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Visualization Basics

Visualization Basics . cs5764: Information Visualization Chris North. 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

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Visualization Basics

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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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