Semiology Of Graphics Supporting tagline
WORK IN PROGRESS..
Preliminary note: This material is based on the important and fundamental Jacques Bertin’s book: “Semiology of Graphics”
By Semiology of Graphics, we refer to the theoretical framework describing the graphic means and techniques we can use to represent in an effective way the information to be shown on graphical charts and maps. The purpose of a graphical chart or map is to offer a synoptic view of a phenomenon. We want to perceive at a glance the structure of the data (extreme values, spatial distribution, relationships, …) usually stored in tables.
To illustrate this point, compare two different views of the same information:
latitude longitude Be
-33.56367896 -66.0919045 498.4863237
-33.56322856 -66.09191961 396.0934873
-33.56277816 -66.09193472 351.9073377
-33.56232777 -66.09194983 365.0816412
-33.56187737 -66.09196494 256.6173953
-33.5636663 -66.09136651 478.2634187
-33.56321591 -66.09138162 476.9797941
...
.
with circles area proportional to the Beryllium 7 concentration.
Different graphical means from text to figurative symbols, colors, size variations, … allow to read an image at different levels: elementary, intermediate and overall. We will discute this point as it relates to the notion of efficiency of visualization based on the message we want to create and to send.
1. Definitions
1.1 Components
Components: are variational concepts we want to represent, for instance population of world countries, concentration of radionuclides at measurements points, landuse, vegetation type, erosion level, …
As variational concepts, components are, by definition, divisible. One may speak of elements, categories, classes, values.
1.1.1 Length of components
Length of components: refers to the number of divisions that it enables us to identify.
Length of a component can be:
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Short or limited: when length does not exceed four. For example binary components like sex (male or female) have a length of two; level of erosion might can be categorized in low, middle, high.
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Long or extensive: when length exceeds some fifteen divisions. The term length can be applied to quantitative series when the latter is divided into steps or classes.
On the other hand, a series of numbers can be infinitely divisible, the term length is no longer applicable. We talk about the range of a quantitative series as the ratio of the smallest to the largest number. When we consider that the practical range of a visual variation in size is limited (it cannot decrease below a ration of 1 to 10 without loosing the greatest part of its efficiency), thus it is easy to understand the importance of this notion and of “the range adjustment” (showing square of values for instance instead of values themselves). This point will be discuss later on.
1.1.2 Level of organization of components
Level of organization of components can be:
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qualitative/nominal: when categories are not ordered in a universal manner (type of vegetation, measurement type, …). Faced with qualitative concept, the observer can adopt two perceptual approaches: a selective approach (difference -where is a given category-) engendered by question of an elementary or intermediate reading level; an associative approach (similarity -where is a given component, the forest for instance, all categories of trees combined) engendered by questions of an intermediate or overall reading level.
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ordered: when its categories are ordered in a single and universal manner. For instance the components “age” or “level of erosion -low-middle-high”. As qualitative components, they are equidistant: all categories have equal importance, there are no reason to disturb that quality by highlighting a particular category or creating a priori groups of categories.
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quantitative: a series of numbers is quantitative when its object is to specify the variation in distance among the categories.
1.2 Visual/retinal variables
To show, to portray these components on graphs or maps, the graphic designer has at its disposal essentially marks (for us mark is a set of pixels). The purpose of the semiology of graphics is to study the correspondence between graphic marks and components based on their respective properties.
How can we vary marks?
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by where we place them;
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by how we place them (implantation: point, line, area)
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by their visual characteristics (retinal variables)
Marks can vary in:
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position
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size
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value
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texture
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color
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orientation
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shape
2. Properties of visual variables
We will go through each visual variables and study their characteristics (level of organization, perceptual properties).
Properties of visual variables can be:
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Selective: is a change in this variable enough to allow us to select it from a group?
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Associative: is a change in this variable enough to allow us to perceive them as a group?
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Quantitative: is there a numerical reading obtainable from changes in this variable?
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Order: are changes in this variable perceived as ordered?
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Length: across how many changes in this variable are distinctions perceptible?
2.1 Visual variable: Position
2.2 Visual variable: Size
- Size variation over various shape and implantations:
- Size variation can be achieved through: height of a column, area of a sign, number of equal signs.
- Size variation on three implatations: points, lines and areas
2.3 Visual variable: Shape
- Variation in shape at constant size is completely unefficient in rendering quantitative components. You need to refer constantly to the legend to have an idea of quantities.
- Shape variation on three implatations: points, lines and areas
- When the density of marks increases, selectivity and associativity decrease radically.
2.4 Visual variable: Value
- Value variation is achieved through various degrees of white and black
- Value variation on three implatations: points, lines and areas
- Value variation is ordered and cannot be re-ordered
- Value variation is not quantitative (here oil consumption in Europe base unit 1 million tons)
- Value intensity can be mis-read as density when areas vary (here population of Paris)
2.5 Visual variable: Colour
- Very good selectivity and associativity
- Not efficient when used to visualize quantitative data. Need to constantly refer to legend
- Colour variation on three implatations: points, lines and areas
- Issue with the frequently used rainbow palette. The eyes order using values not colours!
2.6 Visual variable: Orientation
- Here variation is line or line-pattern ranging from horizontal to vertical
- Colour variation on three implatations: points, lines and areas