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					Information Visualization
    (Shneiderman and Plaisant, Ch. 13)




                CSCI 6361, etc.
    http://wps.aw.com/aw_shneider_dtui_14
                                Overview
•   Introduction
     – Information visualization is about the interface (hci), and it is more …
     – Scientific, data, and information – visualization


•   Shneiderman’s “data type x task taxonomy”
     – And there are others


•   Examples of data types – 1,2,3, n-dimensions, trees, networks

•   Focus + context

•   Shneiderman’s 7 tasks
     – Overview, zoom, filter, details-on-demand, relate, history, extract


•   North’s more detailed account of information visualization
                 Visualization is …
• Visualize:
   – “To form a mental image or vision of …”
   – “To imagine or remember as if actually seeing …”
   – Firmly embedded in language, if you see what I mean

• (Computer-based) Visualization:
   – “The use of computer-supported, interactive, visual
     representations of data to amplify cognition”
       • Cognition is the acquisition or use of knowledge
       • Card, Mackinlay Shneiderman ’98

   – Scientific Visualization: physical

   – Information Visualization: abstract
     Visualization is not New
• Cave guys, prehistory, hunting

• Directions and maps

• Science and graphs
  – e.g, Boyle: p = vt

• … but, computer based visualization is new
  – … and the systematic delineation of the design
    space of (especially information) visualization
    systems is growing nonlinearly
        Visualization and Insight
• “Computing is about insight, not numbers”
   – Richard Hamming, 1969
   – And a lot of people knew that already

• Likewise, purpose of visualization is
  insight, not pictures
   – “An information visualization is a visual user
     interface to information with the goal of
     providing insight.”, (Spence, in North)

• Goals of insight
   – Discovery
   – Explanation
   – Decision making
“Computing is about insight, not numbers”

  • Numbers – states, %college, income:
      State   % college degree   income   State   % college degree   income
“Computing is about insight, not numbers”

  • Insights:
    –   What state has highest income?, What is relation between education and income?, Any outliers?

        State    % college degree    income            State      % college degree    income
“Computing is about insight, not numbers”

  • Insights:
    –   What state has highest income?, What is relation between education and income?, Any outliers?
A Classic Static Graphics Example

 • Napolean’s Russian campaign
   – N soldiers, distance, temperature – from Tufte
A Final Example, Challenger Shuttle

•   Presented to
    decision makers
     – To launch or not
     – Temp in 30’s

•   “Chart junk”

•   Finding form of
    visual
    representation is
    important
     – cf. “Many Eyes”
              A Final Example

• With right visualization, insight (pattern) is obvious
   – Plot o-ring damage vs. temperature
                        Terminology
•   Scientific Visualization
    – Field in computer science that encompasses user interface, data
      representation and processing algorithms, visual representations, and
      other sensory presentation such as sound or touch (McCormick, 1987)

•   Data Visualization
    – More general than scientific visualization, since it implies treatment of
      data sources beyond the sciences and engineering, e.g., financial,
      marketing, numerical data generally
    – Includes application of statistical methods and other standard data
      analysis techniques (Rosenblum, 1994)

•   Information Visualization
    – Concerned typically with more abstract, often semantic, information,
      e.g., hypertext documents, WWW, text documents
    – From Shneiderman:
        • ~ “use of interactive visual representations of abstract data to
          amplify cognition” (Ware, 2008; Card et al., 1999)


                                                                       Shroeder et al., 2002
       Information Visualization
                         Shneiderman:

• Sometimes called visual data mining

• Uses humans visual bandwidth and human perceptual
  system to enable users to:
   – Make discoveries,
   – Form decisions, or
   – Propose explanations about patterns, groups of items, or
     individual items
        Visual Pathways of Humans

•   .
          Why Visualize?
(The domain scientist and the computer scientist)




                                                    Hudson, 2003
                                      Why Visualize?
      (The domain scientist and the computer scientist)

• Why? … for insight
    – As noted, for discovery, decsion making, and explanation
    – Here, will focus on the “scientist” / “computer scientist” collaboration

• Domain Scientist:
        The biologist, geologist, …

    – “I’d rather be in the lab!”

•   Computer Scientist:
    – “I’d rather be developing algorithms!”

• And an interesting place to be is right in the middle …
    – … which is what visualization is about
    – … so, requires knowing about “scientist” (a human) and “computing
      and display” system (which you know a fair amount about already)
                                                                      Hudson, 2003
Why Visualize? Domain Scientist Reply
 • “If Mathematics is the Queen of the Sciences, then
   Computer Graphics is the Royal Interpreter.”

 • Experiments and simulations produce reams of data

    – And science is about understanding, not numbers

 • Vision is highest-bandwidth channel between computer
   and scientist

 • Visualization (visual representations)
    – Puts numbers back into a relevant framework and allows
      understanding of large-scale features, or detailed features

                                                                    Hudson, 2003
Why Visualize? Computer Scientist Reply

 • Fine, CS is a synthetic discipline:
    – “Toolsmiths”

 • “Driving Problem Approach”
    – Forces you to do the hard parts of a problem
    – Acid test for whether your system is useful
    – Teaches you a little about other disciplines

 • It’s a lot of fun to be there when your collaborator
   uses the tool to discover or build something new


                                                     Hudson, 2003
Bringing Multiple Specialties to Bear
 • Interdisciplinary work often leads to synergies

 • Enables attacks on problems that a single discipline
   cannot work on alone, e.g.,
    – Advanced interfaces
       • Physics, Computer Science
    – Physical properties of DNA:
       • Chemistry, Physics
    – Properties and shape of Adenovirus:
       • Gene Therapy, Physics and Computer Science
    – CNT/DNA computing elements:
       • Computer Science, Physics, Chemistry, Biochemistry



                                                              Hudson, 2003
  About (Scientific) Visualization
• “Scientific visualization is not yet a discipline founded on
  well-understood principles. In some cases we have rules
  of thumb, and there are studies that probe the
  capabilities and limitations of specific techniques. For the
  most part,however, it is a collection of ad hoc techniques
  and lovely examples.”
   – Taylor, 2000




                                                          Hudson, 2003
  About (Scientific) Visualization
• “Scientific visualization is not yet a discipline founded on
  well-understood principles. In some cases we have rules
  of thumb, and there are studies that probe the
  capabilities and limitations of specific techniques. For the
  most part,however, it is a collection of ad hoc techniques
  and lovely examples.”
   – Taylor, 2000

• Or maybe that’s wrong …
   – Maybe in fact we (people) know a lot about visualization, e.g., 2-
     d and 3-d graphs, because we have been doing it since, well, the
     cave days

• Either way the systematic delineation of the design
  space of display techniques for computer based
  visualization is early on
                                                                  Hudson, 2003
Scientific Visualization Data – Exs.
 •   Visualization of data computed from physical simulations (on possibly
     powerful computers) - examples




 •   Visualization of data observed from physical phenomena (e.g., clashes of accelerated particles)
Visualization – Main Ideas
        Visualization – Main Ideas
•   Definition:
     – “The use of computer-supported, interactive visual representations of
       data to amplify cognition.”
         • Card, Mackinlay Shneiderman ’98
         • This is among the most widely accepted contemporary working definitions

•   Visuals help us think
     – Provide a frame of reference, a temporary storage area

•   Cognition → Perception

•   Pattern matching

•   External cognition aid
     – Role of external world in thinking and reason
         • Larkin & Simon ’87
         • Card, Mackinlay, Shneiderman ‘98
         “…amplify cognition…”
• “It is things that make us smart…”

• Humans think by interleaving internal mental action with
  perceptual interaction with the world
   – Try 34 x 72 without paper and pencil (or calculator)

• This interleaving is how human intelligence is expanded
   – Within a task (by external aids)
   – Across generations (by passing on techniques)

• External graphic (visual) representations are an
  important class of external aids

• “External cognition”
“… amplifying cognition…” (opt.)

• Don Norman (cognitive scientist):

   – The power of the unaided mind is highly overrated.
     Without external aids, memory, thought, and
     reasoning are all constrained. But human intelligence
     is highly flexible and adaptive, superb at inventing
     procedures and objects that overcome its own limits.
     The real powers come from devising external aids
     that enhance cognitive abilities. How have we
     increased memory, thought, and reasoning? By the
     invention of external aids: It is things that make us
     smart. (Norman, 1993, p. 43)
    When to use Visualization?
• Many other techniques for data analysis
   – Data mining, DB queries, machine learning…

• Visualization most useful in exploratory data
  analysis:

   – Don’t know (exactly) what you’re looking for …
   – Don’t have a priori questions ...
   – Want to know what questions to ask
Data Analysis and Logical Analysis
 •   Data Analysis
     – Data in visualization:
         • From mathematical models or computations
         • From human or machine collection
     – Purpose:
         • All data collected are (should be) linked to a specific relationship or theory
         • Relationships are detected as patterns in the data
              – Maybe call it insight
              – Relationship may either be functional (good) or coincidental (bad)
              – Data analysis and interpretation are functionally subjective


 •   Logical Analysis
     – Applying logic to observations (data) creates conclusions (Aristotle)
     – Conclusions lead to knowledge (at this point data become information)
     – There are two fundamental approaches to generate conclusions:
         • Induction and Deduction
         • Equally “real” and necessary


                                                                                      Mueller, 2003
    About Information Visualization
                               (Shneiderman focus)

•   In part, IV about “user interface”
     – How to create visual representations that convey “meaning” about abstract data


•   Also about the systems that support interactive visual representations

•   Also about the derivation of techniques that convert abstract
    elements to a data representation amenable to manipulation
     – e.g., text to data


•   In fact IV deals with a wide range of elements
     – Data, transformation, interaction, cognition, …


•   Will wrap by looking at North’s (from Card et al.) account
       Data Type x Task Taxonomy
                              Shneiderman
•   There are various
    types of data (to be
    visualized)

•   There are various
    types of tasks that can
    be performed with
    those data

•   So…, for each type of
    data consider
    performing each type
    of task

•   And there are other
    “taxonomies”, e.g.,
    Card, Mackinlay,
    Schneiderman, 1999
         Another “Taxonomy”
                           From Card et al.

Space                                Data Mapping: Text
    Physical Data                        Text in 1D
    1D, 2D, 3D                           Text in 2D
    Multiple Dimensions, >3              Text in 3D
    Trees                                Text in 3D + Time
    Networks
                                     Higher-Level Visualization
Interaction                              InfoSphere
    Dynamic Queries                      Workspaces
    Interactive Analysis                 Visual Objects
    Overview + Detail

Focus + Context
    Fisheye Views
    Bifocal Lens
    Distorted Views
    Alternate Geometry
1D Linear Data
1D Linear Data
1D Linear Data
2D Map Data
2D Map Data
3D World Data
Temporal Data
Temporal Data
Tree Data
Tree Data
               Tree/Hierarchical Data
•   Workspaces
     –   The Information Visualizer: An Information Workspace by G. R. Robertson, S. K. Card,
         J. M. Mackinlay, 1991 CACM
                Hyperbolic Tree
• Tree layout - decreasing area f(d) center

• Interactive systems, e.g., web site
3-d hyperbolic tree
    using Prefuse
    Trees, Networks, and Graphs
•   Connections between
    /among individual entities

•   Most generally, a graph is a
    set edges connected by a
    set of vertices
     – G = V(e)
     – “Most general” data
       structure

•   Graph layout and display
    an area of iv

•   Trees, as data structure,
    occur … a lot
     – E.g., Cone trees
                          Networks
•   “Most general data
    structure”
    – In practice, a
      way to deal with
      n-dimensional
      data
    – Graphs with
      distances not
      necessarily “fit”
      in a 3-space

•   E.g., Semnet
    – Among the first
                  Networks
• E.g., network
  traffic data
                  Networks
• E.g., network
  as hierarchy
Network Data
               N-dimensional Data
•   “Straightforward” 1, 2,
    3 dimensional
    representations
     – E.g., time and
       concrete

•   Can extend to more
    challenging n-
    dimensional
    representations
     – Which is at core of
       visualization
       challenges

•   E.g., Feiner et al.,
    “worlds within worlds”
                    N-dimensional Data
•   Inselberg

•   “Tease apart” elements
    of multidimensional
    description

•   Show each
     – data element value
       (colored lines)
     – on each variable /
       data dimension
       (vertical lines)

•   Can select set of objects
    by dragging cursor
    across
     – Brushing

•   “Classic” automobile
    example at right
               N-dimensional Data
•   Multidimensional Detective, Inselberg
Multidimensional Data
Multidimensional Data
            Navigation Strategies
• Given some overview to provide broad view of
  information space …

• Navigation provides mean to “move about” in space
   – Enabling examination of some in more detail

• Naïve strategy = “detail only”
   – Lacks mechanism for orientation

• Better:
   – Zoom + Pan
   – Overview + Detail
   – Focus + Context
Focus+Context: Fisheye Views, 1
•   Detail + Overview
    – Keep focus, while remaining aware
      of context

•   Fisheye views
    – Physical, of course, also ..
    – A distance function. (based on
      relevance)
    – Given a target item (focus)
    – Less relevant other items are
      dropped from the display
    – Classic cover
        •   New Yorker’s idea of the world
Focus+Context: Fisheye Views, 2
•   Detail + Overview
    – Keep focus while remaining aware of context

•   Fisheye views
    – Physical, of course, also ..
    – A distance function. (based on relevance)
    – Given a target item (focus)
    – Less relevant other items are dropped from
      the display
    – Or, are just physically smaller – distortion
Distortion Techniques, Generally
•   Distort space = Transform space
     – By various transformations

•   “Built-in” overview and detail, and
    landmarks
     – Dynamic zoom

•   Provides focus + context
     – Several examples follow

•   Spatial distortion enables smooth
    variation
                          Focus + Context, 1
•   Fisheye Views
•   Keep focus while remaining aware of the context
•   Fisheye views:
     –   A distance function (based on relevance)
     –   Given a target item (focus)
     –   Less relevant other items are dropped from the display.
•   Demo of Fisheye Menus:
     –   http://www.cs.umd.edu/hcil/fisheyemenu/fisheyemenu-demo.shtml
                    Focus + Context, 2
•   Bifocal Lens
     –   Database navigation: An Office Environment for the Professional by R. Spence and M.
         Apperley
                    Focus + Context, 3
•   Distorted Views
     –   The Table Lens: Merging Graphical and Symbolic Representations in an Interactive
         Focus + Context Visualization for TabularInformation by R. Rao and S. K. Card
     –   A Review and Taxonomy of Distortion Oriented Presentation Techniques by Y. K.
         Leung and M. D. Apperley
                    Focus + Context, 4
•   Distorted Views
     –   Extending Distortion Viewing from 2D to 3D by M. Sheelagh, T. Carpendale, D. J.
         Cowperthwaite, F. David Fracchia

         Magnification and displacement:
                    Focus + Context, 5
•   Alternate Geometry
     –   The Hyperbolic Browser: A Focus + Context
         Technique for Visualizing Large Hierarchies by
         J. Lamping and R. Rao

•   Demo
          Shneiderman’s “7 Tasks”
•   Overview task                       •   Relate task
     – overview of entire collection         – relate items or groups within the
                                               collection
•   Zoom task
     – zoom in on items of interest     •   History task
                                             – keep a history of actions to support
                                               undo, replay, and progressive
•   Filter task –                              refinement
     – filter out uninteresting items

                                        •   Extract task
•   Details-on-demand task                   – allow extraction of sub-collections
     – select an item or group to get          and of the query parameters
       details
                        VxInsight
•   Developed by Sandia Labs to visualize databases
     – Licensable

•   Elements of database can be “anything”
     – For IV “abstract”
     – e.g., document relations, company profiles

•   Example screens show ?grant proposals
     – Video of demo at:
       www.cs.sandia.gov/projects/VxInsight/vx_science.exe
     – Shows interactive capabilities
    VxInsight

•


           vvv
                            VxInsight
•   Shneiderman’s IV
    Interaction paradigm:
     – Overview
     – Zoom
     – Filter
     – Details on demand
        :
     – Browse
     – Search query
        :
     – Relate
     – History
     – Extract
             VxInsight

• Overview
            VxInsight

• Zoom in
              VxInsight

• to detail
                                 Interaction
•   Dynamic Queries
     –   Dynamic Queries for Visual Information Seeking by B. Shneiderman
     –   Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield
         Displays by C. Ahlberg and B. Shneiderman
     –   Data Visualization Sliders by S. G. Eick
     –   Enhanced Dynamic Queries via Movable Filters by K. Fishkin, M. C. Stone
Recall … Information Visualization
• In part IV about “user interface”
   – How to create visual representations that convey data
     about abstract data

• Also about the systems that support interactive
  visual representations

• Also about the derivation of techniques that convert
  abstract elements to a data representation
  amenable to manipulation
   – e.g., text to data

• North’s account (supp. reading) from Card et al.,
  1999
                   Visualization Pipeline:
                    Mapping Data to Visual Form

                                              F                      F -1                               User
          Raw                                        Visual                                            - Task
                                Dataset                                         Views
     Information                                     Form
                                                                                            Visual
                   Data                    Visual                 View                    Perception
              Transformations             Mappings            Transformations
                                                                                  Interaction




• Visualizations:
   – “adjustable mappings from data to visual form to human perceiver”

• Series of data transformations
   – Multiple chained transformations
   – Human adjust the transformation

• Entire pipeline comprises an information visualization
                   Visualization Stages

                                              F                      F -1                               User
          Raw                                        Visual                                            - Task
                                Dataset                                         Views
     Information                                     Form
                                                                                            Visual
                   Data                    Visual                 View                    Perception
              Transformations             Mappings            Transformations
                                                                                  Interaction




•   Data transformations:
    – Map raw data (idiosynchratic form) into data tables (relational descriptions
      including metatags)

•   Visual Mappings:
    – Transform data tables into visual structures that combine spatial substrates,
      marks, and graphical properties

•   View Transformations:
    – Create views of the Visual Structures by specifying graphical parameters
      such as position, scaling, and clipping
                   Information Structure

                                                  F                      F -1                               User
              Raw                                        Visual                                            - Task
                                    Dataset                                         Views
         Information                                     Form
                                                                                                Visual
                       Data                    Visual                 View                    Perception
                  Transformations             Mappings            Transformations
                                                                                      Interaction



•   Visual mapping is starting point for visualization design

•   Includes identifying underlying structure in data, and for display
     –   Tabular structure
     –   Spatial and temporal structure
     –   Trees, networks, and graphs
     –   Text and document collection structure
     –   Combining multiple strategies

•   Impacts how user thinks about problem - Mental model
    Challenges for Info. Visualization
                              Shneiderman

•   Importing and cleaning data

•   Combining visual representations with textual labels

•   Finding related information

•   Viewing large volumes of data

•   Integrating data mining

•   Integrating with analytical reasoning techniques

•   Collaborating with others

•   Achieving universal usability

•   Evaluation
    Challenges for Info. Visualization
•    Combining visual representations with textual labels
    Challenges for Info. Visualization
•   Viewing large volumes of data
    Challenges for Info. Visualization
•   Integrating with
    analytical reasoning
    techniques
      End
• .

				
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