Information Visualization: Amplifying Cognition with Visual Representations

ICS 280: Advanced Topics in Information Visualization Professor Alfred Kobsa Overview of Information Visualization II April 12, 2001 Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 1

Definitions (1) • External Cognition • Use of external world to accomplish cognition. • Cognitive artifacts e.g. Post-it notes, bookmarks, wedding ring. • Information design • Design of external representations to amplify cognition. • e.g. Maps. • Data graphics • Use of abstract, nonrepresentational visual representations of data to • amplify cognition. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 2

Definitions (2) • Visualization • Use of computer-based, interactive visual representations of data to • amplify cognition • Scientific visualization • Use of interactive visual representations of scientific data, typically • physically based, to amplify cognition. • Information visualization • Use of interactive visual representation of abstract, nonphysically • based data to amplify cognition. • e.g. Business information. • * large amount of data • * interactivity Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 3

How Visualization Amplifies Cognition (1) • Classic study of Larkin and Simon (1987) • Solving physics problems using diagrams vs using non-diagrammatic • representations. They compared the effort to do search, recognition, • and inference. • (1) By grouping together information that is used together large • amounts of search were avoided. • (2) By using location to group information about a single element, the • need to match symbolic labels was avoided, leading to reductions • in search and working memory. • (3) The visual representation automatically supported a large number of • perceptual inferences that were extremely easy for humans. • e.g. Recognize geometric elements like alternate interior angles Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 4

How Visualization Amplifies Cognition (2) • (1) Increased Resources • - Expanded working memory available for solving a problem ° • - Expanded storage of information - quickly accessible massive amounts • of information. • (2) Reduced Search • - Visualizations group information used together, reducing search. • - Representation of large amount of data in a small space. • (3) Enhanced Recognition of Patterns • - Recognition instead of recall (menu based vs command interface) • - Visually organizing data by structural relationships enhances patterns Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 5

How Visualization Amplifies Cognition (3) • (4) Perceptual Inference • - Visual representations make some problems obvious e.g. geometry of • a problem helps to find solutions. • - Visualization can enable complex specialized graphical computations. • (5) Perceptual Monitoring • - Visualization can allow the monitoring of a large number of • potential events if the display is organized so that these stand out by • appearance or motion. • (6) Manipulable medium • - Unlike static diagrams, visualizations can allow exploration of a space • of parameter values and can amplify user operations. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 6

How Visualization Amplifies Cognition (4) • LifeLines • http://www.cs.umd.edu/hcil/lifelines/ • Lifelines provides a general visualization environment for medical • records. It eliminates long lists to scroll, complex searches, endless • menus, etc. A one screen overview of the record using timelines • provides direct access to the data. • For a patient record, medical problems, hospitalization and • medications can be represented as horizontal lines, while icons • represent discrete events such as physician consultations, • progress notes or tests. Line color and thickness can illustrate • relationships or significance. Rescaling tools and filters allow users to • focus on part of the information, revealing more details. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 7

How Visualization Amplifies Cognition (5) • Inxight Tree Studio • http://www.inxight.com/index.html • Inxight Tree Studio allows web site builders to create visual • site maps called Star Trees. • The users can see at a glance all the organization of the web site. • Search features that improve the navigation Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 9

How Visualization Amplifies Cognition (6) • On-line Library of Information Visualization Environments • http://www.otal.umd.edu/Olive/ • List of Information Visualization projects • http://www2.iicm.edu/ivis/ivis/node1.htm Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 10

Mapping Data to Visual Form (1) Raw Data Data Data Tables Visual Form Data Transformation Visual Structures Visual Mappings Views Task View Transformations Human Interaction Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 11

Mapping Data to Visual Form (2) • Raw Data • - Data transformations map raw data, that is, data in some idiosyncratic • form, into Data Tables • - Numbers, texts, names, etc -> without structure • -> difficult to map to visual forms Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 12

Mapping Data to Visual Form (4) • Data Tables • - This tables are based on relational descriptions of data extended • through the use of metadata Casei represents a unique object and the values represent characteristics Variables are divided into input and outputs. The columns represent cases, sets of values for each of the variables Input variables uniquely determine the output variables Metadata are the labels for the rows and columns Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 14

Mapping Data to Visual Form (4) • Data Tables • - This tables are based on relational descriptions of data extended • through the use of metadata. • - Dimensionality is used to refer to the number of input variables, • the number of output variables, both together, or even the number of • spatial dimension in the data. • - Variable types can be nominal, ordinal or quantitative. • - Errors or missing values and statistical calculations can add additional • information. Data tables often contain derived value or structure (e.g. • derive the mean [value], sort the variables [structure]). • - Data transformation can help to discovered patterns. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 15

Mapping Data to Visual Form (6) • Visual Structures • - Visual Mappings transform data tables into visual structures. • - The structures combine spatial substrates, marks and graphical properties. • - A mapping is said to be expressive if all and only the data in the Data • Table are also represented in the Visual Structure. • - A mapping is said to be more effective if it is faster to interpret, can • convey more distinctions, or leads to fewer errors than some other • mapping (e.g. sine wave) Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 17

Mapping Data to Visual Form (6) • Gestalt laws - principles of organization • - There can be interaction among the visual coding of information. • - Part of the point of coding information visually is to produce patterns • that the eye detects from ensembles of components. Gestalt principles • Pragnanz The tendency of objects near one another to be grouped together into a perceptual unit. Elements are more likely to form groups if the groups appear familiar or meaningful. Neighboring elements are grouped together when they are potentially connected by straight or smoothly curving lines. If several stimuli are presented together, there is a tendency to see the form in such a way that the similar items are grouped together. The tendency to unite contours that are very close to each other. Every stimulus pattern is seen in such a way that the resulting structure is as simple as possible. Elements that are moving in the same direction seem to be grouped together. • Proximity • Similarity • Closure • Good continuation • Common fate • Familiarity Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 18

Mapping Data to Visual Form (8) • Views • - View Transformations create Views of the Visual Structures by specifying • graphical parameters such as position, scaling, and clipping. • - The user can restrict the view to certain data ranges. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 20

Mapping Data to Visual Form (8) • Views • - Since spatial position is such a good encoding several techniques have • been developed to increase the amount of information that can be • encoded with it. • Composition is the orthogonal placement of axes, creating a 2D metric • space. • Alignment is the repetition of an axis at a different position in the space. • Folding is the continuation of an axis in an orthogonal dimension. • Recursion is the repeated subdivision of space. • Overloading is the reuse of the same space for the same Data Table • see-soft Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 21

Mapping Data to Visual Form (8) • View Transformation • - View transformations interactively modify and augment Visual • Structures to turn static presentations into visualization by establishing • graphical parameters to create Views of Visual Structures. • Location probes • Location proves are view transformations that use location in a visual • structure to reveal additional Data Table Information. (filmfinder) • Viewpoint Controls • Use affine transformations to zoom, pan, and clip the viewpoint. Víctor González (vmgyg@ics.uci.edu) ICS 280: Information Visualization 22

Multiplication 186 x 18 --------- 1488 186 _______ 3348 back

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Information Visualization: Amplifying Cognition with Visual Representations