Stacking-Based Visualization of Trajectory Attribute Data
By Christian Tominski, H. Schumann, Gennady L. Andrienko, Natalia V. Andrienko. IEEE Transactions on Visualization and Computer Graphics, 2012
334 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Signal Processing
View Paper: https://www.semanticscholar.org/paper/383440c0c0c4012f36f696285a5e69772e7df413
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Shixia Liu
- Yingcai Wu
- Mengchen Liu
- Ronghua Liang
- Guodao Sun
- Weiwei Cui
- Gennady L. Andrienko
- Natalia V. Andrienko
In Geography, Planning and Development
- Gennady L. Andrienko
- Yingcai Wu
- Shixia Liu
- Natalia V. Andrienko
- Ronghua Liang
- Guodao Sun
- Daniel A. Keim
- Jean-Daniel Fekete
In Signal Processing
- Shixia Liu
- Yingcai Wu
- Ronghua Liang
- Guodao Sun
- Natalia V. Andrienko
- Gennady L. Andrienko
- Mengchen Liu
- Weiwei Cui
Citations in the corpus
- Visual analytics of movement: An overview of methods, tools and procedures (2013, methodology): “To avoid overlapping, the idea of stacking is often used [6] [51][63]: trajectories are represented by bars or bands stacked within a temporal or spatial”
- A survey on information visualization: recent advances and challenges (2014, methodology): “[136] used two of the dimensions to represent the geographic map and the third to stack trajectories and detailed attribute”
- A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, methodology): “Stacking-based visualization of trajectories in 3D space[52].”