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Essentials of Information Visualization: Design, Evaluation, and Data Transformation

This guide covers fundamental concepts in information visualization, emphasizing design processes and evaluation methods. Key milestones for group projects include defining design concepts, conducting formative evaluations, and presenting final results. It discusses the visualization pipeline, key data transformations, and visual mappings. The document also highlights user interaction and measures of effectiveness in visualization design. Furthermore, foundational readings on multidimensional visualization tools and various visualization models provide a basis for understanding visual communication's role in data insight.

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Essentials of Information Visualization: Design, Evaluation, and Data Transformation

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  1. Visualization Basics cs5764: Information Visualization Chris North

  2. Project • Milestones: • Team: choose team (due Wed!) • Design Concept & Presentation: problem, lit. review, design, schedule (4 weeks) • Formative Eval & Initial Impl • Final presentation: final results • Final paper: publishable?

  3. To Do … • Hand in HW1 now • Read: CMS chapter 1 handout (pg 17-end) • Read: Claims analysis handout • HW 2, due next Wed: MultiD Vis Tools • Paper next wed: “Parallel Coordinates”, Inselberg • vidhya • Get going on Project! 3 weeks • Wed: Go to Kent Square suite 318, GigaPixel Display

  4. Review • What is the purpose of visualization? • How do we accomplish that?

  5. Basic Visualization Model

  6. Goal Data Data transfer Insight (learning, knowledge extraction)

  7. Method Data Data transfer Insight ~Map-1: visual → data insight Map: data → visual Visualization Visual transfer (communication bandwidth)

  8. Visual Mappings Data • Visual Mappings must be: • Computable (math) • visual = f(data) • Comprehensible (invertible) • data = f-1(visual) • Creative! Map: data → visual Visualization

  9. PolarEyes

  10. Visualization Pipeline task Raw data (information) Data tables Visualstructures Visualization(views) Visualmappings Viewtransformations Datatransformations User interaction

  11. Data Table: Canonical data model • Visualization requires structure, data model • (All?) information can be modeled as data tables

  12. Data Table Attributes(aka: dimensions, variables, fields, columns, …) • Values • Data Types: • Quantitative • Ordinal • Categorical • Nominal Items (aka: tuples, cases, records, data points, rows, …)

  13. Attributes • Dependent variables (measured) • Independent variables (controlled)

  14. Data Transformations • Data table operations: • Selection • Projection • Aggregation • r = f(rows) • c = f(cols) • Join • Transpose • Sort • …

  15. Visualization Pipeline task Raw data (information) Data tables Visualstructures Visualization(views) Visualmappings Viewtransformations Datatransformations User interaction

  16. Visual Structure • Spatial substrate • Visual marks • Visual properties

  17. Visual Mapping: Step 1 • Map: data items  visual marks Visual marks: • Points • Lines • Areas • Volumes • Glyphs

  18. Visual Mapping: Step 2 • Map: data items  visual marks • Map: data attributes  visual properties of marks Visual properties of marks: • Position, x, y, z • Size, length, area, volume • Orientation, angle, slope • Color, gray scale, texture • Shape • Animation, blink, motion

  19. Example: Spotfire • Film database • Film -> dot • Year  x • Length  y • Popularity  size • Subject  color • Award?  shape

  20. Visual Mapping Definition Language • Films  dots • Year  x • Length  y • Popularity  size • Subject  color • Award?  shape

  21. E.g. Linear Encoding • year  x x – xmin year – yearmin xmax – xmin yearmax – yearmin year x yearmax xmax yearmin xmin =

  22. The Simple Stuff • Univariate • Bivariate • Trivariate

  23. Univariate • Dot plot • Bar chart (item vs. attribute) • Tukey box plot • Histogram

  24. Bivariate • Scatterplot

  25. Trivariate • 3D scatterplot, spin plot • 2D plot + size (or color…)

  26. The Challenges? • evaluate or compare designs? • Effectiveness? • Data transforations, whats the right data table? • More data, multidimensional • Too many dots, limited space • Choosing which data? • Semantics • System limitations

  27. Visualization Design

  28. HCI Design Process • Iterative, progressively concrete 1. Analyze 2. Design 3. Evaluate

  29. HCI UI Evaluation Metrics • User learnability: • Learning time • Retention time • User performance: *** • Performance time • Success rates • Error rates, recovery • Clicks, actions • User satisfaction: • Surveys Not “user friendly” Measure while users perform benchmark tasks

  30. Visualization Design • Analyze problem: • Data: schema, structures, scalability • Tasks/insights • Prioritize tasks and data attributes • Design solutions: • Data transformations • Mappings: data→visual • Overview strategies • Navigation strategies • Interaction techniques • multiple views vs. integrated views • Evaluate solutions: • Analytic: Claims analysis, tradeoffs • Empirical: Usability studies, controlled experiments

  31. 1. Analyze the Problem • Data: • Information structure • Scalability*** • Users: • Tasks • Existing solutions (literature review)

  32. Information Structures • Tabular: (multi-dimensional) • Spatial & Temporal: • 1D: • 2D: • 3D: • Networks: • Trees: • Graphs: • Text & Documents:

  33. Data Scalability • # of attributes (dimensionality) • # of items • Value range(e.g. bits/value)

  34. User Tasks Forms can do this • Easy stuff: • Reduce to only 1 data item or value • Stats: Min, max, average, % • Search: known item • Hard stuff: • Require seeing the whole • Patterns: distributions, trends, frequencies, structures • Outliers: exceptions • Relationships: correlations, multi-way interactions • Tradeoffs: combined min/max • Comparisons: choices (1:1), context (1:M), sets (M:M) • Clusters: groups, similarities • Anomalies: data errors • Paths: distances, ancestors, decompositions, … Visualization can do this!

  35. Some Visualization Design Principles

  36. Effectiveness & Expressiveness (Mackinlay) • Effectiveness • Cleveland’s rules • Expressiveness • Encodes all data • Encodes only the data

  37. Ranking Visual Properties • Position • Length • Angle, Slope • Area, Volume • Color Design guideline: • Map more important data attributes to more accurate visual attributes (based on user task) Increased accuracy for quantitative data (Cleveland and McGill) • Categorical data: • Position • Color, Shape • Length • Angle, slope • Area, volume • (Mackinlay hypoth.)

  38. Example • Hard drives for sale: price ($), capacity (MB), quality rating (1-5)

  39. Eliminate “Chart Junk” (Tufte) • How much “ink” is used for non-data? • Reclaim empty space (% screen empty) • Attempt simplicity(e.g. am I using 3djust for coolness?)

  40. Increase Data Density (Tufte) • Calculate data/pixel “A pixel is a terrible thing to waste.” (Shneiderman)

  41. Interaction Approach • Direct Manipulation (Shneiderman) • Visual representation • Rapid, incremental, reversible actions • Pointing instead of typing • Immediate, continuous feedback

  42. Information Visualization Mantra (Shneiderman) • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand

  43. Cost of Knowledge / Info Foraging (Card, Piroli, et al.) • Frequently accessed info should be quick • At expense of infrequently accessed info • Bubble up “scent” of details to overview

  44. The “Insight” Factor • Avoid the temptation to design a form-based search engine • More tasks than just “search” • How do I know what to “search” for? • What if there’s something better that I don’t know to search for? • Hides the data

  45. Break out of the Box • Resistance is not futile! • Creativity; Think bigger, broader • Does the design help me explore, learn, understand? • Reveal the data

  46. Class Motto Show me the data!

  47. Claims Analysis • Identify an important design feature • + positive effects of that feaure • - negative effects of that feature

  48. Exercise: Pie vs. Bar • Data: population of the 50 states • Pie: state and pop overloaded on circumf. • Bar: state on x, pop on y

  49. Stacked Bar AK AL AR CA CO …

  50. Upcoming Tabular (multi-dimensional) Spatial & Temporal 1D / 2D 3D Networks Trees Graphs Text & Docs Overview strategies Navigation strategies Interaction techniques Development Evaluation

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