Visualization Encoding

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Visualization Encoding
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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
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Fundamental Tasks

• Information presentation.
• Maps, Photographs, Movies,
• Information extraction.
• Interactive graphical interface

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Information Presentation

• Data Mining Example Clustering

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Information Extraction

• Data Mining Example Clustering

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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.

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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.

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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,

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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

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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

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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

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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.

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An Encoding Example
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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.

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Data Set Encoding

• The data sources are encoded as layers.
• The different result sets are encoded as
  different panes in different layers.

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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)

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• 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.

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• Concatenation Example
• Quarter Qtr1, Qtr2, Qtr2, Qtr4
• Product Coffee, Espresso, Herbal, Tea
• Profit Profit, Sales Sales

Ordinal Field
Group By
Quantitative Field
Sorted By
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• Cross Product Example
• Ordinal x Ordinal

• Ordinal x Quantitative

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• Nest (Division) Example

• Quantitative field does not make sense for
  divisions

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Quarter x SumOfProfit
Product x SumOfSales
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Types of Graphics inside Panes

• Types of Panes
• Ordinal Ordinal
• Ordinal Quantitative
• Quantitative - Quantitative

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Visual Encoding

• Shape
• Size
• Orientation
• Color

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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

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

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Network Data and Task Encoding

• Graph data multiple paths, cycles, lattices
• User problems
• Shortest path
• Topology problems
• Encoding imperfect
• Node-link diagram
• Matrix

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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.

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• Perceptual accuracy ranks

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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.