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ITM 734 Fall 2005. Information Visualization Part 1. Dr. Cindy Corritore Creighton University. 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

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information visualization part 1

ITM 734

Fall 2005

Information Visualization Part 1

Dr. Cindy Corritore

Creighton University

principles of good graphics tufte
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

principles of good graphics
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

Corritore, 2005

principles of good graphics1
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

Corritore, 2005

principles of good graphics2
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

principles of good graphics3
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

Corritore, 2005

slide14

Word ‘Year’

’19’

Data labels on left or in columns

No color or no borders

Grid lines

Corritore, 2005

principles of good graphics4
principles of good graphics
  • proximity principle - integrate text and graphics
    • but be careful …

Principle 5: integrate text and graphics, when possible.

Corritore, 2005

slide16

We don’t estimate volume and area well – back barrel is much larger

than actual 30% growth.

Corritore, 2005

slide17

This is much better -

Corritore, 2005

principles of good graphics5
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

Corritore, 2005

slide19

Two charts of the same data (linguistic ability of Canadians

correlated with primary language)

Corritore, 2005

overall
overall
  • focus on the data, not the chart elements
  • emphasize the important (not the unimportant)!

Corritore, 2005

problem
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)

Corritore, 2005

overview
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!

corritore, 734

challenges
challenges

Corritore, 2005

challenge 1
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

Corritore, 2005

challenge 11
challenge 1
  • interactive visualization interface for exploration of network fault data (network alarm data)
    • experienced network administrator looks for trends/patterns
    • interactive with filters

Corritore, 2005

challenge 12
challenge 1
  • large information spaces

Corritore, 2005

challenge 2
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

Corritore, 2005

challenge 21
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

Corritore, 2005

challenge 22
challenge 2
  • visual correlation between lightning strikes & network alarms
    • time series movie

Corritore, 2005

challenge 3
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

Corritore, 2005

challenge 31
challenge 3
  • software development
    • each sphere a module (diameter - size)
    • lines are func. calls
    • change requests mapped to rate of spin

Corritore, 2005

challenge 32
challenge 3
  • five releases showing selected metrics
    • most recent @ top
    • points modules
    • as evolves, see changes in system
      • perhaps spikes overly convoluted modules

Corritore, 2005

challenge 4
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

Corritore, 2005

slide35
Goal: let human observe, manipulate, search, navigate, explore, filter, discover, understand, and interact with large volumes of data rapidly

Corritore, 2005

shneiderman
shneiderman
  • King of Direct Manipulation
    • mantra: overview first, zoom and filter, details on demand

Corritore, 2005

data types
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 types1
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/

Corritore, 2005

data types2
data types
  • 3D and file systems

Corritore, 2005

data types3
data types
  • multi-dimensional
    • n-dimensional space – examples?
    • spotfire
  • temporal
    • time lines (stock markets, health care)

Corritore, 2005

data types4
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

Corritore, 2005

extended exploration
extended exploration

Linking a river to a histogram

Comparing two rivers

Corritore, 2005

critique
critique

Strong 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

Corritore, 2005

spiral example
spiral Example
  • Spokes (months) and spiral guide lines (years)
  • Planar spiral
  • Distinguishable patterns (rainy season / 1984)

Chimpanzees Monthly food consumption 1980-1988

Corritore, 2005

data types5
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

Corritore, 2005

data types6
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

Corritore, 2005

data types7
data types
  • network – look at these next week

Corritore, 2005

challenges1
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 ……

Corritore, 2005

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