GeoTime Information Visualization

By T. Kapler, W. Wright. IEEE Information Visualization Conference, 2004

380 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/7072ecbea329b17a4a1eb04e93094c1871152c0c

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

1. Gennady L. Andrienko
2. Natalia V. Andrienko
3. Stefan Wrobel
4. Chien-Hsin Hsueh
5. Zhicheng Liu
6. Yuzuru Tanahashi
7. Kwan-Liu Ma
8. H. Schumann

In Geography, Planning and Development

1. Gennady L. Andrienko
2. Natalia V. Andrienko
3. Stefan Wrobel
4. H. Schumann
5. Christian Tominski
6. Zhicheng Liu
7. John T. Stasko
8. Carsten Görg

In Signal Processing

1. Gennady L. Andrienko
2. Natalia V. Andrienko
3. Stefan Wrobel
4. H. Schumann
5. Zhicheng Liu
6. John T. Stasko
7. Carsten Görg
8. Christian Tominski

Citations in the corpus

• An Efficient Framework for Generating Storyline Visualizations from Streaming Data (2015, background): “The visualization mainly consists of a series of converging and diverging lines that represents the timeline of the relational dynamics between different entities.”
• Visual analytics of movement: An overview of methods, tools and procedures (2013, background): “The most common types of display for the visualization of movements of discrete entities are static and animated maps [53][11] and interactive space-time cubes [37][34][32] with linear symbols representing trajectories.”
• Visual Traffic Jam Analysis Based on Trajectory Data (2013, background): “Space time cube [20] [24] [7] uses z-axis to represent the time, but suffers from visual clutter.”
• Jigsaw: Supporting Investigative Analysis through Interactive Visualization (2007, methodology): “The GeoTime system37 provides a good example here.”
• Stacking-Based Visualization of Trajectory Attribute Data (2012, background): “At present, trajectories are often represented in a space-time cube, which combines time and space in a single display [20, 17].”
• Visual analytics tools for analysis of movement data (2007, background): “Currently, animated maps [1][2] and interactive cubes [10][11] are widely used to visualise movement data.”
• Spatial Generalization and Aggregation of Massive Movement Data (2011, neutral): “However, this and similar representations deal with coherent movement of multiple agents, i.e., the case when the agents are moving together as a single unit.”