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

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

This lecture forms part of the course Information Visualisation given at the Vrije Universiteit Brussel.

Beat Signer
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March 17, 2022
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  1. 2 December 2005 Information Visualisation Data Presentation Prof. Beat Signer

    Department of Computer Science Vrije Universiteit Brussel beatsigner.com
  2. Beat Signer - Department of Computer Science - [email protected] 2

    March 17, 2022 Information Visualisation Process Data Representation Data Data Presentation Interaction mapping perception and visual thinking
  3. Beat Signer - Department of Computer Science - [email protected] 3

    March 17, 2022 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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    March 17, 2022 Marks ▪ Basic geometric/graphical element in an image ▪ classified according to the number of spatial dimensions
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    March 17, 2022 Marks … ▪ Zero-, one- or two-dimensional marks ▪ three-dimensional marks (volumes) are not used frequently due to limited perception
  6. Beat Signer - Department of Computer Science - [email protected] 6

    March 17, 2022 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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    March 17, 2022 Channels
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    March 17, 2022 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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    March 17, 2022 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, …
  10. Beat Signer - Department of Computer Science - [email protected] 10

    March 17, 2022 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 ▪ In examples each attribute encoded via single channel ▪ multiple channels might also be used redundantly
  11. Beat Signer - Department of Computer Science - [email protected] 11

    March 17, 2022 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 (ordered attributes) should be shown in a way that our perceptual system senses as ordered → use magnitude channels - unordered data (categorical attributes) should not be shown in a way that perceptually implies an ordering that does not exist → use identity channels
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    March 17, 2022 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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    March 17, 2022 Channel Effectiveness [Visualization Analysis & Design, Tamara Munzner, 2014]
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    March 17, 2022 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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    March 17, 2022 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) [Visualization Analysis & Design, Tamara Munzner, 2014]
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    March 17, 2022 Error Rates Across Channels Results by Cleveland and McGill, 1984
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    March 17, 2022 Channel Effectiveness … ▪ Channel effectiveness mainly based on accuracy but also takes into account ▪ discriminability ▪ separability ▪ popout ▪ grouping
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    March 17, 2022 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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    March 17, 2022 Effective Line Width Use ▪ Limited number of discriminable bins ▪ line width works well for 3 or 4 different values
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    March 17, 2022 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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    March 17, 2022 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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    March 17, 2022 Popout …
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    March 17, 2022 Popout Channels ▪ Popout cannot only occur for colour hue and shape channels ▪ tilt (a) ▪ size (b) ▪ shape (c) ▪ proximity (d) ▪ shadow direction (e)
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    March 17, 2022 Popup Channels …
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    March 17, 2022 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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    March 17, 2022 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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    March 17, 2022 Relative Luminance Perception ▪ Perception of luminance is contextual based on the contrast with surrounding colours
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    March 17, 2022 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 [Visualization Analysis & Design, Tamara Munzner, 2014]
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    March 17, 2022 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 for small regions
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    March 17, 2022 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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    March 17, 2022 No Implicit Order for Hue ▪ Sometimes learned hue orders (not at perception level) ▪ green-yellow-red from traffics lights ▪ rainbow colour ordering
  32. Beat Signer - Department of Computer Science - [email protected] 32

    March 17, 2022 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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    March 17, 2022 Colourmap Categorisation (Taxonomy) [Visualization Analysis & Design, Tamara Munzner, 2014]
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    March 17, 2022 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
  35. Beat Signer - Department of Computer Science - [email protected] 35

    March 17, 2022 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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    March 17, 2022 Ineffective Categorical Colourmap Use
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    March 17, 2022 Example of Using Additional Channels ▪ Dataset with 27 categorical levels from 7 categories
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    March 17, 2022 Example of Using Additional Channels …
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    March 17, 2022 Example of Using Additional Channels …
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    March 17, 2022 Example of Using Additional Channels …
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    March 17, 2022 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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    March 17, 2022 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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    March 17, 2022 Rainbow Continous Colourmaps ▪ Problems of rainbow continous colourmaps ▪ hue is used 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)
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    March 17, 2022 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)
  45. Beat Signer - Department of Computer Science - [email protected] 45

    March 17, 2022 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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    March 17, 2022 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 (e.g. diverging red-green colourmap) ▪ Use colour blindness simulators and tools such as Viz Palette
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    March 17, 2022 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
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    March 17, 2022 Tilt/Angle Channel ▪ Encode magnitude information based on the orientation of a mark ▪ tilt: orientation against the global frame of the display ▪ angle: orientation of a line with respect to another line ▪ Accuracy of our perception of a tilt/angle is not uniform ▪ very accurate near exact horizontal, vertical or diagonal positions
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    March 17, 2022 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 (strong popout) - hard to ignore and should be used carefully
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    March 17, 2022 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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    March 17, 2022 Exercise 5 ▪ Visualisation in Python
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    March 17, 2022 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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    March 17, 2022 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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    March 17, 2022 References … ▪ ColorBrewer 2.0 ▪ https://colorbrewer2.org ▪ Viz Palette ▪ https://projects.susielu.com/viz-palette
  55. 2 December 2005 Next Lecture Data Processing and Visualisation Frameworks