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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
•
.
About Information Visualization
•
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
Physical Data
1D, 2D, 3D
Multiple Dimensions, >3
Trees
Networks
Data Mapping: Text
Text in 1D
Text in 2D
Text in 3D
Text in 3D + Time
Higher-Level Visualization
Interaction
Dynamic Queries
Interactive Analysis
Overview + Detail
Focus + Context
Fisheye Views
Bifocal Lens
Distorted Views
Alternate Geometry
InfoSphere
Workspaces
Visual Objects
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 ndimensional
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
– overview of entire collection
• Zoom task
– zoom in on items of interest
• Filter task –
– filter out uninteresting items
• Details-on-demand task
– select an item or group to get
details
• Relate task
– relate items or groups within the
collection
• History task
– keep a history of actions to support
undo, replay, and progressive
refinement
• Extract task
– allow extraction of sub-collections
and of the query parameters
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
Raw
Information
Data
Transformations
F
Dataset
Visual
Mappings
User
- Task
F -1
Visual
Form
Views
View
Transformations
Visual
Perception
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
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
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
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
• .