[Zurück]


Zeitschriftenbeiträge:

A. Graser, Peter Widhalm, M. Dragaschnig:
"Extracting Patterns from Large Movement Datasets";
GI_Forum, Journal of Geographic Information Science, 1 (2020), S. 153 - 163.



Kurzfassung:
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 datadriven 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.

Schlagworte:
trajectories, spatiotemporal analysis, movement data analysis


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1553/giscience2020_01_s153


Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.