• Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)

GI_Forum 2020, Volume 8, Issue 1

Journal for Geographic Information Science

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum

GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

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GI_Forum 2020, Volume 8, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8740-0
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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Extracting Patterns from Large Movement Datasets

    Anita Graser, Peter Widhalm, Melitta Dragaschnig

GI_Forum 2020, Volume 8, Issue 1, pp. 153-163, 2020/06/25

Journal for Geographic Information Science

doi: 10.1553/giscience2020_01_s153

doi: 10.1553/giscience2020_01_s153


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doi:10.1553/giscience2020_01_s153



doi:10.1553/giscience2020_01_s153

Abstract

Extracting useful information from large spatiotemporal datasets is a challenging task that requires suitable visual data representations. Big movement data are particularly hard to visualize since they are prone to visual clutter caused by overlapping and crisscrossing trajectories. Different data aggregation approaches have been developed to address this challenge and to provide analysts with better visualizations for data exploration and data-driven hypothesis generation. However, most approaches for extracting patterns, such as mobility graphs or generalized flow maps, cannot handle large input datasets. This paper presents a flow extraction algorithm that can be used in distributed computing environments and thus make it possible to explore movement patterns in large datasets. We demonstrate its usefulness in a use case exploring maritime vessel movements

Keywords: trajectories, spatiotemporal analysis, movement data analysis