Laboratory AssignmentsLaboratory Assignments

If time: Visualization, Perception & CognitionIf time: Visualization, Perception & Cognition

ReadingsReadings

Recall: Handout #2Recall: Handout #2 New Reading AssignmentNew Reading Assignment

Today’s ScheduleToday’s Schedule

Presentations of Visual Analysis Projects Presentations of Visual Analysis Projects (continued)(continued)

Discussion of Static Design AssignmentDiscussion of Static Design Assignment Discussion of Workshop ExercisesDiscussion of Workshop Exercises If time: Perception & Cognition in If time: Perception & Cognition in

Visualization StudiesVisualization Studies

Visualizations of Home/House. Child Katrina SurvivorsVisualizations of Home/House. Child Katrina Survivors

Recall: House/Home (Katrina Recall: House/Home (Katrina Victim)Victim)

House/HomeHouse/Home

House as roofHouse as roof

House?HomeHouse?Home

Sources: Slide show of Katrina victim’s drawings of Sources: Slide show of Katrina victim’s drawings of house/home,house/home, Dewann, S. Dewann, S. “Using Crayons to Exorcise Katrina, “Using Crayons to Exorcise Katrina, New York TimesNew York Times, Monday September 17, , Monday September 17, 2007, Arts Section, B1,5.2007, Arts Section, B1,5.

Cognitive issuesCognitive issues

Visualization as a tool useful forVisualization as a tool useful for

aiding comprehension and understanding?aiding comprehension and understanding?

Graphical visualization to Graphical visualization to support more efficient task support more efficient task performanceperformance

– Allowing substitution of rapid perceptualAllowing substitution of rapid perceptual

influences for difficult logical inferencesinfluences for difficult logical inferences– Reducing search for information required for task Reducing search for information required for task

completioncompletion

(Sometimes text is better, however)(Sometimes text is better, however)

IssuesIssues

Cognitive ArtifactsCognitive Artifacts Matching Representation to TaskMatching Representation to Task

(Tic-tac-toe, flight schedules)(Tic-tac-toe, flight schedules) Representations Aid Info Access and Representations Aid Info Access and

ComputationComputation

(Medical prescriptions, Roman numerals, (Medical prescriptions, Roman numerals, mapslegends)mapslegends)

Naturalness and Experiential CognitionNaturalness and Experiential Cognition

Cognitive Maps (1st Lab Cognitive Maps (1st Lab Exercise)Exercise)

You have some existing internal model of theYou have some existing internal model of the

system, stops, how to get theresystem, stops, how to get there glance at SFU map for helpglance at SFU map for help Refine your internal model, clarifying itemsRefine your internal model, clarifying items

and extending itand extending it

Note differences between your map & the Note differences between your map & the official oneofficial one

Process Models: Navigation in Process Models: Navigation in Visual systemsVisual systems

-process by which a person looks at a graphic -process by which a person looks at a graphic and makes some use of itand makes some use of it

substepssubsteps Can you describe process?Can you describe process? Navigation in visual systems - Creation and Navigation in visual systems - Creation and

interpretation of an internal mental modelinterpretation of an internal mental model

•Information foragingInformation foraging

Search for schema (representation)Search for schema (representation) Problem solve to trade off featuresProblem solve to trade off features Search for a new schema that reducesSearch for a new schema that reduces

problem to a simple trade-offproblem to a simple trade-off Package the patterns found in somePackage the patterns found in some

output productoutput product

NavigationNavigation

•CrystallizationCrystallization

ProcessProcess

BrowsingBrowsing

Useful whenUseful when Good underlying structure so that items close to oneGood underlying structure so that items close to one

another can be inferred to be similaranother can be inferred to be similar Users are unfamiliar with collection contentsUsers are unfamiliar with collection contents Users have limited understanding of how system isUsers have limited understanding of how system is

organized and prefer less cognitively loaded methodorganized and prefer less cognitively loaded methodof explorationof exploration

Users have difficulty verbalizing underlyingUsers have difficulty verbalizing underlyinginformation needinformation need

Information is easier to recognize than describeInformation is easier to recognize than describe

User Tasks in Visualization User Tasks in Visualization Environments--Eleven basic Environments--Eleven basic actionsactions identify, locate, distinguish, categorize,identify, locate, distinguish, categorize,

cluster, distribution, rank, compare withincluster, distribution, rank, compare withinrelations, compare between relations,relations, compare between relations,associate, correlateassociate, correlate

Data Types and TasksData Types and Tasks

TerminologyTerminology

Data case – An entity in the data setData case – An entity in the data set Attribute – A value measured for all dataAttribute – A value measured for all data

casescases Aggregation function – A function thatAggregation function – A function that

creates a numeric representation for a setcreates a numeric representation for a setof data cases (eg, average, count, sum)of data cases (eg, average, count, sum)

Steps in Creating VisualizationSteps in Creating Visualization

1. Retrieve Value1. Retrieve ValueGeneral Description:General Description:Given a set of specific cases, find attributes ofGiven a set of specific cases, find attributes ofthose cases.those cases.Examples:Examples:- What is the mileage per gallon of the Audi - What is the mileage per gallon of the Audi TT?TT?- How long is the movie Gone with the Wind?- How long is the movie Gone with the Wind?

2. Filter2. Filter

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

3. Compute Derived Value3. Compute Derived Value

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

4. Find Extremum4. Find Extremum

General Description:General Description: Find data cases possessing an extreme value of anFind data cases possessing an extreme value of an

attribute over its range within the data set.attribute over its range within the data set.Examples:Examples:- What is the car with the highest MPG?- What is the car with the highest MPG?- What director/film has won the most awards?- What director/film has won the most awards?- What Robin Williams film has the most recent- What Robin Williams film has the most recentrelease date?release date?

5. Sort5. Sort

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

6. Determine Range6. Determine Range

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

7. Characterize Distribution7. Characterize Distribution

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

8.8. Find AnomaliesFind Anomalies

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

9. Cluster9. Cluster

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

10. Correlate10. Correlate

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

Compound tasksCompound tasks

““Sort the cereal manufacturers by average fatSort the cereal manufacturers by average fatcontent”content”Compute derived value; SortCompute derived value; Sort

““Which actors have co-starred with JuliaWhich actors have co-starred with JuliaRoberts?”Roberts?”Filter; Retrieve valueFilter; Retrieve value

What questions were left out?What questions were left out?

Basic mathBasic math“Which cereal has more sugar, Cheerios or Special K?”“Which cereal has more sugar, Cheerios or Special K?”“Compare the average MPG of American and Japanese “Compare the average MPG of American and Japanese cars.”cars.”

Uncertain criteriaUncertain criteria“Does cereal (q, Y, Zj) sound tasty?”“Does cereal (q, Y, Zj) sound tasty?”“What are the characteristics of the most valued customers?“What are the characteristics of the most valued customers?

Higher-level tasksHigher-level tasks“How do mutual funds get rated?”“How do mutual funds get rated?”“Are there car aspects that Toyota has concentrated on?”“Are there car aspects that Toyota has concentrated on?”

More qualitative comparisonMore qualitative comparison“How does the Toyota RAV4 compare to the Honda CRV?”“How does the Toyota RAV4 compare to the Honda CRV?”“What other cereals are most similar to Trix?”“What other cereals are most similar to Trix?”

ConcernsConcerns

InfoVis tools may have influencedInfoVis tools may have influencedstudents’ questionsstudents’ questions

Graduate students as group being studiedGraduate students as group being studiedHow about professional analysts?How about professional analysts?

Subjective – Not an exact scienceSubjective – Not an exact science

Analytic gaps – Analytic gaps –

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