Visual Computing

1. Visual Computing Lecture 2 Visualization, Data, and Process

2. Pipeline 1High Level Visualization Process • Data Modeling • Data Selection • Data to Visual Mappings • Scene Parameter Settings (View Transforms) • Rendering

3. Pipeline 2Computer Graphics • Modeling • Viewing • Clipping • Hidden Surface Removal • Projection • Rendering

4. Pipeline 3Visualization Process

5. Pipeline 4Knowledge Discovery(Data Mining)

6. A Data Analysis Pipeline Raw Data Processed Data HypothesesModels Results D Cleaning Filtering Transforming Statistical Analysis Pattern Rec Knowledge Disc Validation A B C

7. Where Does Visualization Come In? • All stages can benefit from visualization • A: identify bad data, select subsets, help choose transforms (exploratory) • B: help choose computational techniques, set parameters, use vision to recognize, isolate, classify patterns (exploratory) • C: Superimpose derived models on data (confirmatory) • D: Present results (presentation)

8. What do we need to know to do Information Visualization? • Characteristics of data • Types, size, structure • Semantics, completeness, accuracy • Characteristics of user • Perceptual and cognitive abilities • Knowledge of domain, data, tasks, tools • Characteristics of graphical mappings • What are possibilities • Which convey data effectively and efficiently • Characteristics of interactions • Which support the tasks best • Which are easy to learn, use, remember

9. Design Principles • Visual display • Interaction • Human Abilities • Visual perception • Cognition • Motor skills Imply Inform design Constrain design • Techniques • Graphs & plots • Maps • Trees & Networks • Volumes & Vectors • … • Design Process • Iterative design • Design studies • Evaluation • Frameworks • Data types • Tasks Visualization Components

10. Issues Regarding Data • Type may indicate which graphical mappings are appropriate • Nominal vs. ordinal • Discrete vs. continuous • Ordered vs. unordered • Univariate vs. multivariate • Scalar vs. vector vs. tensor • Static vs. dynamic • Values vs. relations • Trade-offs between size and accuracy needs • Different orders/structures can reveal different features/patterns

11. Types of Data • Quantitative (allows arithmetic operations) • 123, 29.56, … • Categorical (group, identify & organize; no arithmetic) Nominal (name only, no ordering) • Direction: North, East, South, West Ordinal (ordered, not measurable) • First, second, third … • Hot, warm, cold Interval (starts out as quantitative, but is made categorical by subdividing into ordered ranges) • Time: Jan, Feb, Mar • 0-999, 1000-4999, 5000-9999, 10000-19999, … Hierarchical (successive inclusion) • Region: Continent > Country > State > City • Animal > Mammal > Horse Adapted from Stone & Zellweger

12. Quantitative Data • Characterized by its dimensionality and the scales over which the data has been measured • Data scales comprise: • Interval scales - real data values such as degrees Celsius, but do not have a natural zero point. • Ratio data scales - like interval scales, but have a natural zero point and can be defined in terms of arbitrary units. • Absolute data scales - ratio scales that are defined in terms of non-arbitrary units.

13. Data Dimensions • Scalar - single value • e.g. Speed. It specifies how fast an object is traveling. • Vector – multi value • e.g Velocity. It tells the speed and direction. • Tensor – multi value • Scalars and vectors are special cases of tensors with degree (n) equal to 0 and 1 respectively. • The number of tensor components is given as dn, where d is the dimensionality of the coordinate system. • In a three dimensional coordinate system (d=3), a scalar (n=0) requires three values; and a tensor (n=2) requires 9 values. • There is a difference between a vector and a collection of scalars. • A multidimensional vector is a unified entity, the components of which are physically related. • The three components of a velocity vector of particle moving through three-space are coherently linked; while a collection scalar measurements such a weight, temperature, and index of refraction, are not.

14. Metadata • Metadata provides a description of the data and the things it represents. • e.g., a data value of 98.6 oF has two metadata attributes: temperature and temperature scale. • The value 98.6 has little meaning without the metadata attribute of temperature. • By adding Fahrenheit the attribute, we know the Fahrenheit sale is used. • Metadata may also include descriptions of experimental conditions and documentation of data accuracy and precision.

15. Issues Regarding Mappings • Variables include shape, size, orientation, color, texture, opacity, position, motion…. • Some of these have an order, others don’t • Some use up significant screen space • Sensitivity to occlusion • Domain customs/expectations

16. www3.sympatico.ca/blevis/Image10.gif

17. Importance of Evaluation • Easy to design bad visualizations • Many design rules exist – many conflict, many routinely violated • 5 E’s of evaluation: effective, efficient, engaging, error tolerant, easy to learn • Many styles of evaluation (qualitative and quantitative): • Use/case studies • Usability testing • User studies • Longitudinal studies • Expert evaluation • Heuristic evaluation

18. Categories of Mappings • Based on data characteristics • Numbers, text, graphs, software, …. • Logical groupings of techniques (Keim) • Standard: bars, lines, pie charts, scatterplots • Geometrically transformed: landscapes, parallel coordinates • Icon-based: stick figures, faces, profiles • Dense pixels: recursive segments, pixel bar charts • Stacked: treemaps, dimensional stacking • Based on dimension management (Ward) • Dimension subsetting: scatterplots, pixel-oriented methods • Dimension reconfiguring: glyphs, parallel coordinates • Dimension reduction: PCA, MDS, Self Organizing Maps • Dimension embedding: dimensional stacking, worlds within worlds

19. Scatterplot Matrix • Each pair of dimensions generates a single scatterplot • All combinations arranged in a grid or matrix, each dimension controls a row or column • Look for clusters, outliers, partial correlations, trends

20. Parallel Coordinates • Each variable/dimension is a vertical line • Bottom of line is low value, top is high • Each record creates a polyline across all dimensions • Similar records cluster on the screen • Look for clusters, outliers, line angles, crossings

21. Star Glyph • Glyphs are shapes whose attributes are controlled by data values • Star glyph is a set of N rays spaced at equal angles • Length of each ray proportional to value for that dimension • Line connects all endpoints of shape • Lay glyphs out in rows and columns • Look for shape similarities and differences, trends

22. Other Types of Glyphs

23. Dimensional Stacking • Break each dimension range into bins • Break the screen into a grid using the number of bins for 2 dimensions • Repeat the process for 2 more dimensions within the subimages formed by first grid, recurse through all dimensions • Look for repeated patterns, outliers, trends, gaps

24. Pixel-Oriented Techniques • Each dimension creates an image • Each value controls color of a pixel • Many organizations of pixels possible (raster, spiral, circle segment, space-filling curves) • Reordering data can reveal interesting features, relations between dimensions

25. Methods to Cope with Scale • Many modern datasets contain large number of records (millions and billions) and/or dimensions (hundreds and thousands) • Several strategies to handle scale problems • Sampling • Filtering • Clustering/aggregation • Techniques can be automated or user-controlled

26. Examples of Data Clustering

27. Example of Dimension Clustering

28. Example of Data Sampling

29. The Visual Data Analysis (VDA) Process • Overview • Filter/cluster/sample • Scan • Select “interesting” • Details on demand • Link between different views

30. Issues Regarding Users • What graphical attributes do we perceive accurately? • What graphical attributes do we perceive quickly? • Which combinations of attributes are separable? • Coping with change blindness • How can visuals support the development of accurate mental models of the data? • Relative vs. absolute judgements – impact on tasks

31. Role of Perception MC Escher

32. Consider the Following

33. Role of Perception • Users interact with visualizations based on what they see. (e.g. black spots at intersection of white lines) • Must understand how humans perceive images. • Primitive image attributes: shape, color, texture, motion, etc.

34. Visualization Example Op Art - Victor Vasarely OpGlyph (Marchese)

35. Rules of Visual Perception Proximity Similarity Continuity Closure Symmetry Foreground & Background Size Principles of Art & Design Emphasis / Focal Point Balance Unity Contrast Symmetry / Asymmetry Movement / Rhythm Pattern / Repetition Gestalt Psychology

36. Issues Regarding Interactions • Interaction critical component • Many categories of techniques • Navigation, selection, filtering, reconfiguring, encoding, connecting, and combinations of above • Many “spaces” in which interactions can be applied • Screen/pixels, data, data structures, graphical objects, graphical attributes, visualization structures