Data Visualization
Eileen Quenin
Dr. Gallimore HFE 606
Introduction
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What is Data Visualization?
How does Data Visualization Work?
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History -Jacques Bertin
Image Theory
“Image” a definition
Data Visualization and its use today
What are the benefits of Data Visualization?
Examples of Data Visualization
Conclusion
References
What is Data Visualization
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Data visualization is the process of converting raw data
into easily understood pictures of information that
enable fast and effective decisions.
Early in the 20th-century, gestalt psychologists
observed that when elements were gathered into a
figure, the figure took on a perceptual salience that
exceeded the sum of its parts;
Data -> Easily Understood Pictures
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Jacques Bertin who wrote the classic works of graphical
visualization “Semiology of Graphics” states that the
“transformation from numbers to insight requires two stages.”
Data/Processes
Image
Algorithm
Perception
Insight
Bertin’s 7 Visual Variables
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Seven Visual Variables
position
– form
– orientation
– color
– texture
– value
– size
combined with a visual semantics for linking data attributes to visual
elements
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Image Theory
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Visual Processing occurs in 3 steps.
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1) formation of the retinal image,
2) decomposition of the retinal image information
into an array of specialized representations and
3) reassembly of the information into object
perception.
Image
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Bertin's key concept is the image, from which
the theory derives its name.
Roughly speaking, an image is the
fundamental perceptual unit of a visualization.
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An ideal visualizations will contain only a single
image in order to optimize "efficiency," the speed
with which observer can extract the information
Uses Today
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Data-driven actions are increasingly made without
access to information provided by traditional
information presentation
Information visualization is emerging as an important
fusion of graphics, scientific visualization, database,
and human-computer interaction.
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In Military, Commercial Industries use Data Visualization to
convey complex results as understandable images.
What is Data Visualization
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Data visualization is used in software
applications to provide an intuitive graphical
interface.
It is applied to many areas to enable users to
glean useful information from their data for
faster, more informed decision making.
These areas include: Military, private business
sectors and scientific research.
What are the benefits of Data
Visualization?
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Data visualization allows users see several different
perspectives of the data.
Data visualization makes it possible to interpret vast
amounts of data
Data visualization offers the ability to note exceptions
in the data.
Data visualization allows the user to analyze visual
patterns in the data.
Exploring trends within a database through
visualization by letting analysts navigate through data
and visually orient themselves to the patterns in the
data.
Benefits
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Data visualization can help translate data patterns into
insights, making it a highly effective decision-making
tool.
Data visualization equips users with the ability to see
influences that would otherwise be difficult to find.
With all the data available, it is difficult to find the
nuances that can make a difference.
By simplifying the presentation, Data Visualization can
reduce the time and difficulty it takes to move from data
to decision making.
Data Visualization Examples
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The French engineer, Charles Minard (1781-1870), illustrated the disastrous result of
Napoleon's failed Russian campaign of 1812. The graph shows the size of the army by the
width of the band across the map of the campaign on its outward and return legs, with
temperature on the retreat shown on the line graph at the bottom.
Many consider Minard's original the best statistical graphic ever drawn.
Charles Minard
http://www.math.yorku.ca/SCS/Gallery/minard/march-animated.gif
Charles Minard
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This one unravels time into the 3rd dimension,
superposed above the base map.
Examples
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Color Picker
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http://the-light.com/colclick.html
Search Visualization
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http://www.groxis.com/cgibin/grok/g_prod_adjustments.html
http://www.kartoo.com/
http://www.lexisnexis.com/startree/interactiveview.as
p
Temperature Response to
Increased Atmospheric CO2
Temperature Response to
Increased Atmospheric CO2
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This animation illustrates the changes in surface air temperature that result from increasing
levels of atmospheric carbon dioxide.
These results are derived from two extended computer simulations using a comprehensive
numerical model of the Earth's climate system developed at NOAA's Geophysical Fluid
Dynamics Laboratory.
In these simulations, atmospheric carbon dioxide increases at 1% per year from the modernday level at year 1 of each experiment to double that level in year 70 of the first experiment,
and to quadruple that level at year 140 in the second experiment. After that point, atmospheric
carbon dioxide levels are held constant.
The colored shading represents the difference in the surface air temperature between the
simulations with increased CO2 and a control simulation using the same model with today's
levels of atmospheric CO2. As indicated by the shading, warming is more rapid over the
continental regions than over oceanic regions, and is larger in polar regions than at lower
latitudes. Note that warming trend continues well past the time at which CO2 concentrations
level off. This delayed warming is due to the influence of the world's oceans, which store and
release heat over very long periods of time.
Conclusions
Graphical displays should:
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show the data
induce the viewer to think about the substance rather than about methodology,
graphic design, the technology of graphic production, or something else
avoid distorting what the data have to say
present many numbers in a small space
make large data sets coherent
encourage the eye to compare different pieces of data
reveal the data at several levels of detail, from a broad overview to the fine
structure
serve a reasonably clear purpose: description, exploration, tabulation, or
decoration
be closely integrated with the statistical and verbal descriptions of a data set
References
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Cleveland, W. (1984) The elements of graphing data.
Wadsworth: Monterey, Ca.
Green, Marc PhD. (1998) Toward a Perceptual Science of
Multidimensional Data Visualization: Bertin and Beyond.
Marc Green, PhD
Tufte, Edward R. The Visual Display of Quantitative
Information, Graphics Press, Cheshire, Connecticut, 1989.
F.J. Anscombe, "Graphs in Statistical Analysis," American
Statistician, 27 (February 1973), 17-21.
GFDL Gallery;
http://www.gfdl.gov/~jps/GFDL_VG_Gallery.html