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Visualization and Computer Graphics LabJacobs University

Information visualization examples

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Visualization and Computer Graphics LabJacobs University

Information visualization examples

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Visualization and Computer Graphics LabJacobs University

Information visualization examples

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Visualization and Computer Graphics LabJacobs University

Information visualization examples

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Visualization and Computer Graphics LabJacobs University

Information visualization examples

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Visualization and Computer Graphics LabJacobs University

Information visualization examples

Visualization and Computer Graphics LabJacobs University

2.3 Visual encoding in Python

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Pygal

• Dynamic charting library for Python• http://pygal.org

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Visualization and Computer Graphics LabJacobs University

Line Charts

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Visualization and Computer Graphics LabJacobs University

Line Charts

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Visualization and Computer Graphics LabJacobs University

Stacked Line Chart

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Visualization and Computer Graphics LabJacobs University

Bar Charts

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Visualization and Computer Graphics LabJacobs University

Pyramid Charts

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Visualization and Computer Graphics LabJacobs University

Pyramid Charts

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Visualization and Computer Graphics LabJacobs University

Dot Charts

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Visualization and Computer Graphics LabJacobs University

Boxplots

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Visualization and Computer Graphics LabJacobs University

Pie Charts

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Visualization and Computer Graphics LabJacobs University

Scatterplots

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Visualization and Computer Graphics LabJacobs University

Radar Charts

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Visualization and Computer Graphics LabJacobs University

World Maps

Visualization and Computer Graphics LabJacobs University

3. Visualization Principles and Best Practices

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Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

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Visualization and Computer Graphics LabJacobs University

Good and bad visualizations

Visualization and Computer Graphics LabJacobs University

4. Visual encoding

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Visual encoding

• We have seen that attributes may have different characteristics (numerical, ordered, categorical).

• Special attributes are time (temporal dimension) and geographical location (spatial dimensions) in case of spatiotemporal data.

• Entities are typically visually encoded by some kind of glyph.

• Glyphs vary with respect to position, size, shape, orientation, color, and texture.

• Visual encoding is the mapping of attributes of the entity to specifics of the glyph.

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Visual encoding

• The exact visual encoding of a data set depends on the number and characteristics of the available attributes and on the analysis tasks of the given application.

• Interaction mechanisms allow for switching between different attributes to allow for different views on the attributes and to fulfill different tasks.

• Still, there are guidelines for mapping attributes to visual cues.

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Visual cues

color

shapetexture

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Visualization and Computer Graphics LabJacobs University

Guidelines• location

• size

• color

• orientation

• texture

• shape

• geospatial

• time

• numerical (continuous)

• numerical (discrete)

• ordered

• categorical

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Perception

• One needs to consider that certain visual cues are more perceptionally relevant than others.

• Color is a pre-attentive feature that can be seen immediately.

• Location and size are also easy to perceive.• Shape is more difficult to distinguish.• Orientation and texture do not allow for detecting

subtle changes easily.