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Computer Visualization:
Introduction
Spring, 2014
University of Texas – Rio Grande Valley
CSCI 6361
About the Course …
• Welcome and introductions
• Handout - Syllabus and Schedule
Visualization …
• I see what you mean …
– so, visualization can be considered not just a
visual process, but a cognitive (thought) process
as well
• And a very large part of human brain taken
up with visual system
– and that part of the brain is still useful beyond
“simply” getting an image of the world
– … which is in fact pretty complicated
Visual Pathways of Humans
• And a very large
part of human
brain taken up
with visual
system
– and that part of
the brain is still
useful beyond
“simply” getting
an image of the
world
– … which is in fact
pretty
complicated
Overview
•
Visualization – what it is, why use
– Visualization and insight …and what insight is
– Induction and deduction …. Logic of Discovery and Logic of Justification
•
Scientific visualization and information visualization
– The physical and abstract
– … and data visualization, too
•
Data analysis and data types
– The challenges of N-dimensional data visualization
•
Imaging, Computer Graphics and Visualization
– Distinctions among
•
Data exploration and data mining
– Putting the “human in the loop”
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
• 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
Visualization and Insight
• Computing is about insight, not numbers
• Hamming, 1969
• … and a lot of people got it right away
• Likewise, purpose of visualization is insight, not pictures
• Goals of insight:
• Discovery
• Decision making
• Explanation
“Computing is about insight, not numbers”
“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?
“Computing is about insight, not numbers”
• Insights:
–
What state has highest income?, What is relation between education and income?, Any outliers?
A Classic Example
A wealth of information in a single graphic representation
• Napolean’s Russian campaign
– N soldiers, distance, temperature – from Tufte
Visual Knowledge Tools
1,2,3 d is easy, here, visualizing 6 dimensions
• Arrange information to reveal patterns, allow manipulation of
information for finding patterns, Feiner et al., ~1995
And Insight can be Quick …
• Some examples ….
London Subway – Actual
A jumble
•
x
London Subway
Diagrammatic Map providing “practical” order
• x
And, For what it’s worth …
Insight into state names in country music songs
Pie Chart …
(humor)
•
http://infosthetics.com/archives/2008/09/funniest_pie_chart_ever.html
And …
•
http://www.boingboing.net/2006/11/02/hilarious-piechartvi.html
Useless stuff - clutter
• Will see
various design
principles for
visualization
• Here, “3d”
adds nothing
– (at best)
Detrimental useless stuff
What’s the point here?
• USA Today
A Final Example
• 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
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, decision 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., 2d 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
Examples
•
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
• 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…” – Don Norma and others
• 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 …
• I.e., to determine questions, or, hypotheses
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 – both are logics
• Equally “real” and necessary
Mueller, 2003
Deduction vs. Induction
• Deductive logical analysis probably the
more familiar
– Presented in detail since middle school
• Formulate a hypothesis first, then test
hypothesis
– via experiment and accept/reject
– data collection more “targeted” than in
induction (next)
• i.e., only addressing “truth” (actually
falseness) of hypothesis
– only limited data mining opportunities
Mueller, 2003
Deduction vs. Induction
•
Inductive logical analysis part of scientific process,
and reasoning generally,
– but perhaps delineation of elements less familiar
•
Like, where do the hypotheses come from?
– Insight?
•
Make observations first, then draw conclusions
– organized data survey (structured analysis,
visualization) of the raw data provide the basis for the
interpretation process
– interpretation process will produce knowledge that is
being sought
– experience of individual scientist (observer) is crucial
– important: selection of relevant data, collection method,
and analysis method
– data mining is an important knowledge discovery
strategy
– ubiquitious data collection, filtering, classification, and
focusing is crucial
Mueller, 2003
Logic of Discovery
• In “the scientific method”, or, deduction,
– Where do the hypotheses come from?
• Probably familiar with: Logic of justification
– Concerned with deductive reasoning
– Falsification of theories and hypothesis
– “Writing up the experiment”
• Also, Logic of discovery
– Concerned with inductive reasoning
– Just as can specify with some precision the elements of deductive
reasoning, can specify element of inductive reasoning
– “Getting ideas (hypotheses) to test experimentally”
• Visualization does play role in each
– Emphasis here is on induction, as is perhaps the less familiar
About the Data to be Visualized
Some details – or, a listing of data types to be visualized
•
Origin:
– real world data
• measured from real-world objects and processes (sensors, statistics, surveys)
– model data
• computed by machines (numerical simulations, scientific computations)
– design data - edited by humans
•
•
Size: - number of samples and data items (kB, GB, MB, TB)
Type:
– scalar or multi-variate, N-dimensional: number of attributes per data item
(attribute vector)
– scalar or vector (e.g., flow direction)
•
Range and domain:
– qualitative (non-numerical) vs. quantitative (measurements)
•
Value:
– categorical (nominal):
• categories are disjunct, no intrinsic rank (e.g., {yellow, red, green})
– ordinal data:
• data members of ordered sequence of categories (e.g. {tiny, small, large, huge})
Mueller, 2003
Dataset Dimensionality
More about the data
•
Number of variables involved and dimension of
each variable
•
Univariate data:
– a single variable
– visualization can be a simple plot v = f(x)
•
Bivariate data
– two variables
– visualization can be a surface v = f(x, y)
•
Trivariate data – ex., flame simulation
– three variables
– visualization can be volume rendering v = f(x, y, z)
– occlusions become a problem since we must
visualize a 3D dataset on a 2D screen
•
Multivariate or N-D data (for N > 2)
– visualization becomes challenging
Mueller, 2003
Multivariate Data
Again, 1,2,3 d is easy - Practical Example
•
You (a person) can be considered a multi-dimensional data point when it
comes to your statistical properties, examples are:
– annual salary, rent, mortgage, stock revenues and losses, life insurance, credit
card balance
– number of children, pets, cars, computers, telephones, cell phones, kidneys
– money spent on CDs, computer games, eating out, movies, comic books, DVDs
– hours spent surfing the web, sick leaves, vacations, watching TV, making phone
calls
– location of residence (zip code), profession, nationality, family status, age,
interests
•
Large commercial interest to identify and target certain groups of people
•
•
Another example: Categorize all web pages or text documents
The general task is:
– identify the cluster of datapoints that fit a certain metric or set of criteria
•
The general problem is:
– automated (statistical) methods usually fail for large and fuzzy problem spaces
•
Visualization can help:
– but... how does one visualize data in N-space?
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
Shroeder et al., 2002
Contexts of Visualization
•
If data spatio-temporal (up to 3 spatial coordinates and time),
– typically scientific visualization methods used
•
If data higher dimensional (>4), or abstract,
– typically information visualization techniques used
•
Human perceptual system is highly tuned to space-time relations
– This (3-d, or 4 with time) coordinate system understood with little or no
explanation
•
Visualization of abstract data typically requires extensive explanation to
understand what, and its representation, being viewed
•
Still, there is overlap:
– Often, first step in IV visualization process is to project abstract data into the
spatial-temporal domain (3d + time), and use SciViz techniques to view
– Projection process can be quite complex, involving methods of statistical
graphics, data mining, etc., or may just select subset of dimensions to view
Shroeder et al., 2002
Imaging, Computer Graphics,
and Visualization
•
Imaging, or image processing
– Study of 2D pictures, or images
– Includes techniques to transform, extract information from, analyze, and enhance
images
•
Computer graphics
– Process of creating images using a computer
– Includes both 2D and 3D
•
Visualization
– Process of exploring, transforming, and viewing data as images (or other sensory
forms) to gain understanding and insight into data
•
Distinguishing visualization from computer graphics:
– Dimensionality of data is 3D or greater
– Concerned with data transformation, i.e., information is repeatedly created and
modified to enhance extracting meaning from data
– Naturally interactive, human included directly in process of creating,
transforming, and viewing data
Shroeder et al., 2002
Visualization Encompasses
Exploring and Understanding Data
• Visualization process (simplified)
Measured Data
- CT, MRI, ultrasound
- Satellite
- Laser digitizer
- Stocks, financial
Computational Methods
- Finite element
- Finite Difference
- Boundary element
- Numerical analysis
Data
Transform
Map
Display
Shroeder et al., 2002
Data Exploration and Mining
Techniques - The User in the Loop
• View refinement and navigation loop:
– view and navigation control is important for extended and
detailed visual spaces that contain (visually) mapped data
– working memory needs focus+context to perform better
Mueller, 2003
Data Exploration and Mining
Techniques - The User in the Loop
• Problem solving loop
• Visualizations:
– function in a straightforward way as memory extensions
– enable cognitive operations that would otherwise be impossible
– Enables visualization-centered problem-solving loop involves both
computer-based modeling and a cognitive model integrated through a
visualization
– enhance hypothesis generation and testing operations of working memory
Mueller, 2003
Visualization Pipeline:
Mapping Data to Visual Form, 1/3
Raw
Information
Data
Transformations
F
Dataset
Visual
Mappings
User
- Task
F -1
Visual
Form
Views
View
Transformations
Visual
Perception
Interaction
• Chris North (online) on Card, Mackinlay, and Shneiderman
• 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, 2/3
Raw
Information
F
Dataset
Data
Transformations
Visual
Mappings
User
- Task
F -1
Visual
Form
Views
View
Transformations
Visual
Perception
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, 3/3
Raw
Information
Data
Transformations
F
Dataset
Visual
Mappings
User
- Task
F -1
Visual
Form
Views
View
Transformations
Visual
Perception
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
IBM’s Many Eyes
One of many toolkits, e.g., vtk, ds
• Multiple
visualizations
IBM’s Many Eyes
• Life expectancy
vs. health care
costs
•
http://manyeyes.alphaworks.ibm.com/m
anyeyes/visualizations/life-expectancyvs-per-capita-annu
IBM’s Many Eyes
• Visualization types
End …
• .