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Laboratory Assignments

Laboratory Assignments. If time: Visualization, Perception & Cognition. Readings. Recall: Handout #2 New Reading Assignment. Today’s Schedule. Presentations of Visual Analysis Projects (continued) Discussion of Static Design Assignment Discussion of Workshop Exercises

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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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