Information Visualization Part 1Dr. Cindy Corritore

Creighton University

ITM 734Fall 2005

Corritore, 2005

principles of good graphics (Tufte)

• data graphics should draw viewers attention to the substance and meaning of the data, not to something else– goal: help user reason about the data– relative rather than absolute judgements

Principle 1: above all else, always show the data!

Corritore, 2005

Corritore, 2005

principles of good graphics

• chartjunk - non-data ink, decoration, over-redundancy– moire vibration - appearance of movement– grid - remove or mute – unnecessary 3-D

Principle 2: remove chart junk

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Corritore, 2005

Corritore, 2005

principles of good graphics

graphic content consists of:

data ink and non-data ink– data ink is non-erasable core of graphic– text can be data ink– get rid of the rest as much as possible

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principles of good graphics

Data-Ink Ratio:

data ink / total ink used

Principle 3: maximize the data-ink ratio, within reason (erase non-data ink as much as possible)

Corritore, 2005

Corritore, 2005

Corritore, 2005

Corritore, 2005

principles of good graphics

• redundancy can go too far– bilateral symmetry - can reduce

• have double redundancy - people just process first half anyways, then check to see other half is the same

Principle 4: erase redundant data ink, within reason

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What can be erased (redundant)?

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Word ‘Year’ ’19’Data labels on left or in columnsNo color or no bordersGrid lines

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principles of good graphics

• proximity principle - integrate text and graphics– but be careful …

Principle 5: integrate text and graphics, when possible.

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We don’t estimate volume and area well – back barrel is much larger than actual 30% growth.

Corritore, 2005This is much better -

Corritore, 2005

principles of good graphics

• know a problem if you have to talk yourself through it“let’s see, if it is yellow, it is …”

• often involve color as we don’t give visual ordering to colors

• use varying shades of gray - see order better

Principle 6: keep it simple and understandable to audience

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Two charts of the same data (linguistic ability of Canadians correlated with primary language)

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overall

• focus on the data, not the chart elements

• emphasize the important (not the unimportant)!

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Corritore, 2005

problem ….

Everyone spoke of an information overload,

but what there was in fact

was a non-information overload.

Richard Saul Wurman,

What-If, Could-Be (Philadelphia, 1976)

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overview• increasingly common to actually have all of the

data potentially available– how to map and use it becomes harder and harder

• challenges: world of the computer and data and world of the human– bridge between the intuitive, creative, experience

and the digital, analytical

Solution: Involve the user!

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challenges

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challenge 1

1. growing volume of data with declining information content– provision of data ever cheaper and available– our ability to consume information largely

unchanged

• Key Issues: exploring, navigation, browsing, immersion/involvement of human and their perceptional apparatus

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challenge 1

• interactive visualization interface for exploration of network fault data (network alarm data)– experienced network

administrator looks for trends/patterns

– interactive with filters

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challenge 1

• large information spaces

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challenge 2

2. convert appropriate data to relevant data: analysis and interpretation– summarize and compress without signif. loss

of content– complex data analysis tools and models for

analysis hard to use– goal: human involvement in processing and

analysis of data• experience and intuition

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challenge 2

• visual interface to model that assess customer perception of phone connections– 12 input parameters

specifying circuit– user explores

performance as a func. of any two parameters

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challenge 2

• visual correlation between lightning strikes & network alarms– time series

movie

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challenge 3

• managing abstract problems/intangibles against increasingly short timescales– build a building - can see the progress;

intangibles hard to visualize– better informed decisions– goal: retain overview of abstract problem

while providing for immediate visibility of changes

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challenge 3

• software development– each sphere a

module (diameter - size)

– lines are func. calls

– change requests mapped to rate of spin

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challenge 3

• five releases showing selected metrics– most recent @ top– points modules– as evolves, see

changes in system• perhaps spikes overly

convoluted modules

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challenge 4

• communicate a vision - wide audience and increasingly conceptual– wider, less specialist audience; mix of

technical, business, customer– hence, must provide a shared experience

picture is worth 1,000 words

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Goal: let human observe, manipulate, search, navigate, explore, filter, discover, understand, and interact with large volumes of data rapidly

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shneiderman

• King of Direct Manipulation– mantra: overview first, zoom and filter, details

on demand

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data types

• 1D – lists, words– http://www.textarc.org/Alice2inWindow.html - Alice in

Wonderland– fisheye – next week

• 2D– map data (gis)– google earth (demo)– smartmoney.com - http://

www.smartmoney.com/maps/?nav=dropTab

Corritore, 2005

data types

• 3D– scientific visualization (molecules, etc)– ThemeView -

http://in-spire.pnl.gov/IN-SPIRE_Help/galaxy.html - shows documents and their relationships

• galaxy view• themeview

– task manager – – Digital library prototype http://

student.ifs.tuwien.ac.at/~andi/libViewer/

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data types

• 3D and file systems

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data types

• multi-dimensional– n-dimensional space – examples?– spotfire

• temporal– time lines (stock markets, health care)

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data types

• temporal– variables over time– metaphors

• River metaphor: Each attribute is mapped to a “current” in the “river”, flowing along the timeline

Current width ~= strength of theme

River width ~= global strength

Color mapping (similar themes – same color family)

Time line

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A company’s patent activity

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extended exploration

Comparing two riversLinking a river to a histogram

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critiqueStrong points:• Intuitive exploration of temporal

changes and relations• Evalutation + improvements• Applicable to general attributes

Weak points:• Limited number of themes / attributes• Interpolated values / outer attributes misleading• No ability to reorder currents• Performance issues

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spiral Example• Spokes (months) and spiral

guide lines (years)• Planar spiral• Distinguishable patterns (rainy

season / 1984)

Chimpanzees Monthly food consumption 1980-1988

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data types

• temporal – – Time Searcher (

http://www.cs.umd.edu/hcil/timesearcher/videos/ts2_HCILsoh2005R.html) – movie

– lifelines - http://www.cs.umd.edu/hcil/lifelines/latestdemo/chi.html

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data types

• trees– hierarchies (file structure)– magnifind– lexusnexus -

http://www.lexis-nexis.com/lncc/hyperbolic/default.htm

– Cop - http://ai.bpa.arizona.edu/COPLINK/demo/Visualization.htm

– Visual Thesauru

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data types

• network – look at these next week

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challenges

• multiple data input• combine visual and text• show relationships• large information spaces – overview then details• collaboration?• navigation must be accurate• all elements must be interactive• new paradigms ……