Visualization Encoding

1. Visualization Encoding

2. Introduction • Information visualization starts from data. • There are many forms that the data could take, text, spreadsheets, relational DB tuples, etc. • There are many patterns that the data could follow, clustering, outlier, correlation, etc. • Encoding: Application Domain Graphic Presentation Data

3. Fundamental Tasks • Information presentation. Maps, Photographs, Movies, … • Information extraction. Interactive graphical interface

4. Information Presentation Data Mining Example: Clustering

5. Information Extraction Data Mining Example: Clustering

6. Data Types • 1-D, 2-D, 3-D, temporal, multi-dimensional, tree and network data. • Data types characterize the information objects in the task domain.

7. Basic Visualization Tasks • Overview of a collection of data. • Zoom in/on objects of interest. • Filter out uninterested items. • Details-on-demand: view details. • Relate: View relationship. • History: Undo, Redo, Refinement. • Extract a subset of the data.

8. 1-D Data and Task Encoding • Linear data: textual document, source code, etc. • User problems: count, find, replace, … • Encoding: fonts, color, size, layout, scrolling, selection capabilities, … • Product example: text editor, browser, …

9. 2-D Data and Task Encoding • Planar or map data: geographical maps, floor plans, newspaper layouts, … • User problems: find adjacent items, search containment, find paths, filtering, details-on-demand, … • Encoding: size, color, layout, arrangement, multiple layers, … • Product example: CAD

10. 3-D Data and Task Encoding • Real-world objects: building, human body • User problems: adjacency in 3-D, inside/outside relationship, position, orientation, occlusion • Encoding: overviews, landmarks, transparency, color, perspective, stereo display • Product example: CAD

11. Temporal Data and Task Encoding • Time series data: medical records, project management, historical presentation • User problems: finding all events before, after or during some time period or moment. • Encoding: time lines

12. Multi-dimensional Data and Task Encoding • Relational and statistical databases tuples. • User problem: finding patterns, clusters, correlations, gaps, outliers. • Challenge: • Simultaneously display many dimensions of large subsets of data. • Create displays that best encode the data pattern for a particular task. • Rapidly select a subset of tuples or dimensions.

13. An Encoding Example

14. Dimensionality Encoding • Multi-dimensional databases are structured as n-dimensional data cube. • The dimensions of the data can be explicitly encoded in the structure of tables.

15. Data Set Encoding • The data sources are encoded as layers. • The different result sets are encoded as different panes in different layers.

16. User Interest Encoding • Providing enough tools and allowing user to specify his interest. • The table configuration encodes the user interest. • Table configurations are defined in form of algebra • Concatenation • Cross product • Nest (Division)

17. For ordinal fields, algebra operand symbols take all domain values. • A = domain (A) = {a1, a2, …, an} • Example: Month = {Jan, Feb, …, Dec} • For quantitative fields, algebra operand symbols take the field names as values. • P = {P} • Example: Profit = {Profit} • Ordinal fields partition the table into rows and columns; quantitative fields are spatially encoded as axes within the panes.

18. Concatenation Example: • Quarter = {Qtr1, Qtr2, Qtr2, Qtr4} • Product = {Coffee, Espresso, Herbal, Tea} • Profit = {Profit}, Sales = {Sales} Ordinal Field Group By Quantitative Field Sorted By

19. Cross Product Example: • Ordinal x Ordinal • Ordinal x Quantitative

20. Nest (Division) Example: • Quantitative field does not make sense for divisions

21. Quarter x SumOfProfit Product x SumOfSales

22. Types of Graphics inside Panes • Types of Panes: • Ordinal – Ordinal • Ordinal – Quantitative • Quantitative - Quantitative

24. Visual Encoding • Shape • Size • Orientation • Color

25. Tree Type Data and Task Encoding • Exponential data: hierarchies, tree structures. • User problems: find the structural properties • Height of the tree • Number of children • Find nodes with same attributes • Encoding: • Outline style of indented labels

26. Node-link diagrams: allowing the encoding of linkage between entities. • Treemap: child rectangles inside parent rectangles • Product example: windows explorer, internet traffic, hyperbolic browser

28. Network Data and Task Encoding • Graph data: multiple paths, cycles, lattices • User problems: • Shortest path • Topology problems • Encoding: imperfect • Node-link diagram • Matrix

29. General Encoding Principles • Expressiveness: • Encode all the facts in the result set. • Encode only the facts in the result set. • Effectiveness: • Depends on the capability of the perceiver. • Encode the more important information more effectively.

30. Perceptual accuracy ranks

31. Conclusion • Visualization helps • Information presentation • Information extraction • Good visual encoding should match the target data and user problems. • Studying the successful/unsuccessful visual encoding designs and techniques helps us to design and develop new encoding approaches.