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Critical Practice I: Infovis
Luca - Campus Sint-Lukas Brussel
a/prof. Andrew Vande Moere
Department of Architecture, Urbanism & Planning - ASRO - KU Leuven
------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
Hackers - United Artists - 1996
Tron - The Electronic Gladiator - 1982
Johnny Mnemonic - Tristar Pictures -1995
Cyber Swap Worlds - 1997
City of News - MIT Media Lab - 1997
VR/Search - Andrew Vande Moere - 1998
VR Data Visualization - ETH-Zurich
Information Visualization for Immersive VR - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=AZmcrVplqDU
VR Data Visualization
Stock Market Swarm - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=LjUZ6vcTc1Q
Stock Market Swarm - Andrew Vande Moere - 2004
http://www.youtube.com/watch?v=LjUZ6vcTc1Q
University Finances Visualization - Andrew Vande Moere - 2003
University Finances Visualization - Andrew Vande Moere - 2003
http://www.youtube.com/watch?v=duxjQKgYtNY
Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
[Proposed] Time Plan
 29 october 2012
- Data + Perception + Data Mapping
- Bad/Good Infographic Guidelines
5 november 2012
- Infovis, Storytelling,Research Results, ...
- Compelling Dataviz Examples
http://vimeo.com/34182381
Six Degrees of Mohamed Atta
http://business2.com/articles/mag/0,1640,35253,FF.html
US Terrorism Response Org Chart
http://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart
Space Shuttle Launch
. O-ring damage data

. launch or not launch?

. risk of human lives versus loosing reputation

. ambient temperature at launch: 25-30 degrees F
Space Shuttle Launch - January 28, 1986
1854 - Epidemiological data chart
1854 - Epidemiological data chart
Choice of “Visual” / “Data”
. anything can be ‘translated’ in anything

. can be ineffective (wrong answers)

. can be inefficient (takes too much effort)

. can be disengaging (no users, giving up, ...)
How to Design Visualization?
. 1. understanding properties of the image

. 2. understanding properties of the data

. 3. understanding how to map data to an image
Visual Perception
. 1. understanding properties of the image
Human perception governed by general principles?
Shark or submarine?
http://en.wikipedia.org/wiki/File:SharkOrSubmarine4024617900.jpg
What to look for in data visualization...
Pre-Attentive Features
. time taken to make a decision is constant

. and is less than 200-250ms (< eye movement)

. independent of number of added detractors

. primitive features, low-level visual processing

. salience depends on: ‘strength’, and ‘context’
‘Pop-out’ Features
. form: line orientation, length, width, visual marks,...

. color: hue, intensity, ...

. motion: flicker, direction of motion, ...

. spatial position: depth, convex/concave shape,...
orienta(on                                                             size
                             length,	
  width       closure




   curvature                density,	
  contrast   number,	
  es(ma(on   colour	
  (hue)




Some already known pre-attentive visual features
Viewer can rapidly & accurately determine
                 whether the target (red circle) is present or absent:
                            difference detected in color


Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Viewer can rapidly & accurately determine
                  whether the target (red circle) is present or absent
                        difference detected in form (curvature)


Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Viewer cannot rapidly & accurately determine whether target is present or
 absent when target has combined two or more features, also present in the
               distracters. Viewer must search sequentially


Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Hue-on-form feature hierarchy: (a) a horizontal hue boundary is pre-
  attentive identified when form is held constant; (b) a vertical hue boundary
 is pre-attentively identified when form varies randomly in the background


Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Hue-on-form feature hierarchy: (c) a vertical form boundary is preattentively
identified when hue is held constant; (d) horizontal form boundary cannot be pre-
                attentively identified when hue varies randomly


Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Viewer cannot rapidly & accurately determine border by a conjunction of
  features (red circles & blue squares on the left, blue circles and red squares on
                                      the right)


Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Target has a unique feature with respect to distractors (i.e. open sides).
                  The group can be detected pre-attentively.

Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Target does not have a unique feature with respect to distractors.
                 The group cannot be detected pre-attentively.

Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
A sloped line among vertical lines is pre-attentive.
                   A sloped line among other sloped ones is not.

Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
1281768756138976546984506985604982826762
  9809858458224509856458945098450980943585
  9091030209905959595772564675050678904567
  8845789809821677654876364908560912949686




Text pre-attentive?
1281768756138976546984506985604982826762
  9809858458224509856458945098450980943585
  9091030209905959595772564675050678904567
  8845789809821677654876364908560912949686




Text pre-attentive?
  ROOD	
   GROEN	
              BLAUW	
             GEEL
        	
   ROZE	
  	
   ORANJE	
         BLAUW	
  	
         GROEN
        	
  	
  BLAUW	
   BRUIN	
  	
      GROEN	
  	
         GEEL
        	
   ORANGE	
  	
  BRUIN	
  	
     BLAUW	
             BRUIN
        	
   ROOD	
  	
   BLAUW	
          GEEL	
  	
   	
     GROEN
        	
   ROZE	
  	
   GEEL	
  	
       GROEN	
  	
         BLAUW




Text pre-attentive?
Gestalt Laws
How can these be applied in information visualization?
Figure and Ground
Figure and Ground
Gestalt Laws: Law of Simplicity
http://machineslikeus.com/the-constructive-aspect-of-visual-perception
Law of Simplicity
http://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
Law of Proximity
We group together things that seem near each other, and assume they are similar.
Law of Similarity
We group together things that share the same color, shape, direction, ....
Law of Similarity
Such as the choice of colors: richer color choice, forcing the orange parts to be together
Law of Connectedness
We group things together that are visually connected. Stronger than similarity.
Law of Continuity
We tend to connect things that are arranged in a smooth way, even when a visual ‘gap’.
Law of Closure
We group things that are enclosed in visual shapes.
Law of Symmetry
We assume the horizontal / vertical part is identical. Good for before/after comparisons...
•pattern	
  recognition
 •we	
  are	
  constantly	
  grouping	
  objects	
  based	
  on	
  color,	
  
  shape,	
  direction,	
  proximity,	
  closure/enclosure,	
  ...
    •so:	
  where	
  should	
  a	
  chart	
  legend	
  be	
  located?
• 	
  keep	
  it	
  ‘simple’
  •we	
  like	
  simple,	
  close,	
  smooth,	
  symmetrical,	
  easy-­‐to-­‐
      process	
  shapes...
•design	
  accordingly
 •if	
  we	
  are	
  aware	
  of	
  these	
  laws	
  we	
  can	
  take	
  advantage	
  
  of	
  them	
  to	
  design	
  better	
  charts	
  or	
  dashboards.
•be	
  careful
 •be	
  aware	
  of	
  their	
  negative	
  impact:	
  we	
  shouldn’t	
  force	
  
  the	
  reader	
  to	
  see	
  groups	
  that	
  aren’t	
  really	
  there
http://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
Context influences visual perception!
71




Context influences visual perception!
Context influences visual perception!
Data
. 2. understanding properties of data
“Concrete” Data
. carries spatial layout

. position, color, visual characteristics

. represented by graphical reproduction
“Abstract” Data
. data without natural representation

. requires metaphor to be perceived

. data is “mapped” in visual form
Information Visualization?
Information Visualization (Scientific) Visualization
Examples                      Examples
stock market, friend          clouds, microscopic events,
network, DNA functions, ...   human organs, ...
Data Characteristics          Data Characteristics
Abstract                      Concrete
Multi-dimensional             2 or 3 dimensional
                              Mostly time-dependent
Requirements.                 Requirements
Visual metaphor               3D and fast rendering
User interaction              User interaction
Focus: Exploration            Focus: Analysis
then Analysis                 then Exploration
then Presentation             then Presentation
Kinds of data...
S Stevens “On the theory of scales and measurements” (1946)
Quantitative Data
. numerical, scalar values, arithmetic operations,...
   . e.g. 4, 14, 5445453, 2, 1.00342, 3, ...




                                                        83
Ordinal Data
. larger / smaller than...
   . e.g. Monday, Tuesday, Wednesday, ...




                                            84
Categorical / Hierarchical Data
. can be categorized
   . e.g. alphabetically, thematically, functionality
   . e.g. desktop folder hierarchy, work hierarchy, ...




                                                          85
Network / Relational Data
. one-to-many, many-to-one relationships
  . often these are ‘weighted’
  . e.g. social networks, people working on projects, ...
                                                            86
Nominal / Unstructured Data
. no order, no units, only “equal or different”
  . e.g. Australia, Belgium, Mexico
                                                  87
Temporal / Dynamic Data
. time dependent
   . related to progress of time, history, ...
   . e.g. stock market, news stories, sensor readings,...
                                                            88
§ attributes
 § dimensions
 § variables
 § columns

§ values
 § quantitative
 § ordinal
 § categorical
 § nominal

§ items
 § tuples
 § datapoints
 § rows
§ dimensionality
 § # of attributes

§ scale / size
 § # of items
§ value range
 § bits/value, min/max, etc.
§ time
 dependency?
“The current complexity of data is
  staggering, and our ability to collect
  data is increasing at a faster rate that
  our ability to analyse it.”




Data Complexity
How is data ‘complex’?
. size: number of records

. dimensionality: number of attributes

. time-dependency: data changes over time
Data is more complex now?
. complexity of human society always increasing

. quantity always increasing, never decreasing!

. speed of data creation always increasing
An Inconvenient Truth - Al Gore
1. Social relevance - An Inconvenient Truth - 2006
An Inconvenient Truth - Al Gore
Complexity of data: The ‘real’ data behind “An Inconvenient Truth - Al Gore”
Data Mapping
. 3. understanding how to map data to an image
Why mapping necessary?
because the data is abstract...
                   “the challenge is to invent
                          new metaphors for
                    presenting information &
                           developing ways to
                            manipulate these
                   metaphors to make sense
                     out of the information...”
Information Visualization ‘Design’ Challenge
Data + data mapping = visual representation that can be (relatively fairly) interpreted
data                                 insight

    10010110                   knowledge
                                 transfer


            data mapping
                                             mapping
                                             inversion


         visualisation                       comprehension
                                             !
                           visual transfer


Data Mapping Methodology
data               §data   mapping requirements?
                               § computable
   10010110                     § no user interaction required
                                § algorithm: data -> value

            data                § comprehensible
            mapping                 § user  understands
                                    § intuitively, within short time
        visualisation           § invertible
                                    § mapping backwards from
                                    § form to exact data value

                                      103




Data Mapping Methodology
Mon                                   Sun          Mon                               Sun

15    17    19    15    22    10    15             15    17    19     15   22   10   15
15    10    11    15    20    12    18             15    10    11     15   20   12   18
14    23    12    15    18    12    17             14    23    12     15   18   12   17
13    11    21    10    29    12    17             13    11    21     10   29   12   17
29    12                                           22    12

Mon                                   Sun          Mon                               Sun

15    17    19    15    22    10    15             15    17    19     15   22   10   15
15    10    11    15    20    12    18             15    10    11     15   20   12   18
14    23    12    15    18    12    17             14    23    12     15   18   12   17
13    11    21    10    29    12    17             13    11    21     10   29   12   17
22    12                                           22    12

      0                             35                   0                           35
                 20          29                               10      12



Including Visual Perception / Dynamic Queries / Pattern Exploration
15   17   19   15   22   10   15   15   17   19   15   18   10   15   15   17   19   15   11   10   15
                 15   10   11   15   20   12   18   15   10   11   15   16   12   18   15   10   11   15   08   12   18
                 14   23   12   15   18   12   17   23   23   27   34   32   29   27   14   15   12   15   18   12   17
                 13   11   21   10   17   12   30   13   11   21   10   29   12   30   13   11   15   10   10   12   10
                 22   12                            22   12                            11   12


                                          0                                            35
                                                         20                  29




                                          0                                            35
                                                     20                      29




                                      0                                                35
                                                    20                       29
Increasing Visual Bandwidth / Visual Abstraction / Increasing Data Density
Example Movie Dataset: How to approach the data mapping?
1 items
                                        data
                                               ê
                                               ???
                                    2 attributes
                                    data
                                         ê
                                         ???

Data Characteristics
From abstract data to visual form
1 data items
                                                ê
                                         visual objects
                             2      data attributes
                                          ê
                              visual object properties

Data Mapping
From abstract data to visual form
objects
                              point, line, area, volume, ...
                           properties
                                                   position
                               size, length, area, volume
                                 orientation, angle, slope
                             color, texture, transparency
                                                     shape
                                    animation, time, blink

Visualization “Language”
From abstract data to visual form
Example scatterplot of movie dataset



 Year → X
 Length → Y
 Popularity → size
 Subject → color
 Award? → shape
Data Mapping Limitations
. data scale: one object for each tuple?

. data dimensionality: visual cue for each attribute?

. value range: metaphorical range?

. time dependency: can metaphor change?
(Cleveland and McGill)



Guideline: Assign most important data pattern the most perceptual accurate visual cue.
Visual objects versus visual attributes.
http://understandinggraphics.com/visualizations/information-display-tips/
What chart type to choose?
http://extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
Intuitive data mapping....




Less intuitive data mapping....
Context influences color perception!
http://www.nationalgeographic.nl/community/foto/bekijken/slow-motion-2




Motion as Visual Cue
. pre-attentive feature, can be added in ‘parallel’

. simple action perceived as sophisticated behavior

. attracts attention, enjoyable, motivates, ...

. technology available: e.g. graphics, screen estate
"An Experimental Study of Apparent Behaviour" (1944)
Fritz Heider & Marianne Simmel
Simple motion, complex interpretation.
http://www.biomotionlab.ca/Demos/BMLwalker.html
Motion properties
“‘Graphical Excellence’ is that which
    gives to the viewer a great number of
    ideas in the shortest time, with the
    least ink, in the smallest space”

. “show data variation”, not “design variation”

. communication: clarity, precision, efficiency

. simplicity of design - complexity of data
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
temperature
time
temp[day]
longitude
latitude
army[size, day]
army[position, day]
1. Show comparisons, contrasts, differences
2. Show causality, mechanism, explanation,
systematic structure
3. Show multivariate data; that is, show more
than 1 or 2 variables
4. Completely integrate words, numbers, images,
diagrams
5.Thoroughly describe the evidence: title, authors
and sponsors, data sources, add measurement
scales, highlight relevant issues
6.Analytical presentations ultimately stand or fall
depending on the quality, relevance and
integrity of their content
Principles for the Analysis and Presentation of Data - Tufte
1. Show comparisons, contrasts,
differences
2. Show causality, mechanism,
 explanation, systematic structure




French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
3. Show multivariate data; that is,
 show more than 1 or 2 variables
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
4. Completely integrate words,
 numbers, images, diagrams
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
5.Thoroughly describe the
 evidence: title, authors and
 sponsors, data sources, add
 measurement scales, highlight
 relevant issues
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
6.Analytical presentations
 ultimately stand or fall depending
 on the quality, relevance and
 integrity of their content
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
Telling a narrative with a “simple” design, though still “complex” data
Chart Components
1. Lie Factor

2. Data Ink Ratio

3. Data Density

4. Chart Junk

5. Expressiveness
Principles for the Analysis and Presentation of Data - Tufte
Lie Factor
Exaggeration of differences between values
Lie Factor
Exaggeration of differences between values
1. Lie Factor
Quantifying the exaggeration of differences between values
1. Lie Factor
Exaggeration of differences between values
1. Lie Factor
How many times can 1978 fit into 1958?
2. Data-Ink Ratio
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
2. Data-Ink Ratio
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
Five Laws of “Data-Ink”
• Above all else show (only) the data
• Maximize the data-ink ratio
• Erase non-data ink
• Erase redundant data-ink
• Revise and edit

2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
2. Data-Ink Ratio
Unnecessary & distracting gridlines - graphics emphasizing style over information
2. Data-Ink Ratio
Unnecessary & distracting patterns - graphics emphasizing style over information
3. Data Density
“A pixel is a terrible thing to waste” Ben Shneiderman
3. Data Density
“A pixel is a terrible thing to waste” Ben Shneiderman
4. Chart Junk
Question: Is the data still visible without the graphics?
How much can you take away before it becomes illegible?
4. Chart Junk
Is the data still visible without the graphics?
4. Chart Junk
Is the data still visible without the graphics?
4. Chart Junk
Is the data still visible without the graphics?
4. Chart junk
Avoid non-data ink (fonts, lines, aesthetics)
                                                Growing wealth and declining ODA 
4. Chart junk
Avoid non-data ink (fonts, lines, aesthetics)
4. Chart junk
Avoid non-data ink (fonts, lines, aesthetics)
5. Expressiveness
Encode the data - Encode only the data
5. Expressiveness
Encode the data - Encode only the data
5. Expressiveness
Use line charts for time-series or continuous data, never for categorical data.
5. Expressiveness
Encode the data - Encode only the data
5. Expressiveness
Encode the data - Encode only the data (and never use 3D on a 2D medium).
5. Expressiveness
Encode the data - Encode only the data
5. Expressiveness
Encode the data - Encode only the data
5. Expressiveness
Write out explanations on the graphic itself.
5. Expressiveness
Write out explanations on the graphic itself.
5. Expressiveness
Add labels next to graph (then no legend required!)
5. Expressiveness
Scaling: lines should cover 2/3 of data area.... (contentious issue!)
5. Expressiveness
Colors: avoid high contrast
5. Expressiveness
Colors: avoid high contrast
5. Expressiveness
Colors: choose harmonious colors
5. Expressiveness
Colors: for related set of data attributes, use similar colors.
5. Expressiveness
Colors: use color for highlighting. Note difference if data attributes are related or not!
5. Expressiveness
Orientation text horizontally. Order by size, not by alphabet.
5. Expressiveness
Comparing vertical axes
junkcharts.typepad.com only when identical...
5. Expressiveness
Comparing vertical axes
junkcharts.typepad.com only when identical, or at least similar rate (e.g. 2x baseline)
5. Expressiveness
Avoid size as quantitative
junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
5. Expressiveness
Avoid size as quantitative
junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
5. Expressiveness
Maps only useful for spatial distribution. They do not take into account population density
junkcharts.typepad.com
(which explains trends), physical size of districts (which are visually more prominent),...
5. Expressiveness
Avoid donut charts...
junkcharts.typepad.com
China, Egypt, Mexico, South Africa, Philippines, India - and for different periods.
Homework
- Find a recent,“Belgian” infographic
- Analyze: dataset, data mapping, ...
- Critique (good/bad) design decisions
- Propose redesign + explain why
- Show example, critique, redesign
- Send 1 PDF file to...
Thank you! Questions?
------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics

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Introduction to Information Visualization (Part 1)

  • 1. Critical Practice I: Infovis Luca - Campus Sint-Lukas Brussel a/prof. Andrew Vande Moere Department of Architecture, Urbanism & Planning - ASRO - KU Leuven ------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
  • 2.
  • 3. Hackers - United Artists - 1996
  • 4. Tron - The Electronic Gladiator - 1982
  • 5. Johnny Mnemonic - Tristar Pictures -1995
  • 7. City of News - MIT Media Lab - 1997
  • 8. VR/Search - Andrew Vande Moere - 1998
  • 9. VR Data Visualization - ETH-Zurich
  • 10. Information Visualization for Immersive VR - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=AZmcrVplqDU VR Data Visualization
  • 11. Stock Market Swarm - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 12. Stock Market Swarm - Andrew Vande Moere - 2004 http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  • 13. University Finances Visualization - Andrew Vande Moere - 2003
  • 14. University Finances Visualization - Andrew Vande Moere - 2003 http://www.youtube.com/watch?v=duxjQKgYtNY
  • 15. Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
  • 16. [Proposed] Time Plan 29 october 2012 - Data + Perception + Data Mapping - Bad/Good Infographic Guidelines 5 november 2012 - Infovis, Storytelling,Research Results, ... - Compelling Dataviz Examples
  • 18. Six Degrees of Mohamed Atta http://business2.com/articles/mag/0,1640,35253,FF.html
  • 19. US Terrorism Response Org Chart http://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart
  • 20.
  • 21.
  • 22. Space Shuttle Launch . O-ring damage data . launch or not launch? . risk of human lives versus loosing reputation . ambient temperature at launch: 25-30 degrees F
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. Space Shuttle Launch - January 28, 1986
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. Choice of “Visual” / “Data” . anything can be ‘translated’ in anything . can be ineffective (wrong answers) . can be inefficient (takes too much effort) . can be disengaging (no users, giving up, ...)
  • 37. How to Design Visualization? . 1. understanding properties of the image . 2. understanding properties of the data . 3. understanding how to map data to an image
  • 38. Visual Perception . 1. understanding properties of the image
  • 39. Human perception governed by general principles?
  • 41. What to look for in data visualization...
  • 42. Pre-Attentive Features . time taken to make a decision is constant . and is less than 200-250ms (< eye movement) . independent of number of added detractors . primitive features, low-level visual processing . salience depends on: ‘strength’, and ‘context’
  • 43. ‘Pop-out’ Features . form: line orientation, length, width, visual marks,... . color: hue, intensity, ... . motion: flicker, direction of motion, ... . spatial position: depth, convex/concave shape,...
  • 44. orienta(on size length,  width closure curvature density,  contrast number,  es(ma(on colour  (hue) Some already known pre-attentive visual features
  • 45. Viewer can rapidly & accurately determine whether the target (red circle) is present or absent: difference detected in color Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 46. Viewer can rapidly & accurately determine whether the target (red circle) is present or absent difference detected in form (curvature) Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 47. Viewer cannot rapidly & accurately determine whether target is present or absent when target has combined two or more features, also present in the distracters. Viewer must search sequentially Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 48. Hue-on-form feature hierarchy: (a) a horizontal hue boundary is pre- attentive identified when form is held constant; (b) a vertical hue boundary is pre-attentively identified when form varies randomly in the background Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 49. Hue-on-form feature hierarchy: (c) a vertical form boundary is preattentively identified when hue is held constant; (d) horizontal form boundary cannot be pre- attentively identified when hue varies randomly Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 50. Viewer cannot rapidly & accurately determine border by a conjunction of features (red circles & blue squares on the left, blue circles and red squares on the right) Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 51. Target has a unique feature with respect to distractors (i.e. open sides). The group can be detected pre-attentively. Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 52. Target does not have a unique feature with respect to distractors. The group cannot be detected pre-attentively. Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 53. A sloped line among vertical lines is pre-attentive. A sloped line among other sloped ones is not. Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  • 54. 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 Text pre-attentive?
  • 55. 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 Text pre-attentive?
  • 56.   ROOD   GROEN   BLAUW   GEEL   ROZE     ORANJE   BLAUW     GROEN    BLAUW   BRUIN     GROEN     GEEL   ORANGE    BRUIN     BLAUW   BRUIN   ROOD     BLAUW   GEEL       GROEN   ROZE     GEEL     GROEN     BLAUW Text pre-attentive?
  • 57. Gestalt Laws How can these be applied in information visualization?
  • 60. Gestalt Laws: Law of Simplicity http://machineslikeus.com/the-constructive-aspect-of-visual-perception
  • 62. Law of Proximity We group together things that seem near each other, and assume they are similar.
  • 63. Law of Similarity We group together things that share the same color, shape, direction, ....
  • 64. Law of Similarity Such as the choice of colors: richer color choice, forcing the orange parts to be together
  • 65. Law of Connectedness We group things together that are visually connected. Stronger than similarity.
  • 66. Law of Continuity We tend to connect things that are arranged in a smooth way, even when a visual ‘gap’.
  • 67. Law of Closure We group things that are enclosed in visual shapes.
  • 68. Law of Symmetry We assume the horizontal / vertical part is identical. Good for before/after comparisons...
  • 69. •pattern  recognition •we  are  constantly  grouping  objects  based  on  color,   shape,  direction,  proximity,  closure/enclosure,  ... •so:  where  should  a  chart  legend  be  located? •  keep  it  ‘simple’ •we  like  simple,  close,  smooth,  symmetrical,  easy-­‐to-­‐ process  shapes... •design  accordingly •if  we  are  aware  of  these  laws  we  can  take  advantage   of  them  to  design  better  charts  or  dashboards. •be  careful •be  aware  of  their  negative  impact:  we  shouldn’t  force   the  reader  to  see  groups  that  aren’t  really  there http://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
  • 73.
  • 74.
  • 75.
  • 76. Data . 2. understanding properties of data
  • 77. “Concrete” Data . carries spatial layout . position, color, visual characteristics . represented by graphical reproduction
  • 78. “Abstract” Data . data without natural representation . requires metaphor to be perceived . data is “mapped” in visual form
  • 80. Information Visualization (Scientific) Visualization Examples Examples stock market, friend clouds, microscopic events, network, DNA functions, ... human organs, ... Data Characteristics Data Characteristics Abstract Concrete Multi-dimensional 2 or 3 dimensional Mostly time-dependent Requirements. Requirements Visual metaphor 3D and fast rendering User interaction User interaction Focus: Exploration Focus: Analysis then Analysis then Exploration then Presentation then Presentation
  • 81. Kinds of data... S Stevens “On the theory of scales and measurements” (1946)
  • 82. Quantitative Data . numerical, scalar values, arithmetic operations,... . e.g. 4, 14, 5445453, 2, 1.00342, 3, ... 83
  • 83. Ordinal Data . larger / smaller than... . e.g. Monday, Tuesday, Wednesday, ... 84
  • 84. Categorical / Hierarchical Data . can be categorized . e.g. alphabetically, thematically, functionality . e.g. desktop folder hierarchy, work hierarchy, ... 85
  • 85. Network / Relational Data . one-to-many, many-to-one relationships . often these are ‘weighted’ . e.g. social networks, people working on projects, ... 86
  • 86. Nominal / Unstructured Data . no order, no units, only “equal or different” . e.g. Australia, Belgium, Mexico 87
  • 87. Temporal / Dynamic Data . time dependent . related to progress of time, history, ... . e.g. stock market, news stories, sensor readings,... 88
  • 88. § attributes § dimensions § variables § columns § values § quantitative § ordinal § categorical § nominal § items § tuples § datapoints § rows
  • 89. § dimensionality § # of attributes § scale / size § # of items § value range § bits/value, min/max, etc. § time dependency?
  • 90. “The current complexity of data is staggering, and our ability to collect data is increasing at a faster rate that our ability to analyse it.” Data Complexity
  • 91. How is data ‘complex’? . size: number of records . dimensionality: number of attributes . time-dependency: data changes over time
  • 92. Data is more complex now? . complexity of human society always increasing . quantity always increasing, never decreasing! . speed of data creation always increasing
  • 94. 1. Social relevance - An Inconvenient Truth - 2006
  • 96. Complexity of data: The ‘real’ data behind “An Inconvenient Truth - Al Gore”
  • 97. Data Mapping . 3. understanding how to map data to an image
  • 99. because the data is abstract... “the challenge is to invent new metaphors for presenting information & developing ways to manipulate these metaphors to make sense out of the information...” Information Visualization ‘Design’ Challenge
  • 100. Data + data mapping = visual representation that can be (relatively fairly) interpreted
  • 101. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transfer Data Mapping Methodology
  • 102. data §data mapping requirements? § computable 10010110 § no user interaction required § algorithm: data -> value data § comprehensible mapping § user understands § intuitively, within short time visualisation § invertible § mapping backwards from § form to exact data value 103 Data Mapping Methodology
  • 103. Mon Sun Mon Sun 15 17 19 15 22 10 15 15 17 19 15 22 10 15 15 10 11 15 20 12 18 15 10 11 15 20 12 18 14 23 12 15 18 12 17 14 23 12 15 18 12 17 13 11 21 10 29 12 17 13 11 21 10 29 12 17 29 12 22 12 Mon Sun Mon Sun 15 17 19 15 22 10 15 15 17 19 15 22 10 15 15 10 11 15 20 12 18 15 10 11 15 20 12 18 14 23 12 15 18 12 17 14 23 12 15 18 12 17 13 11 21 10 29 12 17 13 11 21 10 29 12 17 22 12 22 12 0 35 0 35 20 29 10 12 Including Visual Perception / Dynamic Queries / Pattern Exploration
  • 104. 15 17 19 15 22 10 15 15 17 19 15 18 10 15 15 17 19 15 11 10 15 15 10 11 15 20 12 18 15 10 11 15 16 12 18 15 10 11 15 08 12 18 14 23 12 15 18 12 17 23 23 27 34 32 29 27 14 15 12 15 18 12 17 13 11 21 10 17 12 30 13 11 21 10 29 12 30 13 11 15 10 10 12 10 22 12 22 12 11 12 0 35 20 29 0 35 20 29 0 35 20 29 Increasing Visual Bandwidth / Visual Abstraction / Increasing Data Density
  • 105. Example Movie Dataset: How to approach the data mapping?
  • 106. 1 items data ê ??? 2 attributes data ê ??? Data Characteristics From abstract data to visual form
  • 107. 1 data items ê visual objects 2 data attributes ê visual object properties Data Mapping From abstract data to visual form
  • 108. objects point, line, area, volume, ... properties position size, length, area, volume orientation, angle, slope color, texture, transparency shape animation, time, blink Visualization “Language” From abstract data to visual form
  • 109. Example scatterplot of movie dataset Year → X Length → Y Popularity → size Subject → color Award? → shape
  • 110. Data Mapping Limitations . data scale: one object for each tuple? . data dimensionality: visual cue for each attribute? . value range: metaphorical range? . time dependency: can metaphor change?
  • 111. (Cleveland and McGill) Guideline: Assign most important data pattern the most perceptual accurate visual cue.
  • 112. Visual objects versus visual attributes. http://understandinggraphics.com/visualizations/information-display-tips/
  • 113. What chart type to choose? http://extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
  • 114. Intuitive data mapping.... Less intuitive data mapping....
  • 115. Context influences color perception!
  • 116. http://www.nationalgeographic.nl/community/foto/bekijken/slow-motion-2 Motion as Visual Cue . pre-attentive feature, can be added in ‘parallel’ . simple action perceived as sophisticated behavior . attracts attention, enjoyable, motivates, ... . technology available: e.g. graphics, screen estate
  • 117. "An Experimental Study of Apparent Behaviour" (1944) Fritz Heider & Marianne Simmel
  • 118. Simple motion, complex interpretation. http://www.biomotionlab.ca/Demos/BMLwalker.html
  • 120. “‘Graphical Excellence’ is that which gives to the viewer a great number of ideas in the shortest time, with the least ink, in the smallest space” . “show data variation”, not “design variation” . communication: clarity, precision, efficiency . simplicity of design - complexity of data
  • 121. French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 124. 1. Show comparisons, contrasts, differences 2. Show causality, mechanism, explanation, systematic structure 3. Show multivariate data; that is, show more than 1 or 2 variables 4. Completely integrate words, numbers, images, diagrams 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issues 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their content Principles for the Analysis and Presentation of Data - Tufte
  • 125. 1. Show comparisons, contrasts, differences
  • 126. 2. Show causality, mechanism, explanation, systematic structure French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 127. 3. Show multivariate data; that is, show more than 1 or 2 variables French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 128. 4. Completely integrate words, numbers, images, diagrams French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 129. 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issues French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 130. 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their content French Invasion of Russia (Minard, +-1864) Napoleon Retreat (Minard, +-1864)
  • 131. Telling a narrative with a “simple” design, though still “complex” data
  • 133. 1. Lie Factor 2. Data Ink Ratio 3. Data Density 4. Chart Junk 5. Expressiveness Principles for the Analysis and Presentation of Data - Tufte
  • 134. Lie Factor Exaggeration of differences between values
  • 135. Lie Factor Exaggeration of differences between values
  • 136. 1. Lie Factor Quantifying the exaggeration of differences between values
  • 137. 1. Lie Factor Exaggeration of differences between values
  • 138. 1. Lie Factor How many times can 1978 fit into 1958?
  • 139. 2. Data-Ink Ratio 2. Data-Ink Ratio Unnecessary & distracting patterns - graphics emphasizing style over information
  • 140. 2. Data-Ink Ratio 2. Data-Ink Ratio Unnecessary & distracting patterns - graphics emphasizing style over information
  • 141. Five Laws of “Data-Ink” • Above all else show (only) the data • Maximize the data-ink ratio • Erase non-data ink • Erase redundant data-ink • Revise and edit 2. Data-Ink Ratio Unnecessary & distracting patterns - graphics emphasizing style over information
  • 142. 2. Data-Ink Ratio Unnecessary & distracting gridlines - graphics emphasizing style over information
  • 143. 2. Data-Ink Ratio Unnecessary & distracting patterns - graphics emphasizing style over information
  • 144. 3. Data Density “A pixel is a terrible thing to waste” Ben Shneiderman
  • 145. 3. Data Density “A pixel is a terrible thing to waste” Ben Shneiderman
  • 146. 4. Chart Junk Question: Is the data still visible without the graphics? How much can you take away before it becomes illegible?
  • 147. 4. Chart Junk Is the data still visible without the graphics?
  • 148. 4. Chart Junk Is the data still visible without the graphics?
  • 149. 4. Chart Junk Is the data still visible without the graphics?
  • 150. 4. Chart junk Avoid non-data ink (fonts, lines, aesthetics) Growing wealth and declining ODA 
  • 151. 4. Chart junk Avoid non-data ink (fonts, lines, aesthetics)
  • 152. 4. Chart junk Avoid non-data ink (fonts, lines, aesthetics)
  • 153. 5. Expressiveness Encode the data - Encode only the data
  • 154. 5. Expressiveness Encode the data - Encode only the data
  • 155. 5. Expressiveness Use line charts for time-series or continuous data, never for categorical data.
  • 156. 5. Expressiveness Encode the data - Encode only the data
  • 157. 5. Expressiveness Encode the data - Encode only the data (and never use 3D on a 2D medium).
  • 158. 5. Expressiveness Encode the data - Encode only the data
  • 159. 5. Expressiveness Encode the data - Encode only the data
  • 160. 5. Expressiveness Write out explanations on the graphic itself.
  • 161. 5. Expressiveness Write out explanations on the graphic itself.
  • 162. 5. Expressiveness Add labels next to graph (then no legend required!)
  • 163. 5. Expressiveness Scaling: lines should cover 2/3 of data area.... (contentious issue!)
  • 166. 5. Expressiveness Colors: choose harmonious colors
  • 167. 5. Expressiveness Colors: for related set of data attributes, use similar colors.
  • 168. 5. Expressiveness Colors: use color for highlighting. Note difference if data attributes are related or not!
  • 169. 5. Expressiveness Orientation text horizontally. Order by size, not by alphabet.
  • 170. 5. Expressiveness Comparing vertical axes junkcharts.typepad.com only when identical...
  • 171. 5. Expressiveness Comparing vertical axes junkcharts.typepad.com only when identical, or at least similar rate (e.g. 2x baseline)
  • 172. 5. Expressiveness Avoid size as quantitative junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
  • 173. 5. Expressiveness Avoid size as quantitative junkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
  • 174. 5. Expressiveness Maps only useful for spatial distribution. They do not take into account population density junkcharts.typepad.com (which explains trends), physical size of districts (which are visually more prominent),...
  • 177. China, Egypt, Mexico, South Africa, Philippines, India - and for different periods.
  • 178. Homework - Find a recent,“Belgian” infographic - Analyze: dataset, data mapping, ... - Critique (good/bad) design decisions - Propose redesign + explain why - Show example, critique, redesign - Send 1 PDF file to...
  • 179. Thank you! Questions? ------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics