Visual Encoding with
SCOPeS Visual Variables/Properties
Jack Zheng
Spring 2023
IT 7113 Data Visualization
http://idi.kennesaw.edu/it7113/
©
https://www.edocr.com/v/631d1wpb/jgzheng/SCOPeS-Visual-Properties
https://www.edocr.com/user/jgzheng/collection/datavisualizationlecturenotes
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
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
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 t