An automatic generation of schematic maps to display flight routes for air traffic controllers

Hurter, Christophe; Serrurier, Mathieu; Alonso, Roland; Tabart, Gilles; Vinot, Jean-Luc

doi:10.1145/1842993.1843034

Cited by 19 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, the more groups are present in a visualization, the harder it is to develop a set of distinct colors. A smart coloring algorithm similar to those described in [8,28,25,24] could perhaps be developed to automatically choose perceptually different colors based on group proximities. Even such approaches however would be limited by the number of groups and the intricacy of the spatial layout.…”

Section: Discussionmentioning

confidence: 99%

How to Display Group Information on Node-Link Diagrams: An Evaluation

Jianu

Rusu

et al. 2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We present the results of evaluating four techniques for displaying group or cluster information overlaid on node-link diagrams: node coloring, GMap, BubbleSets, and LineSets. The contributions of the paper are three fold. First, we present quantitative results and statistical analyses of data from an online study in which approximately 800 subjects performed ten types of group and network tasks in the four evaluated visualizations. Specifically, we show that BubbleSets is the best alternative for tasks involving group membership assessment; that visually encoding group information over basic node-link diagrams incurs an accuracy penalty of about 25% in solving network tasks; and that GMap's use of prominent group labels improves memorability. We also show that GMap's visual metaphor can be slightly altered to outperform BubbleSets in group membership assessment. Second, we discuss visual characteristics that can explain the observed quantitative differences in the four visualizations and suggest design recommendations. This discussion is supported by a small scale eye-tracking study and previous results from the visualization literature. Third, we present an easily extensible user study methodology. Permanent repository link

show abstract

Section: Discussionmentioning

confidence: 99%

How to Display Group Information on Node-Link Diagrams: An Evaluation

Jianu

Rusu

et al. 2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Decreasing opacity levels can reveal some patterns (mainly the density variation) but many important details remain hidden. Schematic representations [33] and edge bundling [23,37] partly solve the problem but introduce undesired displacements. Trajectories in 3D space require explicit representation of altitudes or depths, e.g., by per-segment colors [3] or by using 3D displays [20].…”

Section: Interactive Visualization and Filtering Of Trajectoriesmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1. Different approach routes to London airports are represented in a 3D view by central trajectories of density-based clusters of relevant parts of flight trajectories. The images show the routes that were used in two days with different wind parameters.Abstract-Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

show abstract

“…In a departure from convention, Fink et al [25] have proposed a "curvilinear" drawing approach which has produced metro maps using a force-directed algorithm. Hurter et al [26] have also created a schematic automation method specifically for air traffic control routes. They use simulated annealing to solve a cost minimization model that penalizes crossings, density and distortion.…”

Section: Background and Related Workmentioning

confidence: 99%