marti hearst sims 247 sims 247 lecture 3 graphing basics, continued january 27, 1998

Marti HearstSIMS 247

SIMS 247 Lecture 3SIMS 247 Lecture 3Graphing Basics, ContinuedGraphing Basics, Continued

January 27, 1998January 27, 1998


TodayToday

• Finish graphing basicsFinish graphing basics• Demonstrate on web access Demonstrate on web access

exampleexample• Discuss Tufte’s Data Ink Discuss Tufte’s Data Ink

Maximization principleMaximization principle


Types of Symbolic DisplaysTypes of Symbolic Displays(Kosslyn 89)(Kosslyn 89)

• GraphsGraphs

• ChartsCharts

• MapsMaps

• DiagramsDiagrams

0

20

40

60

80

100

1st Q tr 2nd Q tr 3rd Q tr 4th Q tr

East

West

North

T ype n am e he reT ype ti t le he re





Types of Symbolic DisplaysTypes of Symbolic Displays• GraphsGraphs

– at least two scales required– values associated by a symmetric “paired

with” relation• Examples: scatter-plot, bar-chart, layer-graph

• ChartsCharts– discrete relations among discrete entities– structure relates entities to one another– lines and relative position serve as links

• Examples: family-tree, flow-chart, network diagram


Types of Symbolic Displays (cont.)Types of Symbolic Displays (cont.)

• MapsMaps– internal relations determined (in part) by

the spatial relations of what is pictured– labels paired with locations

• Examples: map of census data, topographic maps

• DiagramsDiagrams– schematic pictures of objects or entities– parts are symbolic (unlike photographs)

• Examples: how-to illustrations, figures in a manual


Standard Graph TypesStandard Graph Types

• Scatter plotsScatter plots• Line graphsLine graphs• Time series Time series (strip (strip

charts)charts)

• Dot plotsDot plots• Bar ChartsBar Charts• Pie ChartsPie Charts• Layer GraphsLayer Graphs


Choosing the AxesChoosing the Axes• Independent vs. Dependent variablesIndependent vs. Dependent variables

– the dependent variable changes relative to the independent one• sales against season• tax revenue against city

• What happens when there is more What happens when there is more than one independent variable?than one independent variable?– Most important is assigned to X axis– Other(s) differentiated by mark symbol

I

D


Basic Types of DataBasic Types of Data

• Qualitative -- nominalQualitative -- nominal– no inherent order (for comparisons)

• city names, types of diseases, ...

• Qualitative -- ordinalQualitative -- ordinal– ordered, but not at measurable intervals

• first, second, third, …• cold, warm, hot

• Quantitative -- interval and ratioQuantitative -- interval and ratio


Combining Data Types in GraphsCombining Data Types in Graphs(adapted from Kosslyn 89)(adapted from Kosslyn 89)

Scale I Scale II Example Information Available

Nominal Ordinal States by rank in oilproduction

N(N-1)/2 inequality comparisons

Nominal Interval Students by SAT score Map N objects onto intervalscale

Ordinal Ordinal Ranked oil production byranked coal production

Relative rank comparisons

Ordinal Interval Ranked oil production bycoal production

Comparison of differences fordifferent ranks

Interval Interval SAT math score by SATenglish score

Difference on one dimensionas a function of the other

Nominal by Nominal: Use a Chart


Scatter PlotsScatter Plots• Qualitatively determine if variablesQualitatively determine if variables

– highly correlated• linear mapping between horizonal & vertical axes

– nonlinear relationship• a curvature in the pattern of plotted points

– low correlation• spherical, rectangular, or irregular distributions

• Place points of interest in contextPlace points of interest in context• apply shapes or color to points representing special

entities, see where they end up


Time SeriesTime Series

• Change over timeChange over time• Facilitates finding trendsFacilitates finding trends• Also known as “strip charts”Also known as “strip charts”


Web Page Visit BehaviorWeb Page Visit Behavior

• What are our goals?What are our goals?• What questions do we want to What questions do we want to

answer?answer?• What kind of data might we collect?What kind of data might we collect?• How might we convey this How might we convey this

information?information?• Who is the audience?Who is the audience?


Web Access Data TypesWeb Access Data Types(consider the possible combinations)(consider the possible combinations)

Web Pages Users

Nominal URL (address)Domain Type (org, edu, gov, …)From-linkTo-linkType (jpeg, text, binary, …)

GenderPhysical locationJob type

Ordinal Page length (short, medium, long)

Quantitative Number of accessesLength (in time) of accessesAge of page

Age


Hypothetical GraphsHypothetical Graphs

length of page

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How to Show Link Traversal?How to Show Link Traversal?

How to link together the to-links How to link together the to-links and from-links in our web access and from-links in our web access example?example?


ChartsCharts

• Structural / organizational materialStructural / organizational material– nominal by nominal

• Specify relationships among discrete Specify relationships among discrete members of a setmembers of a set

• Not relating on quantitative dimensionsNot relating on quantitative dimensions• Components of ChartsComponents of Charts (Kosslyn 89):(Kosslyn 89):

– directed vs. undirected links – how many types of links

– types of mapping e.g., one-to-one, one-to-many, many-to-many

• Tables can also be considered chartsTables can also be considered charts


Mapping Types in ChartsMapping Types in Charts

one-to-one

one-to-many many-to-many


Chart ExampleChart Example(organizational chart)(organizational chart)


Chart ExampleChart Example(Software architecture, labels omitted, by Chen and Hong 97)(Software architecture, labels omitted, by Chen and Hong 97)


How to show link patterns in web How to show link patterns in web access example? access example?

Problem: only shows one stepThink about this for next time.


Graph/Chart HybridsGraph/Chart Hybrids

• An area for innovation An area for innovation • Combine Structure with GraphicsCombine Structure with Graphics• Example: Docuverse Example: Docuverse (Spring et. al 96)(Spring et. al 96)

• structure: file system structure• graphics: color -> file age

• Example: TileBars Example: TileBars (Hearst 95)(Hearst 95)

• structure: document subtopics (columns)• structure: faceted query (rows)• graphics: gray-level -> number of hits


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Discussion:Discussion: Tufte’s Notion of Data Ink MaximizationTufte’s Notion of Data Ink Maximization

• What is the main idea?What is the main idea?– draw viewers attention to the

substance of the graphic– the role of redundancy– principles of editing and redesign

• What’s wrong with this? What is What’s wrong with this? What is he really getting at?he really getting at?


Next Time:Next Time:Multidimensional GraphingMultidimensional Graphing

How do we handle cases with more How do we handle cases with more than three variables?than three variables?– Multiple views– Scatterplot matrix– Parallel Coordinates– Tufte examples: combine space and

time– Interaction/animation across time


References for this LectureReferences for this Lecture

• Kosslyn, Stephen M. Understanding Charts and Kosslyn, Stephen M. Understanding Charts and Graphs. Graphs. Applied Cognitive Psychology, 3, Applied Cognitive Psychology, 3, 185-226. 1989185-226. 1989

• Spring, Michael B., Morse, Emile, and Heo, Misook. Spring, Michael B., Morse, Emile, and Heo, Misook. Multi-level Navigation of a Document Space. Multi-level Navigation of a Document Space. http://www.lis.pitt.edu/~spring/mlnds/nlnds/mlnds.htmlhttp://www.lis.pitt.edu/~spring/mlnds/nlnds/mlnds.html

• Schall, Matthew. SPSS DIAMOND: a visual exploratory Schall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. data analysis tool. Perspective, 18 (2), Perspective, 18 (2), 1995. 1995. http://www.spss.com/cool/papers/diamondw.htmlhttp://www.spss.com/cool/papers/diamondw.html

• Hearst, M. TileBars, Visualization of Term Distirubtion Hearst, M. TileBars, Visualization of Term Distirubtion in Full Text Information Access. in Full Text Information Access. Proceedings of ACM Proceedings of ACM SIGCHI 95SIGCHI 95..

• Tufte 83Tufte 83

marti hearst sims 247 sims 247 lecture 3 graphing basics, continued january 27, 1998

Documents

marti hearst sims

chart slide

manual slide

d slide

ratio slide

data types

network diagram slide

essential meaning slide