6 Perceptual issues Thanks to Colin Ware, John Stasko, Robert - - PowerPoint PPT Presentation

6 perceptual issues
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6 Perceptual issues Thanks to Colin Ware, John Stasko, Robert - - PowerPoint PPT Presentation

Elective in Software and Services (Complementi di software e servizi per la societ dell'informazione) Section Inf nfor ormat ation V on Visual sualizat ation on Numbers of credit : 3 Gius usep eppe pe S Sant antucci 6


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Elective in Software and Services (Complementi di software e servizi per la società dell'informazione) Section Inf

nfor

  • rmat

ation V

  • n Visual

sualizat ation

  • n

Numbers of credit : 3

Gius usep eppe pe S Sant antucci

6 – Perceptual issues

Thanks to Colin Ware, John Stasko, Robert Spence, Ross Ihaka, Marti Hearst, Kent Wittemburg

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Outline

  • Visible Light & Eyes
  • Luminance/Brightness /Lightness
  • Colors
  • Color & Information Visualization
  • Pre-attentive processing
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Visible light and pure colors

  • Let’s start with the basic stuff : the visible light (for human beings)
  • It is a tiny part of the whole spectrum
  • The usual unit is the nanometer, 10-9 m, and the visible range is from 400 (violet)

to 700 nm (red)

  • Light consisting of a single wavelength is monochromatic light, which looks to the

eye as a pure color.

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Light sources

  • Usual light sources are not monochromatic and are

characterized by their spectral power distribution Sun spectral power distributions

A Incandescent light source D50 and D65 statistical representations of average day light B standard CIE definition of direct sunlight C standard CIE definition of average daylight

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Light, objects, and perceived colors

  • Perceived color of an object depends on the light source, the properties of

visual system, and how light interacts with the object

  • Light can be

– Refracted and then transmitted through it (if the object is transparent), – Absorbed by it (transforming in to heat) – Scattered inside the object if it is not completely transparent, because of the collision of photons with the molecules of the object (like sunlight scattering in the atmosphere) – Reflected

  • Color = reflected light (same of light source) + scattered (depends
  • n the object)
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The eye

  • it is like a camera (focal length = 17 mm)

– or is a camera like the eye?

  • high resolution only in the very little fovea area
  • two basic sensors rods and cones

Rods work only in low light Black and white Disabled with day light Cones work with high light Colors About 100.000 cones in the fovea 180 cones per degree

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Chromatic aberration

  • Different wavelengths are focused at different distance
  • If we use in the same image two far pure colors the eye is not

able to focus both of them

Red text is closer then blue text

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Visual angle (degrees, minutes, seconds)

θ=2 arctan (h/2d) 1 cm tall object viewed at 57 cm has 1 degree angle 57 cm is a good approximation of the distance at which we view a computer monitor That roughly corresponds to the fovea visual angle

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Simple acuity

  • Acuity : ability to

see details (pixels should be below our acuity)

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Spatial contrast sensitivity

Contrast Spatial Freq.

We need high contrast for both low and high frequencies

This allows for producing monitors with high difference in luminance that is not perceived (center till 30 % brighter than borders)

2 8 16 32 60

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Acuity falls off rapidly from fovea

1 3 5 1 3 5 D i s t a n c e f r

  • m

F

  • v

e a ( d e g . ) 1 8 6 4 2

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Scale Matters

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The optimal display

  • A modern display has about 40 pixels per cm (~100 dpi)
  • 1 cm at 57 cm of view corresponds to 1 degree
  • The fovea is equipped with 180 cones per degree and we

are are able to distinguish about 60 cycles per degree

  • And that (sampling theory) we need to sample at twice so,

120 pixels per cm should be enough (~300 dpi)

  • Neglecting superacuity a monitor should be be about

4000x4000

  • So we are still far away from a monitor good as our eyes

– But the iphone 4 with its Retina display?

  • 960x640 8x5cm  326 dpi…. But from 30 cm you need 600/dpi…
  • With 300 dpi you just makes pixels not distinguishable (no so bad…)
  • So why we have laser printers capable of 1200 dots per

inch (460 dots per centimeter)?

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1200 dpi laser printer ?

  • Aliasing. From a fundamental theorem of signal

transmission we know that we have to sample a signal at least twice the highest frequency

  • Aliasing occurs when we sample a regular pattern by

another regular pattern at different frequency

Cones in the fovea follow non regular patterns

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1200 dpi laser printer can help

  • Antialiasing Computing the average of the light pattern can

mitigate the problem in a cost-effective way than simply increasing the pixel number

  • It requires additional computation that further increases with

colors

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1200 dpi laser needed for gray !

  • The dots of a laser print are either black or white
  • So a pixel is made of several dots:

– A 16x16 dots matrix can implement a pixel with 256 gray levels

  • Square pixels are not used (aliasing again)
  • Patterns of dots are randomized
  • A 1200 dpi laser print is 1200 only for black & white
  • For a gray image it scales to about 120 dpi or less
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Superacuity and displays

  • Vernier acuity applies also at monitor lines
  • Appropriate antialising techniques result in a Vernier

acuity better than pixel resolution ! Vernier Super Acuity = 7.5 sec vs (3600/ 40)= 90 sec

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Receptive fields

  • Receptive field. Disregarding several detail we can

concentrate on the visual area that responds to light If the light is on the center the corresponding neuronal activity increases If the light is on the border the corresponding neuronal activity decreases The receptive field is inhibited

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The DOG function

  • This is typically

described with a Difference Of Gaussians (DOG) function

  • One Gaussian represent

the center

  • The other one the

border

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Receptive field

  • The maximum is when the center is

illuminated and the border is in the dark

  • It is a perfect edge detection function!
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A proof

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Less inhibition More inhibition

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Consequences to be aware of (DOG)

  • Simultaneous Brightness Contrast
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Consequences to be aware of (DOG)

  • Simultaneous Brightness Contrast
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Consequences to be aware of (DOG)

  • Simultaneous Brightness Contrast

Watch the DOG !

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Gray coding + Simultaneous Brightness Contrast can produce very large errors!

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Consequences to be aware of (DOG)

  • The Chevreul illusion

Adjacent pattern of different intensity create edges

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A perfect edge detection !

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Cornsweet effect

  • Suitable shading creates edges and difference in

lightness

  • What is the darker side?
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Cornsweet effect

  • No one…
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Be aware of DOG

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Be aware or use it, like Seurat

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Outline

  • Visible Light & Eyes
  • Luminance/Brightness /Lightness
  • Colors
  • Color & Information Visualization
  • Pre-attentive processing
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Luminance

  • Luminance is the amount of visible light coming from

a region of space or a surface

  • It is a physical value (measurable using a

photometer)

  • Unit: candelas per square meter
  • The human eye reacts differently to different

wavelengths

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The CIE V(λ) function

  • The CIE (Commision Internationale de L’Eclairage)

standardized the V(λ) function (averaging 200 people)

L = ∫ Vλ E λ δ λ

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Practical usage of luminance

  • Text background contrast: ISO and W3C specify a

minimum level of luminance difference (1:3)

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Brightness

  • Brightness refers to the perceived amount of light coming from a self-

luminous source

– E.g., nighttime instruments, stars, ship lights, etc.

  • It is not linear with luminance and the usage of the magnitude estimation

technique is quite popular:

– Subject are experimentally asked to indicate when a perceived sensation is twice then a reference one – Most physical sensation follow a simple power low:

S=aIn

– S is the sensation, a is a constant and the stimulus intensity I is raised to a power n

  • For large source of light (5 degrees) the law is:

Brighness = Luminance0.333

  • For point sources of light the law is:

Brighness = Luminance0.5

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A well known example : star magnitude

  • Magnitude 1 : the strongest stars visible in the sky (by Hipparco)
  • Magnitude 2 : stars showing ½ brightness of magnitude 1
  • ….
  • Magnitude 6 : the faintest stars that are visible at naked eyes
  • Hipparco et al. idea has been recently revised:
  • Magnitude 6 is now 100 times less bright of 1 not 64
  • So the real brightness ratio between two consecutive magnitudes was

about 2.5

  • But they did not have computers and photometers…
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Monitor gamma

  • Most visualizations are produced on a monitor
  • Nowadays computer allows for setting the gamma value (ranging

from 1.4 to 3.0)

  • The relationship between physical luminance and voltage is:

L=Vγ

  • A gamma value of 3 perfectly compensates the

Brightness = Luminance0.333 law resulting in a display characterized by a linear relationship between voltage and perceived brightness

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Lightness

  • Lightness refers to the perceived reflectance of a surface
  • A white surface is light
  • Black surface is dark
  • As brightness, lightness is not directly related to luminance
  • Some adaptive mechanism of the vision system allow for

perceiving lightness (as well as colors) in a constant way:

– A black object in a sunny day reflects about 1000 candelas per square – The same object in an office light reflects 50 candelas per square

  • It is still perceived as black

– A white paper in the same office reflects less light than the black

  • bject in the sun. Still perceived as white
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Adaptation

  • The iris of the eye opens and closes, modulating the

incoming light

  • More significant: photopigment is bleached under

high light and eyes became less sensitive

  • When moving from strong light to dark photopigment

is regenerated, but it takes time

  • That’s way entering in a dark room from a sunny day

makes you blind for several seconds

  • The fully regeneration process takes up to 30

minutes (as stars observers perfectly know…)

  • So the eye is not a photometer at all !!!
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Monitor illumination and gamma

  • Monitor are not used in dark

rooms

  • In normal usage about 15 – 40

% of light comes from the environment light and not from the monitor

  • We can take that in account

adding a constant to the gamma equation: L=A+Vγ

  • In such a case a better linear

relationship between Luminance and V is obtained with a lower gamma

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Outline

  • Visible Light & Eyes
  • Luminance/Brightness /Lightness
  • Colors
  • Color & Information Visualization
  • Pre-attentive processing
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Trichromacy Theory

  • We have three kind of cones,

sensible to different wavelengths LMS (Long, Medium, Short)

  • That’s is the reason why we

generate colors using three primaries (e.g., RGB)

  • Chickens engineers will became

crazy in designing a monitor with 12 colored phosphors

  • LMS cones spectral

sensitivity curves

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Color blindness

  • About 7% of the male population and 1% of female population

suffer from some color vision deficiency

  • That correspond to a lack of one (most cases) of the three

receptors

  • A better comprehension of the matter allows for designing

systems usable by both trichromats and dichromats

Missing L Missing M Missing S Color simulation

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Color measurement

G + B + R

  • We are able to match any color

with a mixture of no more than three light (called primaries) C Ξ rR+gG+bB

  • Where Ξ denotes that the

equation refers to a perceptual match

  • Two different spectral color

distributions look the same (metamers) if they stimulate the three cones in the same way

  • No physical reason but perceptual
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Change of primaries

  • The amount of RGB required to “generate” a color

has been determined by CIE through several experiments performed in 1931 that produced several standards

  • The problem with RGB is that they are not able to

cover all the perceivable colors and different primaries have been chosen for that

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The CIE X,Y, and Z color space

  • They selected three abstract primaries able to represent a wide

color space that includes the visible color gamut (in gray)

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The CIE chromaticy diagram

  • Being the X,Y, and Z space

difficult to understand they defined a transformation from X, Y, and Z to a new coordinate system x,y, and z that, under the constraint that x+y+z=1, i.e., normalizing with respect to the amount of light, can be represented in a two dimensional space

  • Colors on the border are

“pure”colors

  • The distance from the “white

point” give the color saturation

  • The triangle represent the

RGB space

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Some useful properties

  • Given two colors, all the colors given by their mixture

lie on the connecting line

  • It is easy to specify a set of three primaries on it
  • The border represents monochromatic lights
  • The white has 0.333, 0.333, 0.333 coordinates
  • The complementary of a color is produced by

drawing a line between that color and the white and extrapolating to the opposite locus. The mixture of the two colors produces white

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Color opponent process theory

  • The Hearing idea (1920) is that there are only 6 elementary

colors arranged in three pairs

– Black-White (luminance channel) – Red-Green – Yellow-Blue

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Scientific evidence

  • Naming: yellowish green or greenish blue are often

used; reddish green or yellowish blue NOT

  • Cross-cultural naming: an anthropological study on

more than 100 languages shows that color naming definition order is the same as follows

white black green yellow green blue brown pink purple

  • range

grey red yellow

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Outline

  • Visible Light & Eyes
  • Luminance/Brightness /Lightness
  • Colors
  • Color & Information Visualization
  • Pre-attentive processing
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Main learned issues

  • Color Perception is Relative
  • Sensitive to Small Differences

– hence need sixteen million colors

  • Not Sensitive to Absolute Values

– hence we can only use < 10 colors for coding

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Color = Classification

Rapid Visual Segm entation Color helps us to determ ine type

white black green yellow green blue brown pink purple

  • range

grey red yellow

Only about six categories

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Color for labeling

  • Distinctness
  • Unique hue (red, green, yellow, blue, black and white)
  • Contrast with background
  • Color blindness (mainly red-green direction)
  • Conventions
  • The bottom line 6 + 6 = 12

12 Colors for labeling

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Color sequences for maps

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The revised gravity gray map

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Outline

  • Visible Light & Eyes
  • Luminance/Brightness /Lightness
  • Colors
  • Color & Information Visualization
  • Pre-attentive processing
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Pre-Attentive Processing

  • Some Visual Properties Processed Pre-Attentively

– No need to focus attention

  • Pre-Attentive Properties Important

for Design of Visualizations – Can be perceived immediately

  • < 200 - 250ms

– Eye movements = at least 200ms – Some processing can be done very quickly  Implies low-level processing in parallel

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Pre-Attentive Experiment

3 6 1 2 N u m b e r

  • f

d i s t r a c t

  • r

s 5 7 9

  • Number of irrelevant items (distractors)

varies

  • Pre-attentive 10 msec per item or better.
  • Decision = Fixed Time

regardless of the number of distractors

 Preattentive

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Segmentation by Primitive Features

  • How many areas ?
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Pre-Attentive Processing - lightness

  • How many 3s ?

8 2 8 8 5 8 8 3 8 2 8 9 8 5

  • 8

2 8 8 5 6 7 8 4 7 2 9 8 8 7 2 t y 4 5 8 2 2 9 4 7 5 7 7 2 2 1 7 8 9 8 4 3 8 9 r 4 5 5 7 9 4 5 6 9 9 2 7 2 1 8 8 8 9 7 5 9 4 7 9 7 9 2 8 5 5 8 9 2 5 9 4 5 7 3 9 7 9 2 9

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Pre-Attentive Channels

  • Form

– Orientation – Size / Length – Curvature – Spatial grouping

  • Color

– Hue – Intensity

  • Motion

– Blinking – Direction of motion

  • Spatial/Position

– 2D position – Stereoscopic depth – Convex/concave for shading

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Pre-Attentive Processing - Curvature

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Pre-Attentive Processing - Shape

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Pre-Attentive Processing - Color

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Pre-Attentive Processing - Enclosure

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Pre-Attentive Processing - Size

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Pre-Attentive Processing - Motion

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Pre-Attentive Processing - Orientation

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Pre-Attentive Processing - Simple

shading

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Pre-Attentive – Summary

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Pre-Attentive Conjunctions

  • Position + Color
  • Size + Color
  • Position + Shape
  • Stereo + Color
  • Color + Motion
  •  Spatial location + some aspect of form
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Color+size conjunction (does not pop out)

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Compound features (do not pop out)

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Pre-Attentive Demo

  • Pre-Attentive Demo by Christopher Healey
  • Target = Red square
  • Distractors

– blue circles (color search) – red squares (shape search) – blue circles and red squares (conjunction search)

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Go!

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Example: Conjunction of Features

With conjunction encoding the red square is not pre-attentively identified.

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A simple example: A symbol set for a tactical map

  • Aircrafts
  • Tanks
  • Buildings
  • Soldiers
  • +
  • Stress during decision
  • Each item can be classified as friendly or hostile
  • Some items exists whose presence is just suspected

but not confirmed

  • Terrain
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A symbol set for a tactical map

  • Aircrafts Tanks Buildings Soldiers

– Different using pre-attentive shapes

  • Each item can be classified as friendly or hostile

– Labeled with two colors

  • Some items exists whose presence is just suspected

but not confirmed

– Made different with pre-attentive enclosure

  • Terrain

– Rendered through simple shapes and colors

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Example

Building Aircraft Soldiers Tank Non confirmed Friendly Hostile River Plain Mountain

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Interpretation of Bertin’s guidance regarding the suitability of various encoding methods to support common tasks

The marks are perceived as PROPORTIONAL to each other

Association Selection Order Quantity Size Value Texture Colour Orientation Shape

The marks can be perceived as SIMILAR The marks are perceived as DIFFERENT, forming families The marks are perceived as ORDERED

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The relative difficulty of assessing quantitative value as a function of encoding mechanism, as established by Cleveland and McGill Length Position Angle Slope Area Volume Colour Density Most accurate Least accurate

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Mackinlay’s guidance for the encoding of quantitative, ordinal and categorical data

Quantitative

Position Length Angle Slope Area Volume Density Shape

Ordinal

Position Density Colour saturatio Texture Connection Containment Length Angle Slope Area Volume Colour hue

Categorical

Position Colour hue Texture Connection Containment Density Colour saturatio Shape Length Angle Slope Area Volume

Treble Bass