Visual Computing. Lecture 2 Visualization, Data, and Process

Transcription

1 Visual Computing Lecture 2 Visualization, Data, and Process

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

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

4 Pipeline 3 Visualization Process

5 Pipeline 4 Knowledge Discovery (Data Mining)

6 A Data Analysis Pipeline Raw Data Processed Data Hypotheses Models Results D Cleaning Filtering Transforming A Statistical Analysis Pattern Rec Knowledge Disc Validation 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 Characteristics of user Perceptual and cognitive abilities Knowledge of domain, data, tasks, tools Characteristics of graphical mappings Types, size, structure Semantics, completeness, accuracy 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 Visualization Components Human Abilities Design Principles Visual perception Visual display Cognition Interaction Motor skills Imply Inform design Frameworks Data types Tasks Constrain design Techniques Graphs & plots Maps Trees & Networks Volumes & Vectors Design Process Iterative design Design studies Evaluation

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, , , , Hierarchical (successive inclusion) Region: Continent > Country > State > City Animal > Mammal > Horse Adapted from Stone & Zellweger 11

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 Logical groupings of techniques (Keim) Numbers, text, graphs, software,. 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 usercontrolled

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

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

37 Interface Design and Usability Engineering Goals: Articulate: who users are their key tasks Task centered system design Methods: Participatory design Evaluate tasks Usercentered design Brainstorm designs Psychology of everyday things Participatory interaction User involvement Task scenario Representation walk& metaphors through low fidelity prototyping methods Products: User and task descriptions Throw-away paper prototypes Refined designs Graphical screen design Interface guidelines Style guides Completed designs Usability testing Field testing Heuristic evaluatio n high fidelity prototyping methods Testable prototypes Alpha/beta systems or complete specification