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 VisualizationShneiderman:

• 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

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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, decsion 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., 2-

d 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 – Exs.

• 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

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…”

• 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

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• Equally “real” and necessary

Mueller, 2003

About Information Visualization(Shneiderman focus)

• 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.

SpacePhysical Data1D, 2D, 3DMultiple Dimensions, >3TreesNetworks

InteractionDynamic QueriesInteractive AnalysisOverview + Detail

Focus + ContextFisheye ViewsBifocal LensDistorted ViewsAlternate Geometry

Data Mapping: TextText in 1DText in 2DText in 3DText in 3D + Time

Higher-Level VisualizationInfoSphereWorkspacesVisual 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 n-dimensional 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

• 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

RawInformation

VisualFormDataset Views

User - Task

DataTransformations

VisualMappings

ViewTransformations

F F -1

Interaction

VisualPerception

Visualization Stages

• 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

RawInformation

VisualFormDataset Views

User - Task

DataTransformations

VisualMappings

ViewTransformations

F F -1

Interaction

VisualPerception

Information Structure

• 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

RawInformation

VisualFormDataset Views

User - Task

DataTransformations

VisualMappings

ViewTransformations

F F -1

Interaction

VisualPerception

Challenges for Info. VisualizationShneiderman

• 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

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