advanced visualization overview. course structure syllabus reading / discussions tests minor...
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Overview Characterization of Visualization Data Types and Characteristics Characterization of Visualization Techniques Surface vs. Volume Rendering Perception’s Role in Visualization Some Common Visualization PackagesTRANSCRIPT
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: