http://idi.kennesaw.edu/it7113/


https://www.edocr.com/v/631d1wpb/jgzheng/SCOPeS-Visual-Properties

Visual Encoding with
SCOPeS Visual Variables/Properties
Jack Zheng
Spring 2025
IT 7113 Data Visualization
http://idi.kennesaw.edu/it7113/
©
https://www.edocr.com/v/631d1wpb/jgzheng/SCOPeS-Visual-Properties
Visual Properties: SCOPeS
 Visual variable or property is the “decoration” applied to visual
elements to represent data values
 A visual property is used to encode different values of a particular
dimension of data
 There are six basic visual properties - easier to be remembered as
“SCOPeS” (Dr. Jack’s term)
Size
Color
Orientation
Position
Texture
Shape

https://infovis-wiki.net/wiki/Visual_Variables


https://www.amazon.com/dp/1589482611

Bertin’s Original Version
https://infovis-wiki.net/wiki/Visual_Variables
Semiology of Graphics: Diagrams, Networks, Maps
1st Edition, by Jacques Bertin,
https://www.amazon.com/dp/1589482611

https://dwbi.org/pages/18

Data Characteristics
 Data encoding or mapping is determined both by the kind of visual
property and the type of data. The encoding process is the fit of the
two.
 The value of a data item is mapped to the value of a particular
visual property based on the types of data
 Some properties can be more effectively represent values of certain
data types than others
Continuous
quantitative data
Numerical values. Example: sales amount, age, height,
etc. usually they can be aggregated (sum, average, etc.)
Ordinal data
Discrete data, but with an order; often qualitative (for
example month in a calendar year) but can be
quantitative (ranked or ranged, like age groups/intervals).
Nominal data
The data is a collection of non-numerical and non-
ordered data (discrete): categorical. Example:
departments in the college
Expanded readings on data characteristics https://dwbi.org/pages/18
Visual Property: Size
 Size is a physical measures of the visual element like length, width,
height, area, angle, quantity of items, etc. It is commonly used for
continuous data values.
 Examples
 Scaling issue
 For various reasons, it is common that the size property does not directly
and truly represent the underlying value. In these cases, it must be very
careful to design the size property, because unreasonable distortions will
impact human perception.
Person symbol height represents
number of years
Area size represents
percentages

http://learn.leighcotnoir.com/artspeak/elements-color/hue-value-saturation/


https://blogs.ifgi.de/digital-cartography/symbols/color-and-color-schemes/


https://en.wikipedia.org/wiki/HSL_and_HSV

Visual Property: Color
 Color is the most common visual property that can be used for both
dimensions (categorical) and measures (continuous or discrete)
 Color has three major properties (HSV)
 hue (color spectrum)
 saturation
 brightness (value)
 Extended reading:
 http://learn.leighcotnoir.com/artspeak/elements-color/hue-value-
saturation/
 Color and color schemes: https://blogs.ifgi.de/digital-
cartography/symbols/color-and-color-schemes/
 https://en.wikipedia.org/wiki/HSL_and_HSV

http://en.wikipedia.org/wiki/Pie_chart


https://www.theinformationlab.co.uk/2014/12/03/geospatial-heat-maps-tableau-via-alteryx/


https://covid.cdc.gov/covid-data-tracker/#global-counts-rates


https://gasprices.aaa.com/


https://spectrum.adobe.com/page/color-for-data-visualization/

Color coding based on data characteristics

Using colors based on data type: categorical and
continuous
 Categorical data (dimensions or measures) – using hues

Represent and differentiate individual data item -
http://en.wikipedia.org/wiki/Pie_chart (colors in pie chart
commonly represent countries, which is nominal or
categorical) – however, color is ineffective if there are more
than 7 items or categories.
 Group a set of similar items or data points (dimensions or
measures)
 Highlight (dimensions or measures): alert, distinguish, etc.
 Continuous data (measures) – using values or saturations

Sequential system: using one color’s value range
 Diverging system: two color gradient (or step) system, often
used in heat maps where two colors represent opposite
directions. (Example:
https://www.theinformationlab.co.uk/2014/12/03/geospatial
-heat-maps-tableau-via-alteryx/)
 Gradient or stepped:
 gradient is a smooth transition, often used for continuous
measure
 Stepped usually categorize measures in ranges and uses
more discrete color values, https://covid.cdc.gov/covid-
data-tracker/#global-counts-rates;
https://gasprices.aaa.com/
 More references

https://spectrum.adobe.com/page/color-for-data-
visualization/

https://citoolkit.com/articles/traffic-light-assessment/


https://en.wikipedia.org/wiki/Heat_map


https://weatherboy.com/north-america-chills/


https://www.ometrics.com/heat-maps/


https://andyarthur.org/thematic-map-county-population-density.html


https://cambridge-intelligence.com/choosing-colors-for-your-data-visualization/


https://www.dataquest.io/blog/what-to-consider-when-choosing-colors-for-data-visualization/


https://colorbrewer2.org/

Color Systems, Cases and Best Practices
 Traffic light system in performance management
 Uses green, yellow, and red to represent different levels of performances (good,
warning, bad); three colors corresponds to target values; it is a hue-based system
used ranged measures.
 https://citoolkit.com/articles/traffic-light-assessment/
 Temperature system in maps (heatmap) – a bipolar hot/cold system: red for hotter
temperature and blue for colder
 Heat maps (https://en.wikipedia.org/wiki/Heat_map) use color to represent target
values, either continuous or in groups/ranges (ordinal)
 Originally used for data related to temperature and weather
https://weatherboy.com/north-america-chills/
 Also for data that has a temperature metaphor, such as click map
(ttps://www.ometrics.com/heat-maps/), population density
(https://andyarthur.org/thematic-map-county-population-density.html), lighting,
salary, etc.
 More best practices and examples of using colors in data visualization:
 https://cambridge-intelligence.com/choosing-colors-for-your-data-visualization/
 https://www.dataquest.io/blog/what-to-consider-when-choosing-colors-for-data-
visualization/
 https://colorbrewer2.org (a good color choice tool)

https://blog.datawrapper.de/beautifulcolors/


http://www.dataatworkbook.com/data-work-03-beyond-visual-perception/


https://www.gasbuddy.com/GasPriceMap

Other considerations when selecting color
 Color symbolism
 Match the color of visual element to the
real-world counterpart (things or
phenomenon) it represents.
 CI color: use the color consistent with
the company image to represent the
company or organization.
 Reasonably consider aesthetics that
contributes to human’s emotional
perception
 temperature - warm vs. cool
 harmony - sharp vs. soft
 Use beautiful colors:
https://blog.datawrapper.de/beautifulc
olors/
 Color blindness
 Prepare for a second alternative, usually
using textual instead of color.
http://www.dataatworkbook.com/data-work-
03-beyond-visual-perception/
Mismatch of natural color of the
vegetable with the line color that
represents the vegetable.
Color represents gas prices. Is yellow
cheaper or more expensive?
https://www.gasbuddy.com/GasPriceMap
Which color system is better in the following
charts? Any other better designs?
0
1
2
3
4
Emily
Rachel
Joy
Roger
GPA
0
1
2
3
4
Emily
Rachel
Joy
Roger
GPA
0
1
2
3
4
Emily
Rachel
Joy
Roger
GPA
Hue based color coding for each person – not
necessary, as each person is represented by
position. Color may be used to represent a
second dimension, like school or class.
Value based color coding for each
person – not recommended same
reason as the one on the left.
Unified color
(color has no
specific meaning)
0
1
2
3
4
Emily
Rachel
Joy
Roger
GPA
Can we use value-based color
coding to represent GPA values?
I.e. darker color for higher GPA?
Generally not needed, as GPA is
represented by size of the bars.

http://voyager8.blogspot.com/2014/01/the-historical-relationship-between.html

Visual Property: Orientation
 Orientation is about direction. It can be seen as variations of a
particular shape or pattern pointing to different directions. It can
also be seen as a variation of position (pointing to different positions)
 A common example is the use of arrows or hands pointing to
different directions.
 Examples
 Gauge chart
http://voyager8.blogspot.com/2014/01/the-historical-
relationship-between.html

http://www.gartner.com/technology/research/methodologies/research_mq.jsp


http://en.wikipedia.org/wiki/Cluster_analysis

Visual Property: Position
 Position refers to the location where a data item is
placed. Data values can be visualized as absolute
positions in the visualization, or as the relative
distance between elements.
 Position is commonly used in
 Dot plot – a variation of column chart
 Strip plot (one-dimension scatter plot)
 Scatter plot, or any two-dimensional space
 Maps (geo maps, or any spatial diagrams, etc.)
 Examples
 http://www.gartner.com/technology/research/meth
odologies/research_mq.jsp (top left chart)
 http://en.wikipedia.org/wiki/Cluster_analysis (bottom
left chart)
Visual Property: Texture
 Texture is used much like colors but seldom used for continuous
data.

It is important when color sensitivity is an issue. Implementations
include fill patterns, border patterns, shadow, etc.
 Examples

http://www.masters.com/en_US/scores/


http://v8doc.sas.com/sashtml/gref/z15-ex.htm

Visual Property: Shape

Type of shapes
 Shapes can be formed using simple shapes: square, triangle, etc.
 More complex shapes also can be formed by combination of simple shapes: icon, marker, etc.
 Shapes are usually used to represent different type of things, or nominal/discrete data
(e.g., category). Shapes are rarely used for continuous data.
 Shapes are usually used for dimensions but sometimes used for measures as well.
 Examples
 http://www.masters.com/en_US/scores/ - in this example, shapes are used for measures (ordinal)
actually
 http://v8doc.sas.com/sashtml/gref/z15-ex.htm
- shapes are used for species

https://www.linkedin.com/feed/update/activity:6464536253118373888/


http://metrocosm.com/global-immigration-map/


http://www.storybench.org/role-motion-visualizations/


https://www.gapminder.org/tools/#$chart-type=bubbles&url=v1


https://www.digitalattackmap.com/

Motion - on Top of SCOPeS
 Motion is the movement or transformation of the basic visual properties, which can represent
richer meanings and grab greater attention.
 Dynamic change of positions, shape, color, orientation, texture, size
 Note: simply animating an object in the visualization can just be decorative, but not data driven
 Examples:

Simple position change: move one position to the other
 Growing or shrinking size, and/or accompanies by shape shifting
 Moving direction (of position change): used like orientation

Flickering/blinking/flashing/spinning pattern: can be used like color or texture for categorical data

Speed (of movement and changing) can be added as additional properties in motion driven
visualizations

Typical usages and examples

Show trends along a period of time -
https://www.linkedin.com/feed/update/activity:6464536253118373888/
 Operational process simulation: e.g., disk optimization
 http://metrocosm.com/global-immigration-map/
 http://www.storybench.org/role-motion-visualizations/
 https://www.gapminder.org/tools/#$chart-type=bubbles&url=v1
 https://www.digitalattackmap.com
We do not cover motion in depth in IT 7113, but it can be a very good research topic for class project.
Combinations of Multiple Properties
 Combinations of these properties can be used to represent multi-
dimensional data in the same visualization.
 Example: in a scatter plot, one product can be represented as an
object with small (size) square (shape), red color (color), and
double-line border (texture).
 Size: representing sales amount
 Shape: representing product category
 Color: representing profit/loss
 Line/border textual: representing sales channel
 More complex visual elements (such as icons and symbols) can be
built based on the basic elements and properties discussed above.

https://infovis-wiki.net/wiki/Visual_Variables

Visual Properties Choice
 Choose appropriate visual properties to code data based on data
types
Refer to https://infovis-
wiki.net/wiki/Visual_Variables
The shaded box indicates
most inappropriate.
Best choice on top
and then less
appropriate toward
the bottom. For
example, position
property can be used
to code all types of
data really good; while
area is somewhat good
for quantitative data,
but it is not good for
ordinal and nominal
data.
For example, do not use shape for ordinal
type of data, like month, or class level, etc.
Types of Data
• Choose one visual property to represent one dimension. E.g., use colors for
products.
• Use different visual properties, instead of different values of the same visual
property, for different dimensions (or dimension attributes). E.g. if colors are used for
products, then do not use color for countries again. Choose position or shape for
countries.
Visual Properties used for
Dimensions and Measures
*
Occasionally, only in specific cases
**
Sometimes, depending on more factors
***
Very often and appropriate
Measure
(continuous)
Measure
(ordinal)
Dimension
(categorical: ordinal or
nominal)
Shape
X
*
***
Color (Hue)
*
*
***
Color (Value)
**
**
*
Orientation
**
**
**
Position
***
**
**
Texture
X
*
**
Size
***
**
*
Exercise: which visual coding method is better?
VS.
VS.
 Representing
product
categories
 Representing
population

https://www.bloomberg.com/news/newsletters/2023-10-30/the-us-could-become-the-odd-market-out-in-the-ev-success-story

Improper Color Coding
https://www.bloomberg.com/news/newsletters/2023-10-30/the-us-could-become-the-
odd-market-out-in-the-ev-success-story

https://infovis-wiki.net/wiki/Visual_Variables


https://fastercapital.com/content/Visualization-Techniques--Visual-Encoding--Translating-Data-into-Visual-Language.html


http://wiki.gis.com/wiki/index.php/Visual_variable


https://prism.ucalgary.ca/handle/1880/45758


https://www.oreilly.com/library/view/designing-data-visualizations/9781449314774/ch04.html


https://www.e-education.psu.edu/geog486/node/637


https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118786352.wbieg0761


https://www.amazon.com/dp/1589482611

Readings and Resources
 Key readings
 Visual variable/property basics https://infovis-wiki.net/wiki/Visual_Variables
 Visualization Techniques: Visual Encoding: Translating Data into Visual
Language https://fastercapital.com/content/Visualization-Techniques--Visual-
Encoding--Translating-Data-into-Visual-Language.html
 Additional articles and books
 http://wiki.gis.com/wiki/index.php/Visual_variable
 Considering Visual Variables as a Basis for Information Visualisation,
https://prism.ucalgary.ca/handle/1880/45758
 Choose Appropriate Visual Encodings
https://www.oreilly.com/library/view/designing-data-
visualizations/9781449314774/ch04.html
 For maps https://www.e-education.psu.edu/geog486/node/637
 Roth, R. E. 2017. Visual Variables. The International Encyclopedia of
Geography.
https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118786352.wbieg0761
 Semiology of Graphics: Diagrams, Networks, Maps 1st Edition, by Jacques
Bertin, https://www.amazon.com/dp/1589482611