The Semiology of Graphics
(1918-2010)
• French cartographer
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• : Les Diagrammes, Les Reseaux , Les Cartes, Mouton, 1967
• Semiology of Graphics: Diagrams, Networks, Maps (English translation by W.J.Borg), University of Wisconsin Press, 1983
• Mostly interested in the depiction of symbols on maps, but his framework also covers a range of diagrams, networks and data charts
• References
– Monmonier, M. (1983) Mapping It Out: Expository Cartography for the Humanities and Social
Sciences, Chicago Guides to Writing, Editing, and Publishing.
– Bertin, J. (1983) Semiology of graphics: Diagrams, networks, maps. University of Wisconsin
– Roth, R.E. (2017) Visual Variables. The International Encyclopedia of Geography, & Sons, Ltd.
– Carpendale, M.S.T. (2001) Considering Visual Variables as a Basis for Information Visualisation. University of Calgary Technical Report, https://prism.ucalgary.ca/handle/1880/45758 .
– Treisman, A. (1985) Preattentive processing in vision, Computer Vision, Graphics, and Image Processing, 31(2), pp156-177
Semiotics (in brief)
• Visualisation facilitates communication between people
• Visualisation therefore is a visual language
• Like all languages, it has tokens (words, signs) and rules describing how the tokens can legitimately be combined (syntax)
Semiotics is the study of signs and how they convey meaning
Signs can be:
The nature of signs
– symbols: there is no perceptual relationship beween the object and what it is meant to represent (arbitrary)
– icons: there is a clear perceptual relationshop between the object and what it is meant to represent (non-arbitrary)
“An absolute boundary between symbols and icons is
illusory because as soon as a symbol’s meaning has been
learned it will become a meaningful image”
(Sutcliffe (2013), Human-Computer Interface Design, pg164)
• Bertin defined a set of “visual variables” • The various ways a visual object can be
displayed (and therefore perceived) • Independent of each other
• Reducing the map/visualisation into its constituent graphical symbols, for critical analysis
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Bertin’s Visual Variables
• Location variables (position, relative to a coordinate frame)
– e.g. horizonal and vertical axes on a scatterplot; longitude and latitude on a map
– (so fundamental to presenting map information that these variables are often ignored in cartography)
• Retinal variables (perceptual properties)
– ways of representing differences between objects
– size, shape, colour (hue), colour (value), texture, orientation
This separation makes clear the difference between the spatial relationships between symbols and the perceptual properties of the symbols themselves
• Location variables
– fix a ‘graphic mark’ (symbol, visual object) on to a position on the plane
• Retinal variables
– ‘elevate’ that mark with a different ‘pattern of light’
, Mapping It Out (1993)
Bertin, Sémiologie Graphique (1967)
Deux Dimensions du Plan Taille
Couleur Orientation Forme
Two Dimensions on the Plane Size
Colour (value)
Colour (hue) Orientation Shape
The Six Retinal Variables
• Shape: (e.g. square, circle, star)
• Size: (e.g. measured in mm or pixels)
• Orientation: angle of most prominent axis in the symbol to the coodinate axes (e.g. 36o,218o)
• Texture: spacing between repeated elements of a symbol (e.g. fine, coarse)
• Hue:colour,asassociatedwithwavelength(e.g.blue, green, turquoise)
• Value: depth of colour, as associated ink density and represented by greyscale (e.g. red ink with low value will be perceived as pink)
, Mapping It Out (1993)
Carpendale, M.S.T. (2001) Considering Visual Variables as a Basis for Information Visualisation.
Pre-attentive processing
Visual variables are recognised immediately – “pre-conceptually”
– at a sensory level (rather than cognitive level) – “seen” rather than “understood”
• Often called “pop-out” (Treisman, 1985)
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Associative variables (Bertin)
All variations are perceived equally (location, shape, orientation, colour hue, texture)
No colour is seen as more prominent than another
No shape is seen as more prominent than another
Allows for other variations to be noticed (e.g. different colour values)
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Dissociative variables (Bertin)
One variation dominates (size, colour value)
The eye is drawn to the darker colour values
Larger sizes are seen as more dominant than smaller ones
Variation in other variables is likely to be overlooked (e.g. different colours)
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Selective variables (Bertin)
It is possible to focus on the variations of the variable, despite variations in other variables
(only shape is not selective)
Easy to see the distribution of red circles, despite location changes
Not so easy to see the distribution of hexagons, even though they are distributed in the same way as the red circles above
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Ordered perception (Bertin)
Variations are perceived as being ranked in order (location, size, colour value)
Green is not seen as ‘more’ than purple or red
Darker circles are seen as ‘more’ than the lighter ones
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Quantitative perception (Bertin)
(an extension of ordered perception)
The variation can be quantitatively estimated (location, size)
Darker circles are seen as ‘more’ than the lighter ones, but it is difficult to estimate ‘how much’ more
It is possible to estimate ‘how much’ more the larger circles represent in comparison with the smaller ones
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Using the variables Unordered (colour hue, orientation, shape, texture)
for nominal information: apples, oranges, pears Ordered, non-quantitative (colour value)
for ordinal information: rainfall map
Ordered, quantitative (location, size)
for numerical information: electricity usage
(also good for non-quantitative and nominal information given their visual dominance)
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
Data attributes
• Categorical/ “nominal” – no implicit order
– African countries
– implicit order
– non-numerical
– days of the week
• Quantitative
– implicit order
– numerical
– runners’ finishing times
From “Visulisation Analysis & Design” , T. Munzner, , CRC Press, 2015, (Chapter 2)
Visual variables and data attributes
Y=yes N=no
G=good M=marginal P=poor
. Roth, Visual Variables, The International Encyclopedia of Geography (2017) derived from Bertin (1967/1983), MacEachren (1995), and MacEachren et al. (2012).
Aside: “pop-out”
Is there an order for which some variables are pre-attentively more prominent than others?
based on: A. Treisman, Preattentive processing in vision, 1985.
Pop-out examples follow…
Orientation
What if there are two variations?
Colour vs orientation
Shape vs orientation
Texture vs orientation
Texture vs shape
Extensions to Bertin’s Visual Variables
• Morrison (1974)
– colour saturation, arrangement
– particularly for cartographic purposes
• MacEachren (1995)
– crispness, resolution, transparency
– variations enabled by digital manipulation
(see Roth for details)
. Roth, Visual Variables, The International Encyclopedia of Geography (2017)
The Semiology of Graphics
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