Advanced Visualization

Overview

Course Structure

Syllabus Reading / Discussions Tests Minor Projects Major Projects

http://www.cs.nmt.edu/~cs554For details, go to course web site:

Overview

Characterization of Visualization Data Types and Characteristics Characterization of Visualization Techniques Surface vs. Volume Rendering Perception’s Role in Visualization Some Common Visualization Packages

Characterization of Visualization

What is visualization?Oxford: “make visible, esp. to one’s mind (a

thing not visible to the eye)” Value of visualization

gain insight and understanding

Characterization of Visualization

Information Visualization large quantities of dataneed for understandingrecognition speedcreation of a cognitive map or internal model

Barcelona Metro

Barcelona Metro

Barcelona’s Maps…

• Let’s explore two interactive maps

http://www.tmb.net/en_US/barcelona/moute/planols/planols.jsp

http://www.tmb.net/en_US/barcelona/moute/planols/planoxarxametro.jsp

Information Visualization

Example: Market map variationshttp://www.smartmoney.com/marketmap/http://stockcharts.com/charts/carpet.html

Characterization of Visualization

Scientific Visualizationvisual representation of the simulation of

some physical entityexploration of numerical data by means of

visual, graphical objects immersive or virtual environments

Model of the Heliosphere Over the Solar Cycle

Ozone Model Holds Key to Ozone Trends

Data Types

numerical e.g., from simulations and measurements

ordinale.g., calendar based

categoricale.g., the names of plants on the planet

Data Sources Simulations

ex: CFD, environmental modeling, virtual crash tests Sensors/Scanners

ex: medical diagnosis, satellites, emissions monitors Surveys/Records

ex: census, consumer tracking, polls, observational studies

Equations   ex: math, health effects models

Data Characteristics

Continuity Continuous: nature is continuous

is there any thing truly continuous?Discrete: anything sampled or stored on

digital media representation error possible aliasing artifacts of sampling

Data Characteristics based on Visualization Techniques CourseDr. David S. Ebert , Dr. Penny Rheingans, University of Maryland

Data Characteristics cont. Structure

Definitions Topology: connectivity (triangle) Geometry: realization of topology (coordinates)

Elements Points: located where data values are known

(geometry) Cells: set up interpolation parameters (topology)

common types: point, line, triangle, quad, tetra, voxel

Data Characteristics Structure

Structured: inherent spatial relationship among points relatively efficient storage: topology is implicit regular

represented implicitly (3x3: dimension, origin, aspect) ex: medical data

rectilinear represented semi implicitly (nx + ny + nz) ex: CFD -- refinement around objects

curvilinear represented explicitly (3*nx*ny*nz) ex: CFD -- flow along river

ease of computation wide array of visualization algorithms

Data Characteristics Structure

Unstructured: no (or unknown) spatial relationship among points

ex: FEM, structural analysis, census, monitor devices flexibility limited visualization algorithms

Data Characteristics Structure

Completely unstructured no known spatial relationship among points ex: pollution monitors, documents, atoms advantages:

flexibility efficient storage (sparse data)

Data Characteristics cont. Data Representation

Compact: efficient memory use ex: structured scheme, unstructured schemes, sparse

matrices, shared vertices Efficient:

computationally accessible retrieve and store in constant time structured schemes

Data Characteristics cont. Data Representation

Mappable: straight-forward conversions native -> rep: simple conversion, no lost information rep -> graphics primitive: especially for interactive display

Minimal coverage: manageable number of options few variants which work for a wide range of data sets

Simple easier to use easier to optimize errors less likely

Data Characteristics cont. Data Transformations

Interpolation Aggregation Smoothing Simplification

Data Quality Missing data Uncertain data Representation error Sampling artifacts

Characterization of Visualization Techniques Categorize visualization techniques by:

what kind of data can be displayed ("attributes") attributes: [scalar, scalar field, nominal, direction, direction

field, shape, position, spatially extended region or object, structure]

what operations act on these attributes ("operations/judgments").

operations: [identify, locate, distinguish, categorize, cluster, distribution, rank, compare within and between relations, associate, correlate]

Visualization Taxonomies

Herman (2000) (for structural data)graph layout, navigation, interaction

Chengzhi (2003) – single factordata typedisplay mode interaction styleanalytic taskbased model

“Taxonomy of visualization techniques and systems – concerns between users and developers are different”

Visualization Taxonomies

Chengzhi (2003) – multiple factorsUser-oriented

analytic task data type

Developer-oriented interaction level representation mode

Visualization Taxonomies

Chengzhi (2003)

Visualization Taxonomies

Chengzhi (2003)

Survey of Techniques

Making Information more Accessible: A Survey of Information Visualization Applications and Techniques.

Gary Geisler January 31, 1998

http://www.cs.nmt.edu/~cs554/papers/Geisler.pdfFor details, go to the paper: