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i247: Information Visualization and Presentation Marti Hearst

i247: Information Visualization and Presentation Marti Hearst. Data Types and Graph Types. Outline. The Roles and Stages of Visualization (briefly) Data Models and Types of Data Which Kinds of Graphs for Which Types of Data? Class Exercise. The Roles and Stages of Visualization.

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i247: Information Visualization and Presentation Marti Hearst

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  1. i247: Information Visualization and PresentationMarti Hearst Data Types and Graph Types

  2. Outline • The Roles and Stages of Visualization (briefly) • Data Models and Types of Data • Which Kinds of Graphs for Which Types of Data? • Class Exercise

  3. The Roles and Stages of Visualization

  4. What Visualization Can Do (Ware) • Allows comprehension of huge amounts of data. • Allows perception of emergent properties • Enables problems with the data to stand out • Facilitates understanding at both large and small scales; patterns linking local features • Facilitates hypothesis formation.

  5. What Visualization Can Do(Tufte ’83) • Show the data • Induce to viewer to think about the data • Avoid distorting what the data have to say • Present many numbers in a small space • Make large data sets coherent • Encourage the eye to compare different pieces of data • Reveal the data at several levels of detail, from overview to fine structure • Serve a clear purpose: • Description, exploration, tabulation, or decoration • Be closely integrated with the statistical and verbal descriptions of a data set.

  6. Stages of Visualization (Ware) • Collection and storage of data • Preprocessing to transform data into something understandable • Hardware and graphics algorithms for producing an image on the screen • Human perceptual and cognitive system. • (I think he’s missing a stage … Design of the visualization.)

  7. Put it Into Questions • What are our goals? • What questions do we want to answer? • What kind of data might we collect? • How might we convey the information associated with this data?

  8. Design Principles • Visual display • Interaction • Human Abilities • Visual perception • Cognition • Motor skills Imply Inform design Constrain design • Design Process • Iterative design • Design studies • Evaluation • Techniques • Graphs & plots • Maps • Trees & Networks • Volumes & Vectors • … • Frameworks • Data types • Tasks Visualization Components From Melanie Tory

  9. Data Models and Types of Data

  10. Basic Elements of a Data Model • A data model represents some aspect of the world • Data models consist of these basic elements: • objects • values (also called attributes) • relations Adapted from Stone & Zellweger

  11. Basic Elements: Objects • Objects are items of interest • people, plants, cars, films, etc… • Objects allow you to define and reason about a domain • ecosystem: ponds, streams, woodlands, mountains, plants, animals, etc. Adapted from Stone & Zellweger

  12. Basic Elements: Values • Values (or attributes) are properties of objects • Two major types • quantitative • categorical • Appropriate visualizations often depend upon the type of the data values Adapted from Stone & Zellweger

  13. Basic Elements: Relations • Relations relate two or more objects • leaves are part of a plant • a department consists of employees • Ecosystem • connections between streams and lakes • predator/prey network of what eats what • … Adapted from Stone & Zellweger

  14. Types of Data (Ware) • Entities • Relationships • Attributes of Entities or Relationships • Nominal / Ordinal / Interval / Ratio (Stevens ’46) • Categorical / Integer / Real • Operations Considered as Data • Mathematical • Merging lists • Transforming data, etc. • Metadata (derived data)

  15. Types of Data (Few) • Quantitative (allows arithmetic operations) • Categorical (group, identify & organize; no arithmetic) Nominal Ordinal Interval Hierarchical Adapted from Stone & Zellweger

  16. Types of Data • Quantitative (allows arithmetic operations) • 123, 29.56, … • Categorical (group, identify & organize; no arithmetic) Nominal (name only, no ordering) • Direction: North, East, South, West Ordinal (ordered, not measurable) • First, second, third … • Hot, warm, cold Interval (starts out as quantitative, but is made categorical by subdividing into ordered ranges) • Time: Jan, Feb, Mar • 0-999, 1000-4999, 5000-9999, 10000-19999, … Hierarchical (successive inclusion) • Region: Continent > Country > State > City • Animal > Mammal > Horse Adapted from Stone & Zellweger

  17. Which Types of Graphs for Which Kinds of Data?

  18. Quantitative Against Categorical From Few, "Quantitative vs. Categorical Data: A Difference Worth Knowing", DM Review Magazine, April 2005

  19. Quantitative against Quantitative From Few, "Quantitative vs. Categorical Data: A Difference Worth Knowing", DM Review Magazine, April 2005

  20. Questions to ask when creating a graph • Is a graph needed? • Yes, if illustrating relationships among measurements • What information is being conveyed? • What is most important? • Start by writing a title

  21. Questions to ask when creating a graph • What data is needed to answer specific questions? • Overview? Relationships? • Grice’s maxims • combine relevant information together • don’t show extraneous information • Who is your audience?

  22. What Format to Use? • Bertin has a notion of efficiency • Tufte says “show the data” • Let’s start with familiar graph types • line graphs • bar charts • scatter plots • layer graphs • When to use each?

  23. Anatomy of a Graph (Kosslyn 89) • Framework • sets the stage • kinds of measurements, scale, ... • Content • marks • point symbols, lines, areas, bars, … • Labels • title, axes, tic marks, ...

  24. When to use which type? • Line graph • x-axis requires quantitative variable • differences among contiguous values • familiar/conventional ordering among ordinals • Bar graph • comparison of relative point values • Scatter plot • convey overall impression of relationship between two variables

  25. What to put on the x axis? • Independent vs. Dependent variables • we often measure one quantitative variable against another • the value of one changes in relation to the other • the dependent variable changes relative to the independent one • the independent variable acts as a “measuring stick” • Independent usually goes on the x (horizontal) axis

  26. Independent vs. Dependent • Independent vs. Dependent variables • heat in degrees against time • sales against season • tax revenue against city • What happens when there is more than one independent variable? • Choose one for the x axis, and another as a variation in the mark (color, shape)

  27. 623! Few on How to Show Information • The best way to show a single value? • Use a textual representation. • Why? • How to draw attention to a number?

  28. Few on How to Show Information • What are tables good for? • Data lookup • Hierarchical relationships

  29. Class Exercise

  30. How to Combine Data Types? • Class Exercise: • Using data about autos from the 70’s • Each person get a column of data • First, identify the data type • Then, stand up • Then, repeat the following several times: • Walk up to someone else. If they have a different column than you do, discuss whether and how you should plot your two columns. • If yes, what question are you answering? • If no, why not? • Then, repeat this, but with groups of three people.

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