Infovis Critical Practice I: Key Concepts in Data Visualization and Perception

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

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

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

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

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)


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


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


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)


Target has a unique feature with respect to distractors (i.e. open sides).
The group can be detected pre-attentively.


Target does not have a unique feature with respect to distractors.
The group cannot be detected pre-attentively.


A sloped line among vertical lines is pre-attentive.
A sloped line among other sloped ones is not.


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?

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!

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 (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

Complexity of data: The ‘real’ data behind “An Inconvenient Truth - Al Gore”

Data Mapping
. 3. understanding how to map data to an image

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

objects
point, line, area, volume, ...
properties
position
size, length, area, volume
orientation, angle, slope
color, texture, transparency
shape
animation, time, blink

Visualization “Language”

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

“‘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)

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


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

Telling a narrative with a “simple” design, though still “complex” data

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

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

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

2. Data-Ink Ratio
Unnecessary & distracting gridlines - graphics emphasizing style over information

2. Data-Ink Ratio

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
Avoid non-data ink (fonts, lines, aesthetics)
Growing wealth and declining ODA

4. Chart junk
Avoid non-data ink (fonts, lines, aesthetics)

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 (and never use 3D on a 2D medium).

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: 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
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...

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

Infovis Critical Practice I: Key Concepts in Data Visualization and Perception

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