Database Reference
In-Depth Information
3
Information Theory
3.1
Visual Communication
Information visualization can be viewed as a communication channel from a dataset
to the cognitive processing center of the human observer. This suggests that it might
be possible to employ concepts from the theories of data communication as a mecha-
nism for evaluating and improving the effectiveness of information visualization
techniques. While there are several early papers that tried to establish linkages be-
tween HCI in general to information theory [5], it might be time to revisit this concept
in light of all the progress that has been made in information visualization in the past
two decades.
We must start with defining information, as it is the core of information visuali-
zation.
Schneider defined information as “always a measure of the decrease of uncertainty
at a receiver” [6] while Cherry stated “Information can only be received where there
is doubt; and doubt implies the existence of alternatives where choice, selection, or
discrimination is called for” [7]. Measuring information is a topic found in many
fields, including science, engineering, mathematics, psychology, and linguistics. In-
formation theory, which primarily evolved out of the study of hardware communica-
tion channels, defines entropy as the loss of information during transmission; it is also
referred to as a measure of disorder or randomness. Another important term is band-
width, which is a measure of the amount of information that can be delivered over a
communication channel in a given period of time. We will attempt to analyze infor-
mation visualization using this terminology.
Information can be categorized in a number of ways. MacKay [8] identifies three
types of information content:
•
Selective information-content: This is information that helps the receiver
make a selection from a set of possibilities, or narrows the range of possibili-
ties. An example might be a symptom that helps make a diagnosis.
•
Descriptive information-content: This type of information helps the user
build a mental model. Two types of descriptive information content have
been identified:
-
Metrical: this type of observation increases the reliability of a pattern,
e.g., a new member of an existing cluster (sometimes termed a metron).
-
Structural: this type of observation adds new features or patterns to a
model/representation, e.g., a new cluster (termed a logon).
•
Semantic information-content: This type of information is not covered in
classical information theory. It lies between the physical signals of commu-
nications (called the syntactic) and the users and how they respond to the
signals (called the pragmatics). The pragmatics are the domain of psychol-
ogy, both perceptual and cognitive.
While the first two classes of information content lend themselves well to measure-
ment, it is much harder to determine measures of semantic content, as this in general
is very specific to individuals.
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