Visual Variables for Information Visualization CSC4730/6730 Scientific Visualization Visual Variables for Information Visualization Lecture 11 Ying Zhu Georgia State University

Background The discussion of visual variables is larged based on Jacques Bertin’s work Jacques Bertin, “Semiology of graphics: diagrams, networks, maps.” University of Wisconsin Press, 1983 (first published in French in 1967. Translated in 1983)

Background Jacques Bertin’s semiology of graphics “ Jacques Bertin is one of the fundamental gurus of Information Visualization since he was the first in articulating a coherent and reasoned theory for the analysis of quantitative graphic representation.” - infovis.com

Background The information that is being visualized may not have any obvious visual manifestation The process of creating mapping from the information to the visual representation is non-trivial What are the best visual variables for a particular set of information?

Visualization pipeline

Basic visual units (marks) Points Location, size, shape, color Lines Length, location, Change in thickness, texture, or color does not change the meaning of the line Changing location will change its meaning

Basic visual units Areas Length, width Changing length and width will change the meaning Changing position, color, value, or texture does not change its meaning

Basic visual units Surfaces Similar to area but exist in 3D Changing color, texture does not change its meaning Changes in position, size, shape, or orientation will change its meaning

Basic visual units Volumes Length, width, and height Their size is their meaning Changing position, color, or texture doesn’t change its meaning Changing size, shape, or orientation will change its meaning

Visual units Each visual unit may have multiple visual variables

Visual variables

Which visual variables do Tableau use? Position Size Shape Color Value Text

Visual variables Each visual variable may have multiple characters

Characters of visual variables Selective Is change in this visual variable alone enough to allow us to select it from a group? How easy is it to spot an outlier? Associative Is a change in this visual variable enough to allow us to perceive them as a group? How easy is it to see a cluster? Quantitative Is there a numerical reading obtainable from changes in this visual variable?

Characters of visual variables Order Are changes in this visual variable perceived as ordered? How easy is it to spot a trend? How easy is it to rank things numerically? Length How many changes in value can still be recognized with confidence as separate? How big a range of data can this visual variable encode?

Characters of visual variables Interpretation of symbolic meanings Bertin didn’t discuss this How easy is it to interpret the symbolic (not numeric) meaning of a visual variable? Greatly influence the experience of reading a visualization

Characteristics of each visual variable We will discuss the five characteristics of each visual variable Selective Associative Quantitative Order Length

Position

Example

Example

Example

Size

Size Numerical readings interpreted from changes in size alone are usually approximate and often less accurate Using size to represent numerical variable should be done with caution

Example: Human Poverty Index

Example: pie chart

Example

Example: size or position?

Example

Example: line thickness Monsieur Minard’s visualization of Napoleon’s 1812-1813 invasion of Russia

Shape

Shape While changes in shapes are distinguishable, this distinction can often require considerable interpretation effort Quick visual interpretation of all shapes is often difficult Shape is not a quantitative visual variable Shape is not an ordered visual variable

Shape

Shape The representation power of shape comes from its infinite length and from symbolic interpretation The link between the shape and the intended meaning must be explicit to reduce the mental workload of symbolic interpretation But it’s often difficult to memorize the meanings when many shapes are used

Example: Chernoff Face http://mathworld.wolfram.com/ChernoffFace.html http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF

Words and text We can see a word as a special case of shape Selective (?) Associative (?) Quantitative (No) Order (No) Texts often require serial processing On the other hand, visual marks can often be processed in parallel

Numbers Again, we can see a number as a special case of shape Selective (?) Associative (?) Quantitative (yes!) Order (it depends) This is why spreadsheets are generally not good for spotting outliers, clusters, or trends

Value

Value Changing a mark’s value is achieved by changes in darkness of lightness of the mark. Color is divided into hue, saturation, and value. The color in the later slides actually refer to hue Changes in saturation are not discussed

Value

Value Changes in value do not provide numerical readings One grey may be seen as darker or lighter than other grey, it will not be seen as 4 times as dark as the other grey. Value (grey scale) is not quantitative.

Example

Example

Color

Color Color is not quantitative since the relationship between two marks differing on color will not be read numerically Color is not ordered since changes in color do not easily lend themselves to readings of greater or lesser The link between the color and the intended meaning are often not explicit making it difficult to interpret, especially when many colors are used

Color

Example

Example

Orientation

Orientation Numerical values, quantities or ratios are not associated with changes in orientation. There seems to be some notion of order if the changes in orientation are progressive If they are organized randomly then this sense of order does not exist

Orientation While variations in orientation is theoretically infinite, practically it may be wise to limit its use to four variables: vertical, horizontal and two opposing diagonals

Example: flow visualization http://web.cs.wpi.edu/~matt/courses/cs563/talks/flowvis/flowvis.html

Grain, pattern, and texture

Grain

Pattern The characters of pattern is basically the same as shape

Texture

Example: flags

Example: logos

Example: Game companies in Atlanta http://games.spsu.edu/images/Georgia_Gaming.png

Flags and logos Well known flags and logos usually requires very low mental workload for interpreting its symbolic meaning Famous flags and logos are selective and associative (e.g., when you go grocery shopping) Obscure logos are generally not selective

Motion Selective: probably Associative: yes Quantitative: no Order: probably Length: considerable variations

Other visual variables Bertin’s book did not include depth, occlusion, and transparency, which should be addressed.

What visual marks and visual variables are used?

Rankings of visual variables Based on the works of Drs. Jock Mackinlay, William Cleveland, et al. Rank visual variables for different data types Quantitative (numerical) data Ordinal data Nominal data

Rankings of visual variables Ranking by accuracy for quantitative data Position Length Angle Slop Area Volume Density Color saturation

Rankings of visual variables Ranking by accuracy for ordinal data Position Density Color Saturation Color Hue Texture Connection Containment Length Angle Slope Area Volume

Rankings of visual variables Ranking by accuracy for nominal data Position Color Hue Texture Connection Containment Density Color Saturation Shape Length Angle Slope Area Volume

Summary Selective Associative Quantitative All the visual variables are selective Associative Everyone is associative except for shape Quantitative Position: yes Size: maybe

Summary Order Length Position, size, and value All have theoretically infinite length but limited by the resolution of computer display

Interpretation of symbolic meanings All visual variables require some mental effort to interpret their symbolic meanings This is also part of visual mapping Developers often pay less attention to this aspect of visual mapping However, the complexity of reading a visualization is largely influenced by the symbolic interpretation

Readings M.S.T. Carpendale, “Considering Visual Variables as a Basis for Information Visualization”, Technical Report, Dept. of Computer Science, University of Calgary 2001 http://innovis.cpsc.ucalgary.ca/innovis/uploads/Publications/Publications/Carpendale-VisualVariablesInformationVisualization.2003.pdf

Reference Jacques Bertin, “Semiology of graphics: diagrams, networks, maps.” University of Wisconsin Press, 1983 (first published in French in 1967. Translated in 1983)

Readings Interview with Jacques Bertin http://www.infovis.net/printMag.php?num=116&lang=2