Information Visualization
CSC540, Fall 2007
Nancy Green
1. What is Information Visualization
• Visualization (Viz): using computersupported interactive graphics to “amplify
cognition”
• Graphics can help user to see hidden
patterns in data or solve problems
• Scientific viz: data about physical world
(pictures, diagrams)
• Info viz: abstract, non-pictorial data (e.g.
relational database, results of web search)
History of Info Viz
• Before computers, static data graphics
– E.g. line graphs, bar graphs, scatter plots
• Now some user interfaces
– Use data graphics to present data
– Some even automatically design data
graphics (e.g. CMU SAGE & VISAGE)
– Some allow users to interact with graphics
(e.g. dynamic query sliders to let user filter
data from display: HomeFinder HCIL TR 92-01:
http://www.cs.umd.edu/hcil/spotfire/ )
How Info Viz “amplifies cognition” (1
st)
• Enables user to detect patterns visually,
e.g. correlation between number of
bedrooms and sale price of houses
price
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bedrooms
4
How Info Viz amplifies cognition (2nd)
• Reduces search for information
– Spatial indexing: e.g. with weather map you
can quickly locate temperature info by
geographical location
– Data density: e.g. weather map can show
temperature, rainfall amount, air pressure,
etc. in small space
How Info Viz amplifies cognition (3rd)
• Reduces load on user’s memory and
cognitive processing by allowing user to
solve problems using simple perception;
example: If A=13, B=99, C=130, which is
greater, (A+B) or C?
• See next slide …
How Info Viz amplifies cognition (3rd)
• Reduces load on user’s memory and
cognitive processing by allowing user to
solve problems using simple perception;
example: If A=13, B=99, C=130, which is
greater, (A+B) or C?
A
B
Just compare length of A+B (top bar)
to
C
length of C (bottom bar)
How Info Viz amplifies cognition
(4th & 5th)
• Enables user to monitor many events
perceptually
– Ex. 1,000 node network of blue dots; change
to flashing red means node is down!
• Enables user to manipulate parameters
directly
– Ex. Dynamic query sliders
2. How to Design Effective Info Viz
Consider each:
• user’s tasks and goals
• human perception strengths/weaknesses
• guidelines for effective design of static
data viz
• interactive viz operations
2.1 Role of User Tasks & Goals
“Text or visual better?” depends on tasks/goals:
• If user’s goal to get friend’s phone number
– Phone book (alphabetical list): good
– On-line city map with rollover to get occupant’s names
and phone numbers: not as good if you don’t know
where she lives!
• If user’s goal to get phone numbers of everyone
living on same block (4 streets)
– Phone book (alphabetical list): awful
– City map described above: really good!
Tables vs. Graphs
Tables: good for looking up exact numerical values (if table is
properly indexed)
Graphs: better for seeing patterns, trends, etc. See
http://www.bls.gov/cpi/home.htm
Role of User Tasks & Goals
“Which viz is better?”
depends on
tasks/goals:
Simple example: task to
compare two amounts
– Unlabeled pie chart:
not good (human
ability to discriminate
angles not good)
– Bar graph: better for
this task
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Role of User Tasks & Goals (2)
Example of complex task: Find flight from
Pittsburgh to Mexico City with layover in
Albuquerque at least 1 hour around 3 pm
• Casner’s viz: very efficient for solving this
kind of problem (See my reproduction of his
1991 graphic on next slide)
• Typical map showing flight connections: not
very efficient for this complex task (but good
for task of finding shortest route between 2
cities)
Based on Casner (1991)
2.2 Role of Visual Perception in
Design
• Two types of visual perception
– Controlled: slow, serial, conscious control
(e.g. reading)
– Automatic (“preattentive”): fast, parallel, not
under conscious control (e.g. pattern “jumps
out” from display)
• Design of viz can take advantage of
preattentive visual perception!
Automatic/Preattentive Vision
• See next two slides for pictures illustrating
good preattentive features:
– Color
– Shape
• And illustrating problems with conjunctions
of features
– Color and shape
Preattentive Vision: Color
– Which box contains the red circle?
Preattentive Vision: Shape
– Which box contains the blue circle?
Conjunction of Features
– Two or more features cannot be detected with
preattentive vision; e.g., find the red circle:
2.3 Static Data Viz Design
• Guidelines (some from pre-computer era graphic
designers and statisticians!) based on:
• Types of data to be displayed:
– Nominal: non-numeric, non-ordered, e.g. names of movies
– Ordinal: ordered, e.g., <light, moderate, heavy>
– Quantitative: numeric, e.g., number of children, age, salary
• Independent or dependent variable:
– Value of dependent variable (e.g. max temperature) depends on
corresponding value of independent variable (e.g. hour of day)
– Convention to show independent variable on x-axis
Types of Graphs: Pie Graph
• Good for rough comparison of part to
whole, but not for comparing two parts to
each other, because comparing angles is
difficult perceptual operation for user
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Types of Graphs: Bar graph
• Good for comparing
relative values
• x-axis can be nominal,
ordinal, or quantitative
• Choice of ordering on
axes, stacking, and
grouping can be used to
facilitate different tasks
• Above grouping good for
task: find quarter/region
with most sales
• This stack design better
for task: find worst
quarter for all regions
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East
West
North
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Types of Graphs: Line graph
• Good for seeing trends
• X-axis data must be quantitative,
preferably continuous
• If task to compare multiple lines,
e.g. actual vs. estimated, user
must be able to discriminate
lines
• Subtype: Time series - showing
change over time
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Types of Graphs: Scatter Plot
• Good for showing relation between two
variables
• Can present multiple data sets using
different marks (e.g. red and yellow below)
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Static Data Design Don’ts
• Warning: some popular
graph-drawing programs
like Excel do not prevent
users from violating
principles of good graph
design
• Examples: superfluous
use of 3D, distracting
effects
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More Complex Static Data Viz
Techniques
• Superimposing data on maps – See Minard’s famous
viz of Napolean’s army: http://www.cs.cmu.edu/~sage/sagewalk1.html
• Showing multiple data sets on same graph:
http://www.jfree.org/jfreechart/images/PriceVolumeDemo1.png
• Or on multiple graphs with aligned axes:
http://www.cs.cmu.edu/~sage/sagewalk5.html
• Using encoding of mark to show different values (e.g.
size of city “dot” on map to convey size)
• Use of labeling, annotations (e.g. arrow), or other
visual attributes (e.g. different color of bar) to
highlight salient data values
2.4 Interactive Viz User Operations
• Overview: Get overview of all data (e.g. map
showing all houses for sale)
• Zoom: zoom in on item of interest (e.g. focus
map on area around UNCG)
• Filter: filter out uninteresting items (e.g. use
slider to remove houses with price over $200K)
–
see HomeFinder HCIL TR 92-01: http://www.cs.umd.edu/hcil/spotfire/
–
HCIL Video Reports 1993 - Dynamaps: http://www.cs.umd.edu/hcil/pubs/video93.shtml
• Drill-down: get details (e.g. rollover to get square
feet and number of bedrooms)
Interactive Viz User Operations (cont.)
• Relate: view relationships between data in
two data graphs by “brushing/painting”
items in one graphic with a color, then see
corresponding items in another graph turn
same color
UNCG
$
Age of house
Map of campus neighborhoods
Interactive Viz User Operations (cont.)
• History: undo or replay steps of interaction
• Extract: copy data or drag to another application
• Aggregation: ask for combined data in display
(ex. Show total for years instead of each quarter
of each year)
• Decomposition: ask to show breakdown of
aggregate
For examples: see Visage demo
http://www.cs.cmu.edu/~sage/visagedd.html )