Information Visualization. Visualization and Cognition. Visualization and Graphics. External Cognition. Overview. Reference

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1 Information Visualization Overview External Cognition Information Visualization (definitions, origins, applications) Cognitive Amplification (Knowledge crystallization) Mapping Data to Visual Form (Visualization Reference Model) View Transforms Interaction/Transformation Controls Reference S.K.Card, J.D.Mackinlay, B.Shneiderman, Information Visualization, Readings in Information Visualization: Using Vision to Think, Morgan Kaufman, Chapter 1. Visualization and Cognition Visual metaphors are intimately connected to cognitive processes. Mental processes are considerably aided by external aids. Among the human senses, visual perception is the most important (influential) to cognition. Graphical inventions of all sorts are crucial external aids that make us smart. im- Current graphics technology makes a dramatic contribution: proved rendering, real-time interaction, low cost. ITCS 6010/8010: Information Visualization 1 Information Visualization ITCS 6010/8010: Information Visualization 2 Information Visualization Visualization and Graphics Interactive computer graphics forms the underlying technology that drives (scientific or information) visualization High quality and flexible (programmable) rendering is available on commodity graphics hardware (polygon shaders of the order of millions of textured polygons/pixels/voxels per second) High speed computers makes it possible to process large quantities of data for amplifying cognition, reveal hidden patterns, new ways of gaining insight. Information Visualization: the application of this technology/medium to abstract data from business, education, software, large dynamic systems, etc. External Cognition Importance of the external world (cognitive artifacts of all kinds) in aiding thought and reasoning. Forms the basis for the intuition for the use and application of Information Visualization Example 1: Integer Multiplication Holding partial results in external memory is critical Visual addressing structure (long hand arithmetic) Other examples include slide rules, nomographs, graphing calculators. ITCS 6010/8010: Information Visualization 3 Information Visualization ITCS 6010/8010: Information Visualization 4 Information Visualization

2 Example 3: Sleep/Wake Cycles of Infants Example 2: Diagrams An important class of visual aids Challenger accident - O-ring analysis ITCS 6010/8010: Information Visualization 5 Information Visualization ITCS 6010/8010: Information Visualization 6 Information Visualization Conclusions Visual artifacts are crucial to aiding thought Human civilization has plenty of evidence of visual artifacts (writing, mathematics, printing, diagrams, computing) Information Visualization - exploits the dynamic interactive, inexpensive graphics computers to device new aids (visualizations) to enhance and amplify cognition. Goal of InfoVis : to assimilate massive amounts of information, understand and extract new knowledge. Visualization The Use of Computer-Supported, Interactive, Visual Representations of Data to Amplify Cognition Purpose of visualization is insight, not pictures Goals of insight: discovery, decision making, explanation Information visualization increases our ability to perform such cognitive activities. ITCS 6010/8010: Information Visualization 7 Information Visualization ITCS 6010/8010: Information Visualization 8 Information Visualization

3 Scientific Visualization The Use of Interactive, Visual Representations of Scientific Data, typically Physically based, to Amplify Cognition Dates back to late 80s - NSF report on Visualization in Scientific Computing Visualizations tend to be based on scientific data, to enhance scientists to see phenomena in the data Tends to be based on physical data - human anatomy, earth, molecules, etc. Visual mappings may themselves be abstract, but they tend to be mapped onto physical(geometric) entities, for example, ozone concentrations mapped onto (spherical shaped) model of earth. Examples of Scientific Visualization Medical Imaging (CT, MRI, Ultrasound, PET, SPECT, X-Ray) ITCS 6010/8010: Information Visualization 9 Information Visualization ITCS 6010/8010: Information Visualization 10 Information Visualization Example: Visible Human Project From CT Computational Metrology Rain Water Content in a Severe Storm ITCS 6010/8010: Information Visualization 11 Information Visualization ITCS 6010/8010: Information Visualization 12 Information Visualization

4 Financial Data Flow visualization Flow around a Post (vector, scalar fields) ITCS 6010/8010: Information Visualization 13 Information Visualization ITCS 6010/8010: Information Visualization 14 Information Visualization Load Distribution Information Visualization Tensor field visualization of a point load The Use of Computer-Supported, Interactive, Visual Representations of Abstract Data to Amplify Cognition Visualization of non-physical information - financial data, business information, abstract concepts, relationships or specifications Dual problems of mapping visible properties (color, texture, opacity), as well as transforming non-spatial abstractions to effective visual form Mass, complexity and high dimensionality is a serious problem in Information Visualization, in comparison to Scientific Visualization. ITCS 6010/8010: Information Visualization 15 Information Visualization ITCS 6010/8010: Information Visualization 16 Information Visualization

5 Examples of Information Visualization Views of WWW sites Table Lens - instantiating schemas and manipulating variables/cases for problem solving VHP browser Document workspaces, aided by clustering. Conclusions While visualization is the main focus, perceptualization is the underlying goal Sonification, Tactilization are complementary to visualization, however, visual sense has the highest bandwith. ITCS 6010/8010: Information Visualization 17 Information Visualization ITCS 6010/8010: Information Visualization 18 Information Visualization Origins of Information Visualization Playfair (1786) - use of lines, area to display data Bertin (1967), The Semiology of Graphics - introduced diagrams Tufte(1983) - theory of data graphics and emphasized maximization useful information Tuke (1977), Exploratory Data Analysis - goal was to provide statistical insight; box and whisker plots Cleveland,McGill Dynamic Graphics for Statistics Multivariate Data - Parallel coordinates (Inselberg,Dimsdale 1990) NSF Workshop on Scientific Visualization (1985) Origins of Information Visualization (contd) First IEEE Visualization conference (1990) - earth scientists, supercomputing scientists, fluid flow and medical visualization. Improvements in graphics hardware let users interact with large amounts of information - multivariate databases, document collections Information Visualization, originally introduced (Robertson, Mackinlay, Card 1990) Worlds within worlds (Beshers, 1990), Information Visualizer (Card, Robertson, Mackinlay, 1991) IEEE Symposium on Information Visualization (1996) - various international conferences/workshops Information Visualization journal (Palmgrave/Macmillan 2002) ITCS 6010/8010: Information Visualization 19 Information Visualization ITCS 6010/8010: Information Visualization 20 Information Visualization

6 Knowledge Crystallization Cognitive Amplification Goal is to gain insight into some data relative to a task. Involves gathering information, make sense of it via a schema, packages it into a form for communication/action. Information Visualization can aid in all steps of this process. Visualization Amplifies Cognition Increased Resources: high bandwith hierarchical interaction, increased working memory, storage of massive datasets, parallel visual processing Reduced Search: Clustering, highly scalable and compact representations. Pattern Recognition: visualizations support recognition (as opposed to recall), support abstraction/aggregation, selective omission, multilevel patterns Perceptual Inference: visual representations ease perception Perceptual Monitoring: supports monitoring large number of events, if well organized Manipulable Medium: supports interactived exploration and amplify user operations. ITCS 6010/8010: Information Visualization 21 Information Visualization ITCS 6010/8010: Information Visualization 22 Information Visualization Mapping Data to Visual Form Mapping Data to Visual Form Reference Model for Visualization Model reflects a series of data transformations Human interaction can adjust these transformations using user controls Data Transformations: maps raw data into Data Tables, relational descriptions, that can include metadata Visual Mappings: maps data tables into Visual Structures that combine spatial substrates (spatial domain), marks (geometry) and graphical properties (attributes). View Transformations: create views of Visual Structures, via modeling and camera transforms, clipping. ITCS 6010/8010: Information Visualization 23 Information Visualization ITCS 6010/8010: Information Visualization 24 Information Visualization

7 Data Tables Goal is to transform data into a set of relations, adding structure that makes it easier to map to visual forms. Data tables represent these relationships as well as metadata (descriptive information, for instance) Case i Case j Case k V ariable x V alue ix V alue jx V alue kx V ariable y V alue iy V alue jy V alue ky V ariable z V alue iz V alue jz V alue kz Rows represent variables, while column represents values as tuples, as a set of cases. The labels for rows and columns constitute metadata. Data Tables (contd) Variable Types Nominal (no ordering) Ordinal (ordering) Quantitative (can perform arithmetic) Data Transformations Values Derived Values Structure Derived Structure Values Derived Structure Structure Derived Values Data Transformations can be cascaded. ITCS 6010/8010: Information Visualization 25 Information Visualization ITCS 6010/8010: Information Visualization 26 Information Visualization Visual Structures Data tables are mapped to Visual Structures - mapping into the spatial domain with graphics primitives and their attributes Visual mappings must preserve the data Mapping is expressive, if all and only the data in the data table is visually represented (often difficult) Mapping is effective, if it is faster to interpret, leads to fewer errors, conveys more distinctions Example: a sine wave - displaying a time-amplitude curve is more effective than intensities as a function of time. Visual Perception Goal: Information visualization systems should set up visual representations so as the exploit human perception Perception, a well studied subject - but connection between perception and cognitive functions has only recently been investigated. Human visual system uses a 3-level hierarchical organization to partition limited bandwidth between high spatial resolution and wide aperture to sense the visual environment. An understanding of the human vision (eye) system is useful. ITCS 6010/8010: Information Visualization 27 Information Visualization ITCS 6010/8010: Information Visualization 28 Information Visualization

8 Human Vision System Human Vision System (contd) A movable lens focuses an image on to a substrate containing 125 million photoreceptors 6.5 million are color-detecting cones, rest black and white rods ITCS 6010/8010: Information Visualization 29 Information Visualization Distribution of the receptors are non-uniform, with the central area, the fovea, containing a high density of cones; peripheral areas, rods dominate. The retina has a combined (both eyes) field of 200 horizontally 135 vertically. ITCS 6010/8010: Information Visualization 30 Information Visualization Level 1: Retina Human Vision System Hierarchy Covers the non-foveal portions - density of cones is about cones/µm 2 Good at detecting motion, other changes in environment, and maintaining rough representation of locations of shapes previously examined. Level 2: Foveola Inner part of the fovea, about 400µm 2, in the center of the visual field. The high resolution field is moved to points of interest 1-5 times/sec at rates of 500 degrees/sec (also tiny movements, 70 times/sec). Eye movement mechanisms - part of a complex attention mechanism including head movements, variable size attention window. Stimulus based attention shifting - towards movement or preattentive areas of strong patterns of color, contrast, intensity. Visual information sent to two systems, Encodes spatial properties (location, size, orientation) Encodes object properties (shape, color, texture) ITCS 6010/8010: Information Visualization 31 Information Visualization ITCS 6010/8010: Information Visualization 32 Information Visualization

9 Level 3: Foveola Receptors Density of cones in the foveola is about 27 times greater than in the periphery Information density per neuron in the foveola is about 200 times greater. Notes: The human vision system maintains a parallel surveillance over the entire visual field. Constant movement of the foveola position, sampling to build up precepts of high information content The visual system knits togethera an illusion of continuity from a succession of saccades, extracting information from high information content areas (sharp corners). Very different from a photograph formation process. Example: Car driver perceiving traffic. ITCS 6010/8010: Information Visualization 33 Information Visualization ITCS 6010/8010: Information Visualization 34 Information Visualization Visual Information Processing Controlled Processing Uses mainly the fovea, eg., reading. Processing is detailed, serial, low capacity, conscious. Automatic Processing Information processed non-foveally. Processing is superficial, parallel, unconscious. Exploiting Visual Perception Exploit human vision processing to design effective visualizations Coding techniques to aid search and pattern recognition should use features/attributes that promotes automatic processing, eg., color, size. Interaction among visual codings - produces patterns, clusters the eye can detect automatically (gestalt principles). Focus+Periphery partition of human visual system can be exploited during interaction, by providing more optimal ways to locate objects and visual indexes to more detailed information. Relationships between nodes or dimensions may be represented, and can form an external working memory, aiding cognition. ITCS 6010/8010: Information Visualization 35 Information Visualization ITCS 6010/8010: Information Visualization 36 Information Visualization

10 Visual Structure Representation Spatial Substrate Visual structures are composed from a fairly limited set: Spatial Substrate (spatial dimensions) Marks Graphical Properties Most visualizations are made from this limited set. Perceptually the most dominant dimension The most important variables are mapped into the spatial dimension (Example: Challenger O ring visualization (Tufte) ) Described in terms of axes and their properties Axes may be Nominal, Ordinal, Quantitative. ITCS 6010/8010: Information Visualization 37 Information Visualization ITCS 6010/8010: Information Visualization 38 Information Visualization Scatter Plots Example Spatial Mappings Mapping two variables to the horizontal and vertical axes and plotting them using variety of glyphs/metaphors May extend to third dimension (data within a cube) - does not scale well, as well as occlusion problems. Other variables may be manipulated by other widgets to limit and control the information displayed (sliders, for instance) Composition Scatterplots (2D, 3D) Alignment Scalable Spatial Mappings Have parallel (aligned) axes to match up properties along a common variable (for eg., time) ITCS 6010/8010: Information Visualization 39 Information Visualization ITCS 6010/8010: Information Visualization 40 Information Visualization

11 Scalable Spatial Mappings (contd) Folding Continuation of an axis in an orthogonal dimension Example: Seesoft (software visualization system) Recursion Scalable Spatial Mappings (contd) Recursive subdivision of space, with interactive zoom Sub-regions are coded (size, color, texture) Example: Pad++ (File/directory organization, Bederson/Hollan 1994) ITCS 6010/8010: Information Visualization 41 Information Visualization ITCS 6010/8010: Information Visualization 42 Information Visualization Scalable Spatial Mappings (contd) Overloading Repeated use of the same space Example : Worlds within Worlds (Feiner/Beshers 1990) Marks (Attributes) Graphics primitives Four elementary types, Points, Lines, Areas, Volumes ITCS 6010/8010: Information Visualization 43 Information Visualization ITCS 6010/8010: Information Visualization 44 Information Visualization

12 Connection and Enclosure (contd) Connection and Enclosure Treemaps Point and Line marks can be employed to represent Graphs and Hierarchies These are important for representing relationships and containers Implicitly may also represent gestalt principles, such as proximity (clustering is implicit in a tree representation) Example representations: Treemaps, Hyperbolic Trees. ITCS 6010/8010: Information Visualization 45 Information Visualization ITCS 6010/8010: Information Visualization 46 Information Visualization Connection and Enclosure (contd) Hyperbolic Tree View Transformations A crucial component of information visualization Interactively modify, augment visualization structures Focus+Context, Drill-down, Roll-Up Can be classifed as Location Probes, Viewpoint controls, Distortions ITCS 6010/8010: Information Visualization 47 Information Visualization ITCS 6010/8010: Information Visualization 48 Information Visualization

13 Location Probes Use location in visualizations to reveal additional information Examples: Detail-on-demand popups, Brushing Slicing planes probing volume data, Region of Interest(ROI) extraction, clipping Viewpoint Controls Affine transforms to rotate, pan, zoom into visualizations Clipping Overview+Detail - use two views together, eg. Information Mural (Jarding/Stasko 1995) ITCS 6010/8010: Information Visualization 49 Information Visualization ITCS 6010/8010: Information Visualization 50 Information Visualization Distortion A visual transformation to accommodate focus+context views. Overview and detail are combined into a single view, providing context to the detail view. Real-time (or near real-time) interaction is crucial. Examples: Bifocal lens (Spence/Apperley 1982), Perspective Wall (Mackinlay 1991), Table Lens (Rao 1994), Hyperbolic Tree (Lamping 1996). ITCS 6010/8010: Information Visualization 51 Information Visualization