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Laboratory Assignments

Explore cognitive artifacts, visualization processes, user tasks, and data types in visualization studies. Learn about cognitive maps, navigation, information foraging, and user tasks. Understand how to create visualizations effectively.

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Laboratory Assignments

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  1. Laboratory Assignments If time: Visualization, Perception & Cognition

  2. Readings • Recall: Handout #2 • New Reading Assignment

  3. Today’s Schedule • Presentations of Visual Analysis Projects (continued) • Discussion of Static Design Assignment • Discussion of Workshop Exercises • If time: Perception & Cognition in Visualization Studies

  4. Visualizations of Home/House. Child Katrina Survivors

  5. Recall: House/Home (Katrina Victim)

  6. House/Home • House as roof

  7. House?Home • Sources: Slide show of Katrina victim’s drawings of house/home, Dewann, S. “Using Crayons to Exorcise Katrina, New York Times, Monday September 17, 2007, Arts Section, B1,5.

  8. Cognitive issues • Visualization as a tool useful for aiding comprehension and understanding?

  9. Graphical visualization to support more efficient task performance • Allowing substitution of rapid perceptual influences for difficult logical inferences • Reducing search for information required for task completion (Sometimes text is better, however)

  10. Issues • Cognitive Artifacts • Matching Representation to Task (Tic-tac-toe, flight schedules) • Representations Aid Info Access and Computation (Medical prescriptions, Roman numerals, mapslegends) • Naturalness and Experiential Cognition

  11. Cognitive Maps (1st Lab Exercise) • You have some existing internal model of the system, stops, how to get there • glance at SFU map for help • Refine your internal model, clarifying items and extending it Note differences between your map & the official one

  12. Process Models: Navigation in Visual systems -process by which a person looks at a graphic and makes some use of it • substeps • Can you describe process? • Navigation in visual systems - Creation and interpretation of an internal mental model

  13. Information foraging • Search for schema (representation) • Problem solve to trade off features • Search for a new schema that reducesproblem to a simple trade-off • Package the patterns found in someoutput product

  14. Navigation

  15. Crystallization

  16. Process

  17. Browsing • Useful when • Good underlying structure so that items close to oneanother can be inferred to be similar • Users are unfamiliar with collection contents • Users have limited understanding of how system isorganized and prefer less cognitively loaded methodof exploration • Users have difficulty verbalizing underlyinginformation need • Information is easier to recognize than describe

  18. User Tasks in Visualization Environments--Eleven basic actions • identify, locate, distinguish, categorize,cluster, distribution, rank, compare withinrelations, compare between relations,associate, correlate

  19. Data Types and Tasks

  20. Terminology • Data case – An entity in the data set • Attribute – A value measured for all datacases • Aggregation function – A function thatcreates a numeric representation for a setof data cases (eg, average, count, sum)

  21. Steps in Creating Visualization • 1. Retrieve ValueGeneral Description:Given a set of specific cases, find attributes ofthose cases.Examples:- What is the mileage per gallon of the Audi TT?- How long is the movie Gone with the Wind?

  22. 2. Filter • General Description:Given some concrete conditions on attribute values,find data cases satisfying those conditions.Examples:- What Kelloggns cereals have high fiber?- What comedies have won awards?- Which funds underperformed the SP-500?

  23. 3. Compute Derived Value • General Description:Given a set of data cases, compute an aggregatenumeric representation of those data cases.Examples:- What is the gross income of all stores combined?- How many manufacturers of cars are there?- What is the average calorie content of Post cereals?

  24. 4. Find Extremum • General Description: • Find data cases possessing an extreme value of anattribute over its range within the data set.Examples:- What is the car with the highest MPG?- What director/film has won the most awards?- What Robin Williams film has the most recentrelease date?

  25. 5. Sort • General Description:Given a set of data cases, rank them according tosome ordinal metric.Examples:- Order the cars by weight.- Rank the cereals by calories.

  26. 6. Determine Range General Description:Given a set of data cases and an attribute of interest,find the span of values within the set.Examples:- What is the range of film lengths?- What is the range of car horsepowers?- What actresses are in the data set?

  27. 7. Characterize Distribution General Description:Given a set of data cases and a quantitative attribute ofinterest, characterize the distribution of that attribute’svalues over the set.Examples:- What is the distribution of carbohydrates in cereals?- What is the age distribution of shoppers?

  28. Find Anomalies General Description:Identify any anomalies within a given set of data caseswith respect to a given relationship or expectation,e.g. statistical outliers.Examples:- Are there any outliers in protein?- Are there exceptions to the relationship betweenhorsepower and acceleration?

  29. 9. Cluster • General Description:Given a set of data cases, find clusters of similarattribute values.Examples:- Are there groups of cereals w/ similar fat/calories/sugar?- Is there a cluster of typical film lengths?

  30. 10. Correlate • General Description:Given a set of data cases and two attributes, determineuseful relationships between the values of those attributes.Examples:- Is there a correlation between carbohydrates and fat?- Is there a correlation between country of origin and MPG?- Do different genders have a preferred payment method?- Is there a trend of increasing film length over the years?

  31. Compound tasks • “Sort the cereal manufacturers by average fatcontent”Compute derived value; Sort • “Which actors have co-starred with JuliaRoberts?”Filter; Retrieve value

  32. What questions were left out? • Basic math“Which cereal has more sugar, Cheerios or Special K?”“Compare the average MPG of American and Japanese cars.” • Uncertain criteria“Does cereal (q, Y, Zj) sound tasty?”“What are the characteristics of the most valued customers? • Higher-level tasks“How do mutual funds get rated?”“Are there car aspects that Toyota has concentrated on?” • More qualitative comparison“How does the Toyota RAV4 compare to the Honda CRV?”“What other cereals are most similar to Trix?”

  33. Concerns • InfoVis tools may have influencedstudents’ questions • Graduate students as group being studiedHow about professional analysts? • Subjective – Not an exact science

  34. Analytic gaps – • “obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision making and learning.”

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