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Visual Analysis of Multivariate Movement Data Using Interactive Difference Views

Ove Daae Lampe, Johannes Kehrer, Helwig Hauser

INPROCEEDINGS, Proceedings of Vision, Modeling, and Visualization (VMV 2010), 2010

Abstract

Movement data consisting of a large number of spatio-temporal agent trajectories is challenging to visualize, especially when all trajectories are attributed with multiple variates. In this paper, we demonstrate the visual exploration of such movement data through the concept of interactive difference views. By reconfiguring the difference views in a fast and flexible way, we enable temporal trend discovery. We are able to analyze large amounts of such movement data through the use of a frequency-based visualization based on kernel density estimates (KDE), where it is also possible to quantify differences in terms of the units of the visualized data. Using the proposed techniques, we show how the user can produce quantifiable movement differences and compare different categorical attributes (such as weekdays, ship-type, or the general wind direction), or a range of a quantitative attribute (such as how two hours’ traffic compares to the average). We present results from the exploration of vessel movement data from the Norwegian Coastal Administration, collected by the Automatic Identification System (AIS) coastal tracking. There are many interacting patterns in such movement data, both temporal and other more intricate, such as weather conditions, wave heights, or sunlight. In this work we study these movement patterns, answering specific questions posed by Norwegian Coastal Administration on potential shipping lane optimizations.

Published

Proceedings of Vision, Modeling, and Visualization (VMV 2010)

  • Pages: 315–322
  • Location: Siegen, Germany

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BibTeX

@inproceedings{lampe10differenceViews,
  title = {Visual Analysis of Multivariate Movement Data Using Interactive Difference Views},
  author = {Ove Daae Lampe and Johannes Kehrer and Helwig Hauser},
  year = {2010},
  booktitle = {Proceedings of Vision, Modeling, and Visualization (VMV 2010)},
  abstract = {Movement data consisting of a large number of spatio-temporal 
agent trajectories is challenging to visualize, especially when all trajectories 
are attributed with multiple variates. In this paper, we demonstrate the visual
exploration of such movement data through the concept of interactive difference 
views. By reconfiguring the difference views in a fast and flexible way, we enable 
temporal trend discovery. We are able to analyze large amounts of such movement 
data through the use of a frequency-based visualization based on kernel density 
estimates (KDE), where it is also possible to quantify differences in terms of the 
units of the visualized data. Using the proposed techniques, we show how the user can 
produce quantifiable movement differences and compare different categorical 
attributes (such as weekdays, ship-type, or the general wind direction), or a range 
of a quantitative attribute (such as how two hours’ traffic compares to the average). 
We present results from the exploration of vessel movement data from the Norwegian 
Coastal Administration, collected by the Automatic Identification System (AIS) coastal
tracking. There are many interacting patterns in such movement data, both temporal and 
other more intricate, such as weather conditions, wave heights, or sunlight. In this 
work we study these movement patterns, answering specific questions posed by Norwegian 
Coastal Administration on potential shipping lane optimizations.},
  location = {Siegen, Germany},
  pages = {315--322},



}






 Last Modified: Jean-Paul Balabanian, 2014-04-09