Publikation:

Efficient Mining of Volunteered Trajectory Datasets

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2024

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Forsch, Axel
Funke, Stefan
Haunert, Jan-Henrik

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http://nbn-resolving.de/urn:nbn:de:bsz:352-2-12j9jr6yqvjps0
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https://doi.org/10.1007/978-3-031-35374-1_3
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CC BY 4.0

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BURGHARDT, Dirk, ed., Elena DEMIDOVA, ed., Daniel A. KEIM, ed.. Volunteered Geographic Information : Interpretation, Visualization and Social Context. Cham: Springer Nature, 2024, pp. 43-77. ISBN 978-3-031-35373-4. Available under: doi: 10.1007/978-3-031-35374-1_3

Zusammenfassung

With the ubiquity of mobile devices that are capable of tracking positions (be it via GPS or Wi-Fi/mobile network localization), there is a continuous stream of location data being generated every second. These location measurements are typically not considered individually but rather as sequences, each of which reflects the movement of one person or vehicle, which we call trajectory. This chapter presents new algorithmic approaches to process and visualize trajectories both in the network-constrained and the unconstrained case.

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ISO 690FORSCH, Axel, Stefan FUNKE, Jan-Henrik HAUNERT, Sabine STORANDT, 2024. Efficient Mining of Volunteered Trajectory Datasets. In: BURGHARDT, Dirk, ed., Elena DEMIDOVA, ed., Daniel A. KEIM, ed.. Volunteered Geographic Information : Interpretation, Visualization and Social Context. Cham: Springer Nature, 2024, pp. 43-77. ISBN 978-3-031-35373-4. Available under: doi: 10.1007/978-3-031-35374-1_3
BibTex
@incollection{Forsch2024Effic-68939,
  year={2024},
  doi={10.1007/978-3-031-35374-1_3},
  title={Efficient Mining of Volunteered Trajectory Datasets},
  isbn={978-3-031-35373-4},
  publisher={Springer Nature},
  address={Cham},
  booktitle={Volunteered Geographic Information : Interpretation, Visualization and Social Context},
  pages={43--77},
  editor={Burghardt, Dirk and Demidova, Elena and Keim, Daniel A.},
  author={Forsch, Axel and Funke, Stefan and Haunert, Jan-Henrik and Storandt, Sabine}
}
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