101 - Vi Visu sual aliz izat atio ion Vis is10 Lecture 6: - - PowerPoint PPT Presentation

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101 - Vi Visu sual aliz izat atio ion Vis is10 Lecture 6: - - PowerPoint PPT Presentation

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101 - Vi Visu sual aliz izat atio ion Vis is10 Lecture 6: The Visualization Alphabet: Marks and Channels This Week Homework 1! Lecture 5: Lecture 6: Section 2: Reading: No Device Policy Last Week Terms Dataset Types Data Types

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Vis is10 101 - Vi

Visu sual aliz izat atio ion

Lecture 6: The Visualization Alphabet: Marks and Channels

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This Week

Homework 1! Lecture 5: Lecture 6: Section 2: Reading:

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No Device Policy

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Last Week

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Terms

Dataset Types Data Types

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Tables

Flat Table Multidimensional Table

Keys Attributes Item Values

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Multidimensional Tables

z

Keys: Genes Keys: Patients

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Graphs/Networks

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Fields

Attribute values associated with cells Cell contains data from continuous domain Measured or simulated Sampling & Interpolation

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Other Collections

Sets Lists Clusters

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Data Types

Nominal (labels) Ordinal (ordered) Interval (location of zero arbitrary) Ratio (zero fixed)

On the theory of scales and measurements [S. Stevens, 46]

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Item/Element/ (Independent) Variable

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Attribute/ Dimension/ (Dependent) Variable/ Feature

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Semantics

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Keys?

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Attribute Types?

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Categorical Ordinal Quantitative

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Design Critique

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Recalled Cars NY Times

http://goo.gl/tDVISB

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The Visualization Alphabet: Marks and Channels

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Marks & Channels

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Example: Homework 2

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Marks for Items

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Marks for Links

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Containment can be nested

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Channels (aka Visual Variables)

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Jacques Bertin

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Bertin’s Visual Variables

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Using Marks and Channels

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Redundant encoding

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Good bar chart?

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Types of Channels

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Channels: Expressiveness Types and Effectiveness Ranks

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What visual variables are used?

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What visual variables are used?

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Characteristics of Channels

Selective Associative Quantitative (Magnitude vs Identity Channels)

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Characteristics of Channels

Order (Magnitude vs Identity) Length

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Position

Strongest visual variable Suitable for all data types Problems:

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Example: Scatterplot

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Position in 3D?

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Length & Size

Good for 1D, OK for 2D, Bad for 3D Easy to see whether one is bigger Aligned bars use position redundantly

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Example 2D Size: Bubbles

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Value/Luminance/Saturation

OK for quantitative data when length & size are used. Not very many shades recognizable

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Example: Diverging Value-Scale

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Color

Good for qualitative data (identity channel) Limited number of classes/length (~7-10!) Does not work for quantitative data! Lots of pitfalls! Be careful! Suggested rule:

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Color: Bad Example

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Color: Good Example

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Shape

Great to recognize many classes. No grouping, ordering.

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Chernoff Faces

Idea: use facial parameters to map quantitative data

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More Channels

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Why are quantitative channels different?

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Steven’s Power Law, 1961

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How much longer?

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How much longer?

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How much steeper?

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How much larger (area)?

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How much larger (area)?

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How much larger (diameter)?

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How much darker?

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Position, Length & Angle

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Other Factors Affecting Accuracy

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Cleveland / McGill, 1984

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Heer & Bostock, 2010

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Jock Mackinlay, 1986

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Channels: Expressiveness Types and Effectiveness Ranks

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Next Week

Lecture 7: Lecture 8: Section 3: Homework 1 due to Friday, 30 October.