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GI_Forum 2020, Volume 8, Issue 1Journal 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 |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2020, Volume 8, Issue 1, pp. 153-163, 2020/06/25
Journal for Geographic Information Science
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