtz School for Marine Data Science | |
Bundesministerium für Bildung und Forschung | |
Bundesministerium für Wirtschaft und Klimaschutz | |
Hong Kong Jockey Club Charities Trust | 260920140 |
Bundesministerium für Wirtschaft und Energie | 68GX21002E |
Elon University | 101092749 |
Helmholtz Association | HIDSS-0005 |
Deutsche Forschungsgemeinschaft | 290391021, 491008639 |
Fonds De La Recherche Scientifique - FNRS | 40018132 |
Australian Research Council | CE200100005 |
University of Hong Kong | 2022-23, 109000579, SES-2017614 |
EU's Horizon Europe research and innovation program | CNS-2041952, 101093051, 101070279, CNS-2125530, IIS-1910216, DEB-2109647 |
National Science Foundation | IIS-2203553, IIS-1907855 |
EU's Horizon 2020 research and innovation program | 955895, U23A20296 |
Lamarr Institute for Machine Learning and Artificial Intelligence | Lamarr22B |
National Institutes of Health | R01HL159805 |
Keywords
- Environmental impacts
- Geospatial intelligence
- GPS data
- Mobility Patterns
- Spatiotemporal data
- Urban Mobility