Graphics Reference
In-Depth Information
(2010) list techniques for visualization: visualiza-
tion techniques for spatial data (in one, two, three
dimensions and their combinations), techniques
for geospatial data (point-, line-, area-based data,
and their combinations), techniques for imaging
multivariate data (point-, line-, region-based
techniques, and their combinations), techniques
for visualization of trees, graphs, and networks,
text representations, and interaction techniques.
When considering visualization, the word 'model'
is used both as a copy of some object recreated
either larger or smaller than the original (often
with the use of an interactive modeling system),
or as a mathematical model describing physical
laws and the behavior of physical objects. Chen
(2010) discusses information visualization in
terms of structural modeling aimed “to detect,
extract, and simplify underlying relationships” and
graphical representation aimed “to transform an
initial representation of a structure into a graphical
one, so that the structure can be visually examined
and interacted with” (Chen, 2010, p. 27). Struc-
tural modeling to describe the relationships may
involve applications with the use of graphs, trees,
or cones; detecting proximity and connectivity;
clustering and classification using word search,
multi-dimensional-scaling, and network analysis;
glyphs on charts and graphs; virtual structures;
applying complex network theory, and network
representations (Chen, 2010).
Visualization tools derive its form from many
domains; moreover, there are no defined bound-
aries between disciplines of visualization, while
definitions and theoretical approaches change with
the advances in technologies. Masud, Valsecchi,
Ciuccarelli, Ricci, and Caviglia (2010) identified
the most important domains in visualization:
data as “sequences of numbers or charac-
ters representing qualitative or quantitative
attributes of specific variables. To obtain
information, data is processed and brought
into a context within which it gains a spe-
cific meaning and becomes understandable
to users;
•
Information Visualization:
Means the use
of computer-supported, interactive, visual
representations of abstract data to amplify
cognition (Bederson and Shneiderman,
2003; Card, Mackinlay, & Shneiderman,
1996) and derive new insights. “Information
visualization makes use of human visual
perception capabilities for recognition
of patterns and extraction of knowledge
from raw data and information” (Sabol, in
Bertschi et al., 2011, p. 333);
•
Knowledge Visualization:
Uses visual
representation to transfer insights and
create new knowledge, rather than data,
between individuals; it concentrates on
the recipients, other types of knowledge
(know-why, know-how), and on the pro-
cess of communicating different visual for-
mats (Burkhard, Meier, Smis, Allemang,
& Honish, 2005). Sabol (in Bertschi et al.,
2011, p. 333) defines knowledge as “an ac-
quired, established set of facts, recognized
to be valid and valuable within a specific
domain”;
•
Scientific Visualization:
Established in
1985 at the National Science Foundation
panel, deals with physically based data
defined in reference to space coordinates,
such as geographic data and computer to-
mography data of a body (Voigt, 2002).
Biomolecular structures were first visual-
ized as balls connected with sticks, and
then as spheres with rods; advanced visu-
alizers, the 3D graphics software generate
images from an electron microscope, thus
providing information visualization (Ward,
Grinstein, & Keim, 2010, p. 24);
•
Data Visualization:
“Information which
has been abstracted in some schematic
form” (Friendly, 2009), to provide visual
insights in sets of data. Data may be 1D lin-
ear, 2D, 3D, and multidimensional. Sabol
(in Bertschi et al., 2011, p. 333) describes
