Metamorphic Mappings: The Art of Visualization

1
Metamorphic Mappings The Art of Visualization
Donna Cox

• Presented by James Slentz
• Jesse Bennett
• Michael Ha

2
Overview

• Metaphor Theory and Cultural Contingency
• Data-Viz Mapping Numbers into Pictures
• Alternative Mappings and Postcolonialism
• Varieties of Visualization Experiences Case
  Studies

3
Introduction

• Visualization process of making the invisible
  visible
• Data visualization (Data-Viz) process of
  transforming numerical data into a visual model.
• Arguments that there is a direct relationship
  between data-viz and the cognitive, creative
  mapping process discussed in metaphor theory will
  be presented.

4

• Data-viz maps numbers into pictures, resulting in
  visaphors.
• Visaphors digital visual metaphors.
• Can be interactive software or animations.
• Can be displayed on TVs, printed media, movie
  theatres and also as an alternative, as we will
  discuss, virtual reality.

5
Metaphor Theory and Cultural Contingency

• What is a Metaphor?
• Involves trying to understanding one domain of
  information in terms of another domain.
• Ex Man is a wolf This is a metaphor that
  tries to convey a new understanding that a man
  has the characteristics as a wolf (voracious,
  predator, gathers in packs).
• Of course only some of the characteristics of a
  wolf will be cognitively mapped onto man, while
  the others ignored (four legged, furry)
• Man (target domain) is a wolf (source domain)
• Man ? Wolf (voracious, predator, gathers in packs)

6
Metaphor Theory and Cultural Contingency

• Each domain constitutes a system of beliefs, also
  called a concept network (CN).
• Ex man constitutes a concept network of
  ideas, beliefs and assumptions. Likewise, wolf
  constitutes a concept network of beliefs, facts
  and folklore.

7
Metaphor Theory and Cultural Contingency

• Conventional Metaphors
• Those that are used commonly and are most
  familiar.
• Created and evolved from our physical embodied
  experiences.
• Known so well that we can instantly interpret
  their meaning.
• Ex time is money We understand it takes
  time to make money and conceptualize time as
  being spent, saved or wasted.

8
Metaphor Theory and Cultural Contingency

• Conventional metaphors
• We interpret them as an everyday part of speech.

• Influences how we conceptualize and behave.
• Ex argument is war tells us how we think
  about arguing. We defend, strategize,
  attack, and defeat arguments.

9
Metaphor Theory and Cultural Contingency

• Visual metaphors
• Ex 1 an ad shows a person with earphones that
  look like bricks. Its implying that most
  earphones are heavy and the product advertised is
  light.
• We dont associate characteristics such as the
  shape of a brick to earphones, but rather
  heavy, hard and uncomfortable.
• CN earphones (target domain) ? CN bricks (source
  domain)
• Earphones ? bricks (heavy, hard, uncomfortable)

10
Metaphor Theory and Cultural Contingency

• Visual metaphors
• Ex 2 beer in an champagne bucket with ice. This
  is mapping champagne attributes onto the beer
  brand (some of you would know which brand).
• CN about beer (target domain) ? CN about
  champagne
• Beer product ? champagne (high class and quality,
  special, refined taste)

11
Metaphor Theory and Cultural Contingency

• The following list describes data-viz and how it
  relates to metaphor theory (summary of what we
  just discussed).
• Visaphor has 2 parts target source domain
• These parts represent represent a CN.
• CN beliefs, concepts, symbols, cultural biases,
  assumptions, other metaphors.
• Chars from source dom. are mapped onto target
  dom.
• No one-to-one, some get mapped, others dont.
• We no longer recognize the metaphor of some
  visaphors because they are embedded in culture
  conventional metaphors.

12
Data-VizMapping Numbers into Pictures

• Dealing with Data
• What Is It and Where Does It Come From
• Observational
• Computational

13
Observational

• Instruments or Sensor Data
• Telescopes
• Computed-axial tomography (CAT)
• Collected Data
• Census statistics
• Weather statistics

14
Computational

• Mathematical models
• Approximation methods
• The concept of raw data is misleading because
  it suggests the numbers offer clean, pure
  immediacy.

15
Raw Data

• All data are mediated and filtered
• Scientific data sets are complex and large
• Supercomputers generate
• Multiple-terabyte multidimensional data sets
• No one-to-one mapping to digital screen space
• Challenges integration of data from many
  different sources

16
Transformation into Visual Models

• Data-viz
• A way of organizing the incoming flood of
  quantitative information
• Quantity
• An important concept we use daily

17
Quantity

• What proportion of people die at our age?
• How many calories we consume?
• How much gas costs?
• How much alcohol is in our drink?
• How many hours a week do we study?

18
Quantity

• Motivation for understanding information in
  visual form goes beyond academic inquiry
• The graphical display of quantitative information
  is important to the culture and the individual.
• Visaphors permeate our visual culture and
  influence people

19
Data-viz

• Provides insight by transforming numerical
  information into a visual model
• More specifically, scientific visualization is
  the process of transforming
• A system of numbers
• Mathematical and scientific models
• Observations
• Statistics
• Assumptions
• Instrument recordings
• Into animated and interactive visuals rendered
  through two and three-dimensional computer
  graphics.

20
Rendering

• Defined as the process of creating the onscreen
  digital appearance
• Data-viz is a mapping process

21
Mapping Process

• Visual model Data
  attributes
• The data are the source domain concept networks
• The target and source domains are concept
  networks
• Creating visaphors requires the cognitive mapping
  process described in the preceding eleven-point
  framework
• target domain (concept network) source
  domain (concept network)
• visual model (target concept network)
  data (source concept network)

22
The Science of Visualization

• An approach based on the analysis of human visual
  and perceptual systems
• Researchers have developed a system of guidelines
  or principles that promises to enable people to
  understand more visual information.

23
The Science of Visualization

• This system includes
• What colors to use
• How shape forms present information
• What affects human perception of visual
  information

24
The Science of Visualization

• However, visualization is more than perception
  it is also a process of viisual and cognitive
  interpretation.
• People see, use, and interpret images according
  to their
• Experience
• Cultural understandings
• Habits
• Discipline-specific preferences

25
The Art of Visualization

• Consider what must take place at a higher
  cognitive level when addressing the most
  important question in making a visaphor how can
  the numbers and correlated facts be designed and
  transformed into a visual model that makes sense?

26
The Art of Visualization

• The creative translation of data into visual
  representations.
• Relates to the use of signs
• Involves the invention of symbolic icons or
  visual metaphors that are directly bound to data.

27
The Art of Visualization

• Simple example
• Binding data to a visual element
• The three numbers below are being maped to the
  colors red, green, and blue.
• Red, green, blue 1, 2, 3

28
The Art of Visualization

• Usually the data are large and complex
• Usually beyond simple mapping
• One technique is the use of glyphs

29
Glyphs

• Data-driven symbolic, iconic, graphical objects
  that are bound to the data with attributes such
  as
• Shape
• Color
• Size
• Position
• Orientation

30
Glyphs

• Useful for showing features of the data
• A feature is defined as anything interesting in
  the data
• Glyphs are effective visual objects and have
  become an essential part of many visualization
  environments.

31
The Art of Visualization

• The next three simulations exemplify the use of
  glyphs in understanding the turbulent and
  complicated airflow of tornadoes.
• Visual model Volume of data (several
  million numbers)
• Glyph Subset of the data

32
The Art of Visualization
http//www.psc.edu/research/graphics/gallery/gel
_part_adv.mpg
33
The Art of Visualization
http//www.psc.edu/research/graphics/gallery/case4
100_vol_iso.mpg
34
The Art of Visualization
http//www.psc.edu/research/graphics/gallery/case4
100_ribbons.mpg
35
The Art of Visualization

• Glyph position-orientation Flow of
  air traced in space-time
• Colored paper-like glyphs Flow of
  air around tornado
• Not all of the data can possibly be shown at once

36
The Art of Visualization

• Aesthetic and other editorial decisions are part
  of the mapping process.
• For example, selections of the
• Transparency
• Color
• Camera choreography
• Glyph shape
• Shadows
• Lighting
• All of these are aesthetic decisions

37
The Art of Visualization

• Data-viz requires editorial decisions in both the
  mathematical and graphic presentation of data.
• Interactive techniques have been developed to
  show as much of the multi-dimensions as possible.
• However, a one-to-one mapping is never possible
  in complex, large data sets.
• We simply cannot see all of the variables at once.

38
The Art of Visualization

• Must remember that the visaphor is only a visual
  model
• The target visaphor and source data are concept
  networks that have inherent
• Implications
• Beliefs
• Assumptions
• Approximations
• Aesthetic decisions
• Adaptation of other conventional visaphors such
  as glyphs
• People learn how to read the image from
  familiarity with the conceptual network.
• The visaphor is understood in terms of the data,
  but information is lost

39
The Art of Visualization

• Sometimes visaphors can be biased.
• Next is an example of a popular visualization
  that displays cultural bias.

40
The Art of Visualization

• One of the first visualizations of the Internet
  was very America-centric.

41
The Art of Visualization

• In this example, the partial boundary around the
  United States is skirted by a 300-foot virtual
  cliff made possible through 3D computer graphics
• It drops into blackness without Mexico, Canada,
  or water.

42
The Art of Visualization

• Another visualization shows the conventional
  earth map with the backbone (white) and client
  networks.

43
The Art of Visualization

• When the network was first being built
  technologists called the primary connections the
  backbone.
• The white backbone is itself metaphorical,
  connoting human physical attributes the
  structure that holds up and connects other things.

44
The Art of Visualization

• Finally, visaphors can be measured creatively and
  aesthetically by their position along the
  metaphoric-content continuum.
• The IntelliBadge project is an example of
  visaphors that span the metaphoric-content
  contuum.

45
The Art of Visualization

• The visaphor is considered a literal translation
  of data
• In contrast, the garden iconic representation is
  a figurative, novel visaphor.
• Visual devices such as charts, graphs, and maps
  were once novel visaphors too.

46
Alternative Mappings

• Geographic maps are excellent examples of how
  literal, conventional visual metaphors have
  developed into coherent and consistent systems.
• Their novel origins have been lost over time due
  to familiarity and cultural accommodations.

47
The Milky Way
48
Alternative Mappings

• Contemporary astronomy and astrophysical maps of
  the universe fit into this idea that maps have
  evolved from conventions.
• The Milky Way model has been developed from
• Star catalogs
• Perspective projection
• Research data
• Yet telescopic images are mediated approximations
  and have inherent error ranges.

49
Alternative Mappings

• Most visaphors fail to show these approximations.
• These visual models help us understand one domain
  of information in terms of another, but these are
  not one-to-one mappings.
• Information is often edited or lost.
• Visaphors are aids, but taking the metaphoric
  relationships too literally undermines our
  creative possibilities.

50
Alternative Mappings

• That being said, visaphors cannot be arbitrary.
• They have to work in a physical dimension or
  people will not use them.
• We need to recognize data-viz as a culturally
  contingent process.
• The process of data-viz is metaphorical in
  nature, and recognition of this will lead to more
  complete and creative models of our understanding
  of the universe.

51
The Many Varieties of Visualization Experiences

• The following are continuous visualization
  projects by Donna Cox since 1983

52
Compulages

• Computer Collages and Algorithmic Art
• Donna used mathematical and algorithmic specifics
  to warp and color digital values onto pixels
• Use of 2D and 3D mapping algorithms and an RGB
  editor called ICARE that uses trig functions to
  control Red, Green and Blue values
• Cox coined the term compulages in 1983

53
Renaissance Teams

• Renaissance teams teams of specialists
  (artists, designers, etc..) who work together to
  solve problems involving the visualization of
  data
• Cox worked in one of these scientific
  collaborations in the making of the 1994 IMAX
  film Cosmic Voyage

54
Making of Cosmic Voyage

• The movie examines the size of the universe.
  Everything from the smallest DNA strand to the
  vastness of space.
• We dont have pictures of DNA or our galaxy. In
  order to visually represent them, we have to
  interpret data and give a simulation.

We do not have the technology to view our
galaxy from the outside looking in, so the
use of data and probabilistic methods are
used to develop a visual model.
55

• In order to artistically render images of
  galaxies used in cosmic voyage, advanced
  technologies of supercomputing and visualization
  were used.
• Through scientific collaborations Cox, along
  with other scientists, technologists and artists
  were able to produce the great number of complex
  data-driven visualizations for the movie.
• This collaboration, in creating this film, ended
  up developing new technologies to aid in the
  creation of animations for the film.
• One such technology was the Virtual Director

56
Virtual Director
57
Virtual Director

• Virtual Director is a software framework that
  operates in a CAVE.
• The picture from the previous slide is from
  within a CAVE, which is a room-size, virtual
  environment.
• This system allows the user to control the
  virtual camera through gestural motion capture
  and voice control navigation.

Virtual Director also has remote virtual
collaboration capabilities. This allows
users to collaborate over the internet in linked
CAVE devises so they can interact though
they could be great distances apart.
58
Visaphor Displays

• This section deals with the different mediums the
  visaphors Cox created have been displayed in.
• Two shows Cox worked on (Passport to the
  Universe and The Search for Life) were digital
  shows exhibited in a large digital dome of over 9
  million pixels.

59
Visaphor Displays (cont.)

• Another visaphor exhibit at the Hayden
  Planetarium is the Big Bang.
• Cox helped helped to visualize over 500 gigabytes
  of data to show how the universe grew after the
  big bang.
• This exhibit is displayed such that the audience
  can peer over a railing into a bowl-shaped
  display.

60
IntelliBadge
Intellibadge is an academic experiment that
tracks participants at major public events.
This involves real-time data-viz. This was
first tested at a supercomputing conference
in which the attendees volunteered to carry
RFIDs (radiofrequency identification)
tags. The system provided real-time flow
pattern visualizations. This dynamic multicolored
bar chart shows the distribution of
aggregate professional interests of people
moving through six areas of a convention
center
61
IntelliBadge

• A visaphor called How does your conference
  grow? visualized the conference as a garden.
  Flowers represented different event rooms, flower
  petals grow and shrink with the flow of people
  entering and leaving the rooms, and the rate of
  the flow in and out of rooms was represented by
  aunts, coming to and going from the flowers.

62
Conclusion

• Data-viz has grown in popularity. People are
  interested in viewing visaphors to enhance
  scientific narratives. These visaphors provide
  the public with a scientific view of reality.

63
Criticism and Analysis

• The author did a good job relating a visual
  metaphor to data-viz. In that you are mapping
  characteristics onto a visual metaphor from a
  source domain just like you map data onto a
  visual model from a data source.
• She gave good examples of data-viz and how it is
  used to express novel ideas about data.
• The usage of glyphs was described in a very
  understandable manner, which aids in our
  understanding of the visual metaphor.
• By giving examples of her own personal work, such
  as the film and the creation of glyphs, she adds
  credibility to herself and the paper we analyzed.

64
Web-Links

• http//www.unc.edu/depts/jomc/academics/dri/o11/gr
  owth.html
• http//www.mmm.ucar.edu/review2001/1mainFrame.html
  
• http//www.psc.edu/research/graphics/gallery/torna
  do.html
• http//jan.ucc.nau.edu/jar/LIB/LIB8.html
• http//intellibadge.ncsa.uiuc.edu
• http//www.mmm.ucar.edu/review2001/III.html