Data Presentation - Lecture 5 - Information Visualisation (4019538FNR)

2 December 2005
Information Visualisation
Data Presentation
Prof. Beat Signer
Department of Computer Science
Vrije Universiteit Brussel
beatsigner.com

Beat Signer - Department of Computer Science - bsigner@vub.ac.be 2
March 11, 2021
Information Visualisation Process
Data
Representation
Data
Data
Presentation
Interaction
mapping
perception and
visual thinking

March 11, 2021
Marks and Channels
▪ Marks are basic graphical elements (geometric primi-
tives) to represent items or links
▪ Channels control the appearance of marks
▪ Vis design space described by orthogonal combination of
marks and channels
▪ Complex visual encodings can be decomposed and
analysed in terms of their marks and channels
▪ building blocks for analysing visual encodings

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Marks
▪ Basic geometric/graphical element in an image
▪ classified according to the number of spatial dimensions

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Marks …
▪ Zero-, one- or two-dimensional marks
▪ three-dimensional marks (volumes) are not used frequently

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Mark Types
▪ Item marks
▪ Link marks
▪ connection marks
- pairwise relationship between two items via a line
▪ containment marks (enclosure or nesting)
- hierarchical relationships using areas

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Channels

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Channels …
▪ Control appearance of mark independently of the
dimensionality of the geometric primitive
▪ Many visual channels
▪ spatial position
▪ shape
▪ colour (hue, saturation and luminance)
▪ motion (e.g. flicker, direction and velocity)
▪ size (i.e. length, area and volume)
▪ tilt (angle)
▪ Size and shape channels cannot be used on all types of
marks
▪ e.g. area marks typically not size or shape coded

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Channel Types
▪ Identity channels
▪ information about what something is
▪ e.g. shape, hue colour channel, motion pattern
▪ Magnitude channels
▪ how much of something is there
▪ e.g. size (length, area or volume), luminance or saturation colour
channels, angle, …

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Using Marks and Channels
▪ Progression of chart types
▪ (a) one quantitative and one categorical attribute
▪ (b) scatterplot with two quantitative attributes
▪ (c) two quantitative and one categorical attribute via hue
▪ (d) three quantitative (one via size) and one categorical attribute
▪ Examples with each attribute encoded via single channel
▪ multiple channels might also be used redundantly

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Using Marks and Channels …
▪ Use of marks and channels guided by the principles of
expressiveness and effectiveness
▪ after identifying most important attributes ensure that they are
encoded with the highest ranked channel
▪ Expressiveness principle
▪ visual encoding should express all of, and only, the information
in the dataset attributes
- ordered data should be shown in a way that our perceptual system senses
as ordered → use magnitude channels
- unordered data should not be shown in a way that perceptually implies an
ordering that does not exist → use identity channels

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Using Marks and Channels …
▪ Effectiveness principle
▪ importance of attribute should match the salience of the channel
▪ most important attributes (depends on the task) encoded with
most effective channels
▪ Attributes encoded with position will dominate the user's
mental model
▪ choice of which attributes to encode with position is the most
central choice in visual encoding

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Channel Effectiveness
[Visualization Analysis & Design, Tamara Munzner, 2014]

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Channel Effectiveness …
▪ Obvious way to quantify effectiveness via accuracy
▪ how close is human perceptual judgement to some objective
measurement of the stimulus?
▪ Different visual channels are perceived with different
levels of accuracy
▪ characterised by Steven's Psychophysical Power Law

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Steven's Psychophysical Power Law
▪ Responses to sensory
experience of magnitude
are characterisable by
power laws
▪ 𝑆 = perceived sensation
▪ I = physical intensity
▪ exponent N depends on
sensory modality
▪ most stimuli are magnified
(superlinear) or compressed
(sublinear)

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Error Rates Across Channels
Results by Cleveland and McGill, 1984

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Channel Effectiveness …
▪ Channel effectiveness mainly based on accuracy but
also takes into account
▪ discriminability
▪ separability
▪ popout
▪ grouping

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Discriminability
▪ Quantify the number of distinguishable steps (bins) that
are available within a visual channel
▪ some channels (e.g. line width) have a very limited number
of bins
▪ small number of bins is not a problem if the number of values to
be encoded is also small
▪ number of different values that need to be shown for an attribute
must not be greater than the available bins for the visual channel
- otherwise aggregate or use different visual channel

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Effective Line Width Use
▪ Limited number of
discriminable bins
▪ line width works well
for 3 or 4 different
values

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Separability
▪ Channels are not always completely independent from
each other (interchannel interference)
▪ ranging from fully separable channels to the inseparably
combined integral channels (major interference)
▪ Visual encoding straightforward with separable channels
▪ encoding of different information in integral channels will fail

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Popout
▪ Many channels provide visual popout (preattentive
processing) where a distinct item stands out from many
others immediately
▪ time to spot the different object does not depend on the number
of distractor objects (a) vs. (b)
▪ massively parallel processing of low-level features
▪ popout effect slower for shapes ((c) and (d)) than for colour hue
channel ((a) and (b))
▪ channels with individual popout cannot simply be combined
((e) and (f))
- need serial search to find the red circle in (f)
▪ Most pairs of channels do not support popout
▪ use popout for a single channel at a time

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Popout …

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Popout Channels
▪ Popout cannot only occur for colour hue and shape
channels
▪ tilt
▪ size
▪ shape
▪ proximity
▪ shadow direction

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Popup Channels …

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Grouping
▪ Containment (links) is the strongest cue for grouping
followed with connection coming in second
▪ Items sharing the same level of a categorical attribute
can also be perceived as a group
▪ Proximity is the third strongest grouping approach
▪ Similarity (hue, motion and shape)
▪ shape and motion channel to be used with care

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Relative versus Absolute Judgements
▪ Perceptual system fundamentally based on relative
judgements and not absolute ones (Weber's Law)
▪ e.g. position along a scale can be perceived more accurately than
pure length judgement without a scale

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Relative Luminance Perception
▪ Perception of luminance is contextual based on the
contrast with surrounding colours

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Colour (Hue) Perception
▪ Our visual systems evolved to provide colour constancy
▪ same surface identifiable across illumination conditions
▪ visual system might work against simple colour encodings

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Mapping Colour
▪ Luminance and saturation
are magnitude channels
while hue is an identity
channel
▪ luminance can be used for
two to four levels (bins)
▪ saturation can be used for
up to three levels (bins)
- strongly interacts with size
channel
▪ saturation and hue are non-
separable channels

March 11, 2021
Comparing HSL Lightness
▪ Computed HSL lightness L is the same for all six colours
▪ true luminance as measured by an instrument
▪ perceived luminance L* represents what we see
- more sensitive to certain wavelengths (green and yellow) as shown earlier
with the spectral sensitivity

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No Implicit Order for Hue
▪ Sometimes learned hue orders (not at perception level)
▪ green-yellow-red from traffics lights
▪ rainbow colour ordering

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Colourmaps
▪ A colourmap defines a mapping between colours and
data values
▪ Colourmaps can be categorical or ordered (sequential
or diverging)
▪ use magnitude channels of luminance and saturation
for ordered data

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Colourmap Categorisation (Taxonomy)

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Categorical Colourmaps
▪ Categorical colourmaps (qualitative colourmaps) are
normally segmented (not continous)
▪ effective for categorical data (next best channel after position)
▪ Good resource for creating colourmaps is ColorBrewer

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Categorical Colourmaps …
▪ Can use six to twelve distinguishable hue steps (bins)
for small separated regions
▪ includes background colour and default object colours
▪ use easy nameable colours: e.g. red, blue, green, yellow, orange,
brown, pink, magenta, purple and cyan
▪ Use highly saturated colours for small regions
▪ Use low-saturation colours (pastels) for large regions

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Ineffective Categorical Colourmap Use

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Example of Using Additional Channels
▪ Dataset with 27 categorical levels from 7 categories

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Example of Using Additional Channels …

March 11, 2021

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Ordered Colourmaps
▪ Sequential colourmap ranges from a minimum value to a
maximum value
▪ use luminance (with or without hue) or saturation channel
▪ Diverging colourmap
▪ use two hues at the endpoints and a neutral colour (e.g. white or
grey) as a midpoint

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Rainbow versus Two-Hue Colour Map
▪ How many hues to use in continous colourmaps?
▪ high-level structure versus local neighbourhoods (fine grained
details)
▪ rainbow colourmap makes it easier to discuss specific (nameable)
subranges

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Rainbow Continous Colourmaps
▪ Problems of rainbow continous colourmaps
▪ hue is use to indicate order (despite being an identity channel)
▪ scale is not perceptually linear
▪ fine details cannot be perceived via the hue channel
- luminance channel much better (luminance contrast required for edge
detection in our eyes)

March 11, 2021
Rainbow Continous Colourmaps …
▪ The three problems of rainbow continous colourmaps
can be addressed by using monotonically increasing
luminance colourmaps
▪ multiple hues are ordered according to their luminance from
lowest to highest
▪ Rainbow colourmap
▪ standard rainbow colour-
map (a) vs. perceptually
linear rainbows (b) with
decreased dynamic range
▪ segmented rainbow for
categorical data (c)

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Bivariate Colourmaps
▪ Safest use of colour channel is to visually encode a
single attribute (univariate)
▪ In the colourmap categorisation we have seen
colourmaps encoding two separate attribute (bivariate)
▪ if one of the two attributes is binary then it is straightforward to
create a comprehensible bivariate colourmap
- choose base set of hues and vary the saturation
▪ if both attributes are categorical with multiple levels the results
will be poor
▪ combinations of sequential and diverging attributes should be
used carefully
- appear frequently in vis solutions but some people have difficulties
to interpret their meaning

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Colourblind-Safe Colourmaps
▪ A safe strategy is to avoid using the hue channel only
▪ e.g. vary luminance or saturation in addition to hue in categorical
colourmaps
▪ Avoid colourmaps emphasising red-green (divergent
red-green ramps)
▪ Use colour blindness simulators and tools such
as Viz Palette

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Size Channels
▪ Suitable for ordered data but interacts with most other
channels
▪ length (1D)
- judgment of length is very accurate
▪ area (2D)
- judgement of area is less accurate
▪ volume (3D)
- volume channel is quite inaccurate

March 11, 2021
Angle (Tilt) Channel
▪ Encode magnitude information based on the orientation
of a mark
▪ angle: orientation of a line with respect to another line
▪ tilt: orientation against the global frame of the display
▪ Accuracy of our perception of an angle is not uniform
▪ very accurate near exact horizontal, vertical or diagonal positions

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Other Channels
▪ Shape channel
▪ commonly applied to point marks
▪ can also be applied to line marks (e.g. dotted or dashed lines)
▪ can distinguish between dozens up to hundreds bins
- strong interaction between shape and size channel
▪ Motion channels
▪ direction of motion
▪ velocity of motion
▪ flicker and blinking frequency
▪ very separable from all other static channels
▪ strongly draws attention
- hard to ignore and should be used carefully

March 11, 2021
Other Channels …
▪ Texture and stippling channel
▪ texture can be simplified by considering it as a combination of the
following three perceptual dimensions
- orientation, scale and contrast
▪ texture can be used to show categorical attributes as well as
ordered attributes
▪ Stippling fills regions of drawings with short strokes
- e.g. dashed or dotted lines
- used for area marks in older printing (to simulate grey)

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Exercise 5
▪ Visualisation in Python

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Further Reading
▪ This lecture is mainly based on the
book Visualization Analysis & Design
▪ chapter 5
- Marks and Channels
▪ chapter 10
- Map Color and Other Channels

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References
▪ Visualization Analysis & Design, Tamara
Munzner, Taylor & Francis Inc, (Har/Psc edition),
May, November 2014,
ISBN-13: 978-1466508910
▪ Semiology of Graphics: Diagrams, Networks,
Maps, Jacques Bertin, ESRI PR (1st edition),
January 2010,
ISBN-13: 978-1466508910
▪ Information Visualization: Perception
for Design, Colin Ware, Morgan Kaufmann
(3rd edition) May 2012,
ISBN-13: 978-0123814647

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References …
▪ ColorBrewer
▪ https://colorbrewer2.org
▪ Viz Palette
▪ https://projects.susielu.com/viz-palette

2 December 2005
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Data Processing and Visualisation Frameworks

Data Presentation - Lecture 5 - Information Visualisation (4019538FNR)

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