ICS 280: Information Visualization

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ICS 280 Advanced Topics in Information
Visualization Professor Alfred Kobsa Overview of
Information Visualization II April 12, 2001
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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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
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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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
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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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.
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• Mapping Data to Visual Form (3)

Raw Data
Data
Data Tables
Visual Form
Data Transformation
Visual Structures
Visual Mappings
Views
Task
View Transformations
Human Interaction
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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
Metadata are the labels for the rows and columns
The columns represent cases, sets of values for
each of the variables
Variables are divided into
input and outputs.
Input variables uniquely determine the
output variables
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• Mapping Data to Visual Form (5)

Raw Data
Data
Data Tables
Visual Form
Data Transformation
Visual Structures
Visual Mappings
Views
Task
View Transformations
Human Interaction
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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)

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.
Every stimulus pattern is seen in such a way
that the resulting structure is as simple as
possible.
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.
Neighboring elements are grouped together when
they are potentially connected by straight or
smoothly curving lines.
Elements are more likely to form groups if the
groups appear familiar or meaningful.
Elements that are moving in the same direction
seem to be grouped together.

• Proximity

• Similarity

• Closure

• Good continuation

• Common fate

• Familiarity

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• Mapping Data to Visual Form (7)

Raw Data
Data
Data Tables
Visual Form
Data Transformation
Visual Structures
Visual Mappings
Views
Task
View Transformations
Human Interaction
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• 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.

ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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• Mapping Data to Visual Form (9)

Raw Data
Data
Data Tables
Visual Form
Data Transformation
Visual Structures
Visual Mappings
Views
Task
View Transformations
Human Interaction
ICS 280 Information Visualization
Víctor González (vmgyg_at_ics.uci.edu)
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Multiplication
186 x 18 ---------
1488 186 _______
3348 back
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