1. Marti Hearst SIMS 247 SIMS 247 Lecture 3 Graphing Basics, Continued January 27, 1998

2. Marti Hearst SIMS 247 Today Finish graphing basicsFinish graphing basics Demonstrate on web access exampleDemonstrate on web access example Discuss Tufte’s Data Ink Maximization principleDiscuss Tufte’s Data Ink Maximization principle

3. Marti Hearst SIMS 247 Types of Symbolic Displays (Kosslyn 89) GraphsGraphs ChartsCharts MapsMaps DiagramsDiagrams

4. Marti Hearst SIMS 247 Types 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

5. Marti Hearst SIMS 247 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

6. Marti Hearst SIMS 247 Standard Graph Types Scatter plotsScatter plots Line graphsLine graphs Time series (strip charts)Time series (strip charts) Dot plotsDot plots Bar ChartsBar Charts Pie ChartsPie Charts Layer GraphsLayer Graphs

7. Marti Hearst SIMS 247 Anatomy of a Graph FrameworkFramework –sets the stage –kinds of measurements, scale, … –internal and external ContentContent –point symbols, lines, areas, bars, … LabelsLabels –title, labels for axes, tic marks, legends, … BackgroundBackground –does not impart essential meaning

8. Marti Hearst SIMS 247 Choosing 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 than one independent variable?What happens when there is more than one independent variable? –Most important is assigned to X axis –Other(s) differentiated by mark symbol I D

9. Marti Hearst SIMS 247 Basic 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

10. Marti Hearst SIMS 247 Combining Data Types in Graphs (adapted from Kosslyn 89) Nominal by Nominal: Use a Chart

11. Marti Hearst SIMS 247 Scatter Plots QualitativelyQualitatively 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 PlacePlace points of interest in context apply shapes or color to points representing special entities, see where they end up

12. Marti Hearst SIMS 247 Time Series Change over timeChange over time Facilitates finding trendsFacilitates finding trends Also known as “strip charts”Also known as “strip charts”

13. Marti Hearst SIMS 247 Web Page Visit Behavior What are our goals?What are our goals? What questions do we want to answer?What questions do we want to answer? What kind of data might we collect?What kind of data might we collect? How might we convey this information?How might we convey this information? Who is the audience?Who is the audience?

14. Marti Hearst SIMS 247 Web Access Data Types (consider the possible combinations)

15. Marti Hearst SIMS 247 Hypothetical Graphs length of page length of access URL # of accesses length of access # of accesses length of access length of page 0 5 10 15 20 25 30 35 40 45 short medium long very long days # of accesses url 1 url 2 url 3 url 4 url 5 url 6 url 7 # of accesses

16. Marti Hearst SIMS 247 How to Show Link Traversal? How to link together the to-links and from-links in our web access example?

17. Marti Hearst SIMS 247 Charts Structural / organizational materialStructural / organizational material –nominal by nominal Specify relationships among discrete members of a setSpecify relationships among discrete members of a set Not relating on quantitative dimensionsNot relating on quantitative dimensions Components of Charts (Kosslyn 89):Components of Charts (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

18. Marti Hearst SIMS 247 Mapping Types in Charts one-to-one one-to-manymany-to-many

19. Marti Hearst SIMS 247 Chart Example (organizational chart)

20. Marti Hearst SIMS 247 Chart Example (Software architecture, labels omitted, by Chen and Hong 97)

21. Marti Hearst SIMS 247 How to show link patterns in web access example? Problem: only shows one step Think about this for next time.

22. Marti Hearst SIMS 247 Graph/Chart Hybrids An area for innovationAn area for innovation Combine Structure with GraphicsCombine Structure with Graphics Example: Docuverse (Spring et. al 96)Example: Docuverse (Spring et. al 96) structure: file system structure graphics: color -> file age Example: TileBars (Hearst 95)Example: TileBars (Hearst 95) structure: document subtopics (columns) structure: faceted query (rows) graphics: gray-level -> number of hits

23. Marti Hearst SIMS 247 Docuverse (Spring et. al 96)

24. Marti Hearst SIMS 247 TileBars (Hearst 95)

25. Marti Hearst SIMS 247 Discussion: Tufte’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 he really getting at?What’s wrong with this? What is he really getting at?

26. Marti Hearst SIMS 247 Next Time: Multidimensional Graphing How do we handle cases with more than three variables? –Multiple views –Scatterplot matrix –Parallel Coordinates –Tufte examples: combine space and time –Interaction/animation across time

27. Marti Hearst SIMS 247 References for this Lecture Kosslyn, Stephen M. Understanding Charts and Graphs. Applied Cognitive Psychology, 3, 185-226. 1989Kosslyn, Stephen M. Understanding Charts and Graphs. Applied Cognitive Psychology, 3, 185-226. 1989 Spring, Michael B., Morse, Emile, and Heo, Misook. Multi-level Navigation of a Document Space. http://www.lis.pitt.edu/~spring/mlnds/nlnds/mlnds.htmlSpring, Michael B., Morse, Emile, and Heo, Misook. Multi-level Navigation of a Document Space. http://www.lis.pitt.edu/~spring/mlnds/nlnds/mlnds.html Schall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Perspective, 18 (2), 1995. http://www.spss.com/cool/papers/diamondw.htmlSchall, Matthew. SPSS DIAMOND: a visual exploratory data analysis tool. Perspective, 18 (2), 1995. http://www.spss.com/cool/papers/diamondw.html Hearst, M. TileBars, Visualization of Term Distirubtion in Full Text Information Access. Proceedings of ACM SIGCHI 95.Hearst, M. TileBars, Visualization of Term Distirubtion in Full Text Information Access. Proceedings of ACM SIGCHI 95. Tufte 83Tufte 83