Computer Visualization: Introduction

1. Computer Visualization:Introduction Spring, 2014 University of Texas – Pan American CSCI 6361, Spring 2014

2. About the Course … • Welcome and introductions • Handout - Syllabus and Schedule

3. Visualization … • I see what you mean … • so, visualization can be considered not just a visual process, but a cognitive (thought) process as well • And a very large part of human brain taken up with visual system • and that part of the brain is still useful beyond “simply” getting an image of the world • … which is in fact pretty complicated

4. Visual Pathways of Humans • And a very large part of human brain taken up with visual system • and that part of the brain is still useful beyond “simply” getting an image of the world • … which is in fact pretty complicated

5. Overview • Visualization – what it is, why use • Visualization and insight …and what insight is • Induction and deduction …. Logic of Discovery and Logic of Justification • Scientific visualization and information visualization • The physical and abstract • … and data visualization, too • Data analysis and data types • The challenges of N-dimensional data visualization • Imaging, Computer Graphics and Visualization • Distinctions among • Data exploration and data mining • Putting the “human in the loop”

6. Visualization is … • Visualize: • “To form a mental image or vision of …” • “To imagine or remember as if actually seeing …” • Firmly embedded in language, if you see what I mean • (Computer-based) Visualization: • The use of computer-supported, interactive, visual representations of ______ data to amplify cognition • Cognition is the acquisition or use of knowledge • Scientific Visualization: physical • Information Visualization: abstract

7. Visualization is not New • Cave guys, prehistory, hunting • Directions and maps • Science and graphs • e.g, Boyle: p = vt • … but, computer based visualization is new • … and the systematic delineation of the design space of (especially information) visualization systems is growing nonlinearly

8. Visualization and Insight • “Computing is about insight, not numbers” • Richard Hamming, 1969 • And a lot of people knew that already • Likewise, purpose of visualization is insight, not pictures • “An information visualization is a visual user interface to information with the goal of providing insight.”, (Spence, in North) • Goals of insight • Discovery • Explanation • Decision making

9. Visualization and Insight • Computing is about insight, not numbers • Hamming, 1969 • … and a lot of people got it right away • Likewise, purpose of visualization is insight, not pictures • Goals of insight: • Discovery • Decision making • Explanation

10. “Computing is about insight, not numbers”

11. “Computing is about insight, not numbers” • Numbers – states, %college, income: State % college degree income State % college degree income

12. “Computing is about insight, not numbers” • Insights: • What state has highest income?, What is relation between education and income?, Any outliers? State % college degree income State % college degree income

13. “Computing is about insight, not numbers” • Insights: • What state has highest income?, What is relation between education and income?, Any outliers?

14. “Computing is about insight, not numbers” • Insights: • What state has highest income?, What is relation between education and income?, Any outliers?

15. A Classic ExampleA wealth of information in a single graphic representation • Napolean’s Russian campaign • N soldiers, distance, temperature – from Tufte

16. Visual Knowledge Tools1,2,3 d is easy, here, visualizing 6 dimensions • Arrange information to reveal patterns, allow manipulation of information for finding patterns, Feiner et al., ~1995

17. And Insight can be Quick … • Some examples ….

18. London Subway – ActualA jumble • x

19. London Subway Diagrammatic Map providing “practical” order • x

20. And, For what it’s worth …Insight into state names in country music songs

21. Pie Chart … (humor) • http://infosthetics.com/archives/2008/09/funniest_pie_chart_ever.html

22. And … • http://www.boingboing.net/2006/11/02/hilarious-piechartvi.html

23. Useless stuff - clutter • Will see various design principles for visualization • Here, “3d” adds nothing • (at best)

24. Detrimental useless stuffWhat’s the point here? • USA Today

25. A Final Example • Presented to decision makers • To launch or not • Temp in 30’s • “Chart junk” • Finding form of visual representation is important • cf. “Many Eyes”

26. A Final Example • With right visualization, insight (pattern) is obvious • Plot o-ring damage vs. temperature

27. Why Visualize? (The domain scientist and the computer scientist) Hudson, 2003

28. Why Visualize? (The domain scientist and the computer scientist) • Why? … for insight • As noted, for discovery, decision making, and explanation • Here, will focus on the “scientist” / “computer scientist” collaboration • Domain Scientist: The biologist, geologist, … • “I’d rather be in the lab!” • Computer Scientist: • “I’d rather be developing algorithms!” • And an interesting place to be is right in the middle … • … which is what visualization is about • … so, requires knowing about “scientist” (a human) and “computing and display” system (which you know a fair amount about already) Hudson, 2003

29. Why Visualize? Domain Scientist Reply • “If Mathematics is the Queen of the Sciences, then Computer Graphics is the Royal Interpreter.” • Experiments and simulations produce reams of data • And science is about understanding, not numbers • Vision is highest-bandwidth channel between computer and scientist • Visualization (visual representations) • Puts numbers back into a relevant framework and allows understanding of large-scale features, or detailed features Hudson, 2003

30. Why Visualize? Computer Scientist Reply • Fine, CS is a synthetic discipline: • “Toolsmiths” • “Driving Problem Approach” • Forces you to do the hard parts of a problem • Acid test for whether your system is useful • Teaches you a little about other disciplines • It’s a lot of fun to be there when your collaborator uses the tool to discover or build something new Hudson, 2003

31. Bringing Multiple Specialties toBear • Interdisciplinary work often leads to synergies • Enables attacks on problems that a single discipline cannot work on alone, e.g., • Advanced interfaces • Physics, Computer Science • Physical properties of DNA: • Chemistry, Physics • Properties and shape of Adenovirus: • Gene Therapy, Physics and Computer Science • CNT/DNA computing elements: • Computer Science, Physics, Chemistry, Biochemistry Hudson, 2003

32. About (Scientific) Visualization • “Scientific visualization is not yet a discipline founded on well-understood principles. In some cases we have rules of thumb, and there are studies that probe the capabilities and limitations of specific techniques. For the most part,however, it is a collection of ad hoc techniques and lovely examples.” • Taylor, 2000 Hudson, 2003

33. About (Scientific) Visualization • “Scientific visualization is not yet a discipline founded on well-understood principles. In some cases we have rules of thumb, and there are studies that probe the capabilities and limitations of specific techniques. For the most part,however, it is a collection of ad hoc techniques and lovely examples.” • Taylor, 2000 • Or maybe that’s wrong … • Maybe in fact we (people) know a lot about visualization, e.g., 2-d and 3-d graphs, because we have been doing it since, well, the cave days • Either way the systematic delineation of the design space of display techniques for computer based visualization is early on Hudson, 2003

34. Scientific Visualization Data Examples • Visualization of data computed from physical simulations (on possibly powerful computers) - examples • Visualization of data observed from physical phenomena (e.g., clashes of accelerated particles)

35. Visualization – Main Ideas • Definition: • “The use of computer-supported, interactive visual representations of data to amplify cognition.” • Card, Mackinlay Shneiderman ’98 • This is among the most widely accepted contemporary working definitions • Visuals help us think • Provide a frame of reference, a temporary storage area • Cognition → Perception • Pattern matching • External cognition aid • Role of external world in thinking and reason • Larkin & Simon ’87 • Card, Mackinlay, Shneiderman ‘98

36. “…amplify cognition…” • “It is things that make us smart…” – Don Norma and others • Humans think by interleaving internal mental action with perceptual interaction with the world • Try 34 x 72 without paper and pencil (or calculator) • This interleaving is how human intelligence is expanded • Within a task (by external aids) • Across generations (by passing on techniques) • External graphic (visual) representations are an important class of external aids • “External cognition”

37. “… amplifying cognition…” (opt.) • Don Norman (cognitive scientist): • 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 invention of external aids: It is things that make us smart. (Norman, 1993, p. 43)

38. When to use Visualization? • Many other techniques for data analysis • Data mining, DB queries, machine learning… • Visualization most useful in exploratory data analysis: • Don’t know (exactly) what you’re looking for … • Don’t have a priori questions ... • Want to know what questions to ask … • I.e., to determine questions, or, hypotheses

39. Data Analysis and Logical Analysis • Data Analysis • Data in visualization: • From mathematical models or computations • From human or machine collection • Purpose: • All data collected are (should be) linked to a specific relationship or theory • Relationships are detected as patterns in the data • Maybe call it insight • Relationship may either be functional (good) or coincidental (bad) • Data analysis and interpretation are functionally subjective • Logical Analysis • Applying logic to observations (data) creates conclusions (Aristotle) • Conclusions lead to knowledge (at this point data become information) • There are two fundamental approaches to generate conclusions: • Induction and Deduction – both are logics • Equally “real” and necessary Mueller, 2003

40. Deduction vs. Induction • Deductive logical analysis probably the more familiar • Presented in detail since middle school • Formulate a hypothesis first, then test hypothesis • via experiment and accept/reject • data collection more “targeted” than in induction (next) • i.e., only addressing “truth” (actually falseness) of hypothesis • only limited data mining opportunities Mueller, 2003

41. Deduction vs. Induction • Inductive logical analysis part of scientific process, and reasoning generally, • but perhaps delineation of elements less familiar • Like, where do the hypotheses come from? • Insight? • Make observations first, then draw conclusions • organized data survey (structured analysis, visualization) of the raw data provide the basis for the interpretation process • interpretation process will produce knowledge that is being sought • experience of individual scientist (observer) is crucial • important: selection of relevant data, collection method, and analysis method • data mining is an important knowledge discovery strategy • ubiquitious data collection, filtering, classification, and focusing is crucial Mueller, 2003

42. Logic of Discovery • Where do the hypothesis come from? • Probably familiar with: Logic of justification • Concerned with deductive reasoning • Falsification of theories and hypothesis • “Writing up the experiment” • Also, Logic of discovery • Concerned with inductive reasoning • Just as can specify with some precision the elements of deductive reasoning, can specify element of inductive reasoning • “Getting ideas (hypotheses) to test experimentally” • Visualization does play role in each • Emphasis here is on induction, as is perhaps the less familiar

43. About the Data to be VisualizedSome details • Origin: • real world data • measured from real-world objects and processes (sensors, statistics, surveys) • model data • computed by machines (numerical simulations, scientific computations) • design data - edited by humans • Size: - number of samples and data items (kB, GB, MB, TB) • Type: • scalar or multi-variate, N-dimensional: number of attributes per data item (attribute vector) • scalar or vector (e.g., flow direction) • Range and domain: • qualitative (non-numerical) vs. quantitative (measurements) • Value: • categorical (nominal): • categories are disjunct, no intrinsic rank (e.g., {yellow, red, green}) • ordinal data: • data members of ordered sequence of categories (e.g. {tiny, small, large, huge}) Mueller, 2003

44. Dataset DimensionalityMore about the data • Number of variables involved and dimension of each variable • Univariate data: • a single variable • visualization can be a simple plot v = f(x) • Bivariate data • two variables • visualization can be a surface v = f(x, y) • Trivariate data – ex., flame simulation • three variables • visualization can be volume rendering v = f(x, y, z) • occlusions become a problem since we must visualize a 3D dataset on a 2D screen • Multivariate or N-D data (for N > 2) • visualization becomes challenging Mueller, 2003

45. Multivariate DataAgain, 1,2,3 d is easy - Practical Example • You (a person) can be considered a multi-dimensional data point when it comes to your statistical properties, examples are: • annual salary, rent, mortgage, stock revenues and losses, life insurance, credit card balance • number of children, pets, cars, computers, telephones, cell phones, kidneys • money spent on CDs, computer games, eating out, movies, comic books, DVDs • hours spent surfing the web, sick leaves, vacations, watching TV, making phone calls • location of residence (zip code), profession, nationality, family status, age, interests • Large commercial interest to identify and target certain groups of people • Another example: Categorize all web pages or text documents • The general task is: • identify the cluster of datapoints that fit a certain metric or set of criteria • The general problem is: • automated (statistical) methods usually fail for large and fuzzy problem spaces • Visualization can help: • but... how does one visualize data in N-space?

46. Terminology • Scientific Visualization • Field in computer science that encompasses user interface, data representation and processing algorithms, visual representations, and other sensory presentation such as sound or touch (McCormick, 1987) • Data Visualization • More general than scientific visualization, since it implies treatment of data sources beyond the sciences and engineering, e.g., financial, marketing, numerical data generally • Includes application of statistical methods and other standard data analysis techniques (Rosenblum, 1994) • Information Visualization • Concerned typically with more abstract, often semantic, information, e.g., hypertext documents, WWW, text documents Shroeder et al., 2002

47. Contexts of Visualization • If data spatio-temporal (up to 3 spatial coordinates and time), • typically scientific visualization methods used • If data higher dimensional (>4), or abstract, • typically information visualization techniques used • Human perceptual system is highly tuned to space-time relations • This (3-d, or 4 with time) coordinate system understood with little or no explanation • Visualization of abstract data typically requires extensive explanation to understand what, and its representation, being viewed • Still, there is overlap: • Often, first step in IV visualization process is to project abstract data into the spatial-temporal domain (3d + time), and use SciViz techniques to view • Projection process can be quite complex, involving methods of statistical graphics, data mining, etc., or may just select subset of dimensions to view Shroeder et al., 2002

48. Imaging, Computer Graphics, and Visualization • Imaging, or image processing • Study of 2D pictures, or images • Includes techniques to transform, extract information from, analyze, and enhance images • Computer graphics • Process of creating images using a computer • Includes both 2D and 3D • Visualization • Process of exploring, transforming, and viewing data as images (or other sensory forms) to gain understanding and insight into data • Distinguishing visualization from computer graphics: • Dimensionality of data is 3D or greater • Concerned with data transformation, i.e., information is repeatedly created and modified to enhance extracting meaning from data • Naturally interactive, human included directly in process of creating, transforming, and viewing data Shroeder et al., 2002

49. Measured Data - CT, MRI, ultrasound - Satellite - Laser digitizer - Stocks, financial Computational Methods - Finite element - Finite Difference - Boundary element - Numerical analysis Data Transform Map Display Visualization Encompasses Exploring and Understanding Data • Visualization process (simplified) Shroeder et al., 2002

50. Data Exploration and Mining Techniques - The User in the Loop • View refinement and navigation loop: • view and navigation control is important for extended and detailed visual spaces that contain (visually) mapped data • working memory needs focus+context to perform better Mueller, 2003