Introduction

1. Introduction Dr. Yan Liu Department of Biomedical, Industrial and Human Factors Engineering Wright State University

2. What is Visualization? • Definition • The use of computer-supported, visual representation of data to amplify cognition (Card et al., 1999) • Goal of Visualization is to Provide Insight! • Discovery • Decision making • Explanation Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” - Edward R. Tufte, The Visual Display of Quantitative Information,1983

3. Passamaquoddy Bay Visualization • Lines of pockmarks • Linear ripples, which are errors in the data because the roll of the ship that took the measurement was not properly taken into account

4. Advantages of Visualization • Provides an Ability to Comprehend Huge Amounts of Data • e.g. The image of Passamaquoddy Bay captures more than one million measurements collected with a multibeam echo sounder • Allows the Perception of Emergent Properties that Were Not Anticipated • e.g. The pattern that the pockmarks appear in lines is evident in the image of Passamaquoddy Bay • Enables Problems with the Data to Become Immediately Apparent • e.g. The linear ripples in the image of Passamaquoddy Bay • Facilitates Understanding of Both Large-Scale and Small-Scale Features of the Data • Present both overall view and detailed characteristics of data • Facilitates Hypothesis Formation • Identification of unexpected interesting patterns of data may lead to hypotheses regarding some features of the data

5. External Cognition • Cognition • Mental process of knowing, including awareness, perception, reasoning, and judgment • External cognition • The way in which internal and external representations and processing weave together in thought • Use of external cognitive aid can enhance cognition The power of the unaided mind is highly overrated. Without external aids, memory, thought, and reasoning are all constrained. But human intelligence is highly flexible and adaptive, superb at inventing procedures and objects that overcome its own limits. The real powers come from devising external aids that enhance cognitive abilities. How have we increased memory, thought, and reasoning? By the inventions of external aids: It is things that make us smart. - Donald A. Norman, 1993, Things that Make Us Smart, P. 43

6. Why Does Visualization Work? • Visualization Amplifies Cognition in Six Ways • Increases the memory and processing resources available to the users • Reduces search for information • Enhances detection of patterns • Enables perceptual inference operations • Uses perceptual attention mechanisms for monitoring • Encodes information in a manipulable medium

7. Information vs. Scientific Visualization • Information Visualization • Develop visual metaphors for data that do not have obvious spatial mapping (abstract data) • Scientific Visualization • Visualize aspects of the “physical world” which have physical representations and inherent spatial structures • Relationships • Separate but equal or merged? • Most visualization techniques, interactive tools, and evaluation methods can be applied in both fields

8. Semiotics of Graphics • A View of Visualization as Learned Language • Visualization is about diagrams and how they can convey meaning • Diagrams are made up of symbols whose meanings are created by conventions • A matter of learning the code • The laws of perception are largely irrelevant • Strong philosophical proponents from the field of semiotics • Semiotics is the study of symbols and how they convey meaning • Principle of Arbitrariness of Graphics (Saussure, 1966) • Truth is relative to its social context • All representations (including visualization) have values only to those who understand them and agree to their meanings • No science of visualization with the goal of establishing specific guidelines for better representations

9. Three Graphic Representations A schematic diagram showing the interaction between a person and computer in a virtual world Cave painting Which one is the easiest to understand? An expression of a mathematical formula

10. Pictures as Sensory Languages • There may be a measure of similarity between pictures and the things they represent • People can interpret pictures without training • e.g. Deregowski (1968) reported that adults and children in a remote area of Zambia could easily match photographs of toy animals and the actual toys in spite that they had very little graphic art • One graph can be more effective than the other (a) (b) (a) Is more effective in showing connections among nodes than (b)

11. Sensory versus Arbitrary Symbols • Sensory Representations • Symbols and aspects of visualizations that derive their expressive power from their ability to use the perceptual processing power of the brain without learning • Resistance to instructional bias • Sensory immediacy • Cross-cultural validity • e.g. use of line connections to show relationships between objects Muller-Lyer Illusion Five Religions of Texture

12. Sensory versus Arbitrary Symbols • Arbitrary Representations • Aspects of visualizations that must be learned • Easy to forget • Embedded in culture and applications • e.g. Use of a cross to represent the First Aid is meaningful only to those who know the Red Cross • Formally powerful • e.g. The expressive power of mathematics to convey abstract concepts in a formal and rigorous way • Capable of rapid change

13. Reference Model for Visualization Figure 1.23 in Readings in Information Visualization (Card et al., 1999) Raw Data: data in idiosyncratic formats Data Tables: relations (cases by variables) + metadata Visual Structures: spatial substrates + marks + graphical properties Views: Graphical parameters (position, scaling, clipping, …)

14. Types of Data • Entities • The objects of interest • e.g. people, hurricanes, a school of fish • Relationships • Form the structures that relate entities • Structural or physical (e.g. A is part of B, C is above D) • Conceptual (e.g. the relationship between a store and its customer) • Causal (e.g. A causes B) • Temporal (e.g. A happens before B) • …

15. Types of Data (Cont.) • Attributes (Variables) of Entities or Relationships • Properties of entities or relationships • e.g. color of an apple, temperature of a room • Attribute types • Nominal (unordered labels) • e.g. color, names • Ordinal (things ordered in sequence) • e.g. rankings of preference • Interval (gaps between data values; no true zero) • e.g. time between flight departure and arrival, temperature in the unit of Celsius or Fahrenheit • Ratio (real numbers with true zeros) • e.g. size, money • Dimensions • The number of attributes • 1-D, 2-D, multi-dimensional

16. Types of Data • Operations • Mathematical operations on numbers • e.g. addition, multiplication • Merging two lists to create a large list • Creating a new entity or relationship • e.g. average of a set of numbers • Deleting an entity or relationship • Splitting an entity into its component parts • … • Metadata • Information about data • Structure of a data table (what are the attributes and their types, how many cases or data records)

17. Data Table • Data shown in a relation or a set of relations • More structured and easier to map to visual forms than raw data … Attribute3 Attribute2 Attribute1 Case1 A Typical Structure of a Data Table Case2 … The Data Table of a Film Dataset

18. Data Transformations • Four Types of Data Transformations • Values Derived values (e.g. average) • Structures  Derived structures • Values  Derived structures (e.g. sort, categorize) • Structures  Derived values A Data Table Describing Individuals and Their Ages, Income and Professions

19. Data Transformations Structure  Derived structure (categorize age and income into intervals) Values  Derived values (convert values of age and income to binary values)

20. Data Transformations Structure  Derived values (aggregate individuals by professions and each profession becomes a case)

21. Visual Structures • Key Step in Visualization • Data tables are mapped to visual structures • Expressive Mapping (Example) • If all and only the data in the table are presented in the structure • More Effective Mapping (Example) • If it is faster to interpret, can convey more distinctions, or leads to fewer errors

22. Data Table Bad mapping because it Implies incorrect ordinal relationship among countries Is this mapping more expressive? Back

23. (a) (b) (b) is more effective than (a) for communicating a sine wave

24. One of the Diagrams Showing the History of O-ring Damage that Was Used to Make the Decision to Launch Challenger in 1987 How effective is this diagram in demonstrating the relationship between the degree of damage and temperature?

25. Scatterplot of O-ring Damage Index as a Function of Temperature (Tufte, 1997) “There are right ways and wrong ways to show data; there are displays that reveal the truth and displays that do not” - Edward R. Tufte, Visual Explanation, 1997 Back

26. Visual Structures • Components of Visual Structures • Spatial substrate • Perceptually dominant • Space is defined in terms of axes and their properties • U: Unstructured axis (no axis) • N: Nominal axis (a region is divided into sub-regions) • O: Ordinal axis (the ordering of sub-regions is meaningful) • Q: Quantitative axis (a region has a metric with ratio or interval properties)

27. Axes: Year  QX Popularity  QY Query Widgets: Film Type N (radio button) Ratings O (radio button) Length Q (slide bar) Director N (slide bar) Actress N (slide bar) Actor N (slide bar) Title N (slide bar) Popularity Year The FilmFinder (Ahlberg & Shneiderman, 1994)

28. Visual Structures • Components of Visual Structures • Spatial substrate (Cont.) • Organization of axes • Increase amount of information encoded in spatial substrate • Composition: orthogonal placement of axes, creating up to 3-D metric space • Alignment: repetition of an axis at different positions in the space (Example) • Folding: continuation of an axis in an orthogonal dimension (Example) • Recursion: repeated subdivision of space (Example) • Overloading: reuse of the same space for the same data table (Example)

29. Align Bar Charts for Car Mileage and Car Price (Mackinlay, 1986) Folding of Software Modules in the Visualization of a Large Computer Program (Eick et al., 1992) Back

30. Each directory is represented as a squared frame • Each file is represented as a solid square colored by its type • Each directory has all of its subdirectories and files organized alphabetically inside it Pad++ Directory Brower Snapshot (Bederson & Hollan, 1994) Back

31. A nested heterogeneous coordinate system • A 3D world is embedded in another 3D world; the position of the embedded world’s origin relative to the containing world’s coordinate system specifies the values of the containing world’s three variables that are held constant Worlds within Worlds (Feiner & Beshers, 1990)

32. Visual Structures • Components of Visual Structures (Cont.) • Marks • Points (0-D) • Lines (1-D) • Areas (2-D) • Volumes (3-D) • Connections and enclosure • Represent link or hierarchical relationships among objects • e.g. node-link trees, treemaps • Retinal properties • The retina of the eye is sensitive to them • e.g. position, size, gray scale, orientation, color, texture, shape

33. A space constraint layout of a tree structure • Nested rectangles to represent parent – child relationships in a tree • The area of a rectangle is proportional to the number of cases in its corresponding node • The hue of a rectangle denotes the type of business of its corresponding company • The lightness of the color of a rectangle denotes the one-day change value of its stock (the darker, the larger the change) • More information about Treempa (http://www.cs.umd.edu/hcil/treemap/) Treemap of the New York Stock Exchange on Dec. 5, 1997

34. Rainfall Month Temporal Encoding • Temporal Data Tables • Qt some visual representation • Animation • Some attribute  Time • Used to keep track of changes of view or visualization • Used to enhance a visual effect • e.g. rotating a complicated object in a 2-D display can induce 3-D effects A Chart Shows How Rainfall Varies across Different Months in a Year

35. View Transformation • Functions • Interactively modify and augment visual structures to turn static presentations into visualizations • Exploit time to extract more information from the visualization that would be possibly statically • Types of View Transformations • Location probes • Use location in a visual structure to reveal addition information • Pointing (Example) • Brushing: the curser passing over one location creates visual effects at others’ marks • Can augment visual structure • Magic lens: objects in the selected region reveal additional properties of the data table (Example)

36. Pointing FilmFinder Showing the Details of a Probed Film Back

37. A Magic Lens that Provides More Details about a Roadmap

38. View Transformation • Types of View Transformations (Cont.) • Viewpoint controls • Use affine transformations to zoom, pan, and clip the viewpoint • An affine transformation preserves collinearity (i.e. all points lying on a line initially still lie on a line after transformation) and ratios of distances • e.g. zooming, overview + detail (two windows are used together: an overview window of the visual structure and a detail window that provides a magnified focus) • Distortion • Modifies a visual structure to create focus + context views in a single structure • e.g. hyperbolic tree, perspective wall, bifocal lens, etc.

39. Radial layout • The root is placed at the center, while the children are placed at an outer ring to their parents • Distortion technique • A nonlinear technique is used to map the plane into a circle, shrinking the nodes of the tree that are far from the focus point • Non-overlapping • To ensure non-overlapping of nodes, an open angle is assigned to each node and all children of a node are laid out in its open angle • Refocusing • Transformations are provided to allow fluent node repositioning; Users can move and reposition a node Hyperbolic Tree of Ebay

40. Interaction and Transformation Control • Human Interaction • Raw data -> data table • Filtering, changing the structure of the data table, etc. • Data table -> visual structure • Varying the mappings of spatial substrates, marks, connections and enclosure, and/or retinal properties • Visual structure -> view • Probing, manipulating viewpoint, creating distortions of views, etc.