1. Advanced Visualization Overview

2. Course Structure Syllabus Reading / Discussions Tests Minor Projects Major Projects http://www.cs.nmt.edu/~cs554 For details, go to course web site:

3. 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

4. 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

5. Characterization of Visualization Information Visualization  large quantities of data  need for understanding  recognition speed  creation of a cognitive map or internal model

6. Barcelona Metro

9. 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

10. Information Visualization Example: Market map variations  http://www.smartmoney.com/marketmap/ http://www.smartmoney.com/marketmap/  http://stockcharts.com/charts/carpet.html http://stockcharts.com/charts/carpet.html

11. Characterization of Visualization Scientific Visualization  visual representation of the simulation of some physical entity  exploration of numerical data by means of visual, graphical objects  immersive or virtual environments

12. Model of the Heliosphere Over the Solar Cycle

13. Ozone Model Holds Key to Ozone Trends

14. Data Types numerical  e.g., from simulations and measurements ordinal  e.g., calendar based categorical  e.g., the names of plants on the planet

15. 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

16. 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 Course Dr. David S. Ebert Dr. David S. Ebert, Dr. Penny Rheingans, University of MarylandDr. Penny Rheingans

17. 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

18. 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

19. Data Characteristics Structure  Unstructured: no (or unknown) spatial relationship among points ex: FEM, structural analysis, census, monitor devices  flexibility  limited visualization algorithms

20. Data Characteristics Structure  Completely unstructured no known spatial relationship among points ex: pollution monitors, documents, atoms advantages:  flexibility  efficient storage (sparse data)

21. 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

22. 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

23. Data Characteristics cont. Data Transformations  Interpolation  Aggregation  Smoothing  Simplification Data Quality  Missing data  Uncertain data  Representation error  Sampling artifacts

24. 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]

25. Visualization Taxonomies Herman (2000) (for structural data)  graph layout, navigation, interaction Chengzhi (2003) – single factor  data type  display mode  interaction style  analytic task  based model “Taxonomy of visualization techniques and systemsTaxonomy of visualization techniques and systems – concerns between users and developers are different– concerns between users and developers are different”

26. Visualization Taxonomies Chengzhi (2003) – multiple factors  User-oriented analytic task data type  Developer-oriented interaction level representation mode

27. Visualization Taxonomies Chengzhi (2003)

28. Visualization Taxonomies Chengzhi (2003)

29. 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.pdf For details, go to the paper: