Data Visualization
- The purpose of visualizaion is insight, not pictures.
- A picture is worth a thousand wrods, a good visualization is worth a thousand pictures.
- Is structured in a Visualization Pipeline
Motivation #
Why represent data visually? #
Nearly 
of our brain is devoted to processing visual information.
Why have Visualizations when you can have statistics? #
Why are both humans and computers necessary? #
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Humans necessary because:
- Many analysis problems are ill-defined
- Don’t know exactly what questions to ask in advance
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Computers necessary because:
- Large datasets are infeasible to draw by hand
- Goes beyond human capacities / patience
- Supports interactivity
Goals #
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Visual exploration (explorative)
- find unkown / unexpected
- create hypotheses
-
Visual analysis (confirmative)
- confirm or reject hyptheses
- information drill-down
-
Presentation
- effective and efficient communication of results
Types of Visualizations #
Areas of Data Visualization #
- Volume Visualizaion
- Flow Visualizaion
- Information Visualizaion
- Visual Analytics
Information Visualization / Visual Analytics is dealing with data that is discrete and more abstract.
Dimensionalities #
- 3-Dimensional Visualizations
- Volume Visualizaion
- Flow Visualization
- N-Dimensional Visualizations
- Information Visualization
- Visual Analytics
Scientific visualization #
- Volume Visualization
- Flow Visualization
Obtaining data #
- Real world
- Measurements and observations
- Medical imaging
- Material Sciences
- Geographic Data
- Theoretical world
- Mathematical / technical models
- Artificial world
- Data that is designed
Limits of Data Visualization #
- Computational limits (processing time / resources)
- Display limits (resolution)
- Human limits (preception)
Lie Factor #
Change-Blindness Effect #
An observer does not notice a difference between two mostly identical images.