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CHAPTER 14:
Information Visualization
Designing the User Interface:
Strategies for Effective Human-Computer Interaction
Fifth Edition
Ben Shneiderman & Catherine Plaisant
in collaboration with
Maxine S. Cohen and Steven M. Jacobs
Addison Wesley
is an imprint of
© 2010 Pearson Addison-Wesley. All rights reserved.
Information Visualization
• Introduction
• Data Type by Task Taxonomy
• 7 basic data types
• 7 basic tasks
• Challenges for Information Visualization
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Introduction
• “A picture is worth a thousand words”
• Information visualization can be defined as the
use of interactive visual representations of
abstract data to amplify cognition (Ware, 2008;
Card et al., 1999)
• The abstract characteristic of the data is what
distinguishes information visualization from
scientific visualization
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Introduction (cont’d)
• Information visualization: categorical variables and
the discovery of patterns, trends, clusters, outliers,
and gaps
• Scientific visualization: continuous variables,
volumes and surfaces
• Information visualization provides compact
graphical presentations and user interfaces for
interactively manipulating large numbers of items
(102 to 106), possibly extracted from far larger
datasets
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Introduction (cont.)
• Sometimes called visual data mining, it uses the enormous
visual bandwidth and the remarkable human perceptual
system to enable users to make discoveries, take
decisions, or propose explanations about patterns, groups
of items, or individual items
• Visual-information-seeking mantra:
- Overview first, zoom and filter, then details on demand
- Overview first, zoom and filter, then details on demand
- Overview first, zoom and filter, then details on demand
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Data Type by Task Taxonomy
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The seven data types
1. 1D Linear
-
One dimensional
Sequential organization
Examples: list of names, dictionaries, text
documents, program source codes
Interface-design issues: colors, sizes, layouts,
methods for overview, scrolling and selection
User tasks: find number of items, see items
with some attributes (e.g., recently added),
find most common items, see an item with all
its attributes, etc.
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Data Type by Task Taxonomy: 1D Linear Data
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Data Type by Task Taxonomy: 1D Linear Data
(cont.)
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Data Type by Task Taxonomy: 1D Linear Data
(cont.)
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The seven data types
2. 2D Linear
-
-
Two-dimensional, planar data
Examples: geographical maps, floor plans, newspaper
layouts
Each item in the collection:
- covers some part of the total area
- has attributes such as name, value, owner
- has UI features: color, shape, size, opacity
Multiple layers can be used, each 2D
User tasks: finding adjacent items, regions containing
specific items, paths between items
Typical application: GIS, which constitute a large
research and commercial domain
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Data Type by Task Taxonomy: 2D Map Data
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The seven data types
3. 3D World
-
-
Three-dimensional, real-world objects
Examples: molecules, human body, buildings
Each item in the collection has volume and a complex
relationships with the other items
Applications: medical imaging, architectural drawing,
mechanical design, scientific simulations
User tasks deal with continuous variables such as
temperature and density
Users must cope with position and orientation and must
handle occlusion and navigation
3D techniques are used in overviews, landmarks,
teleportation, tangible user interfaces, multiple views
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Data Type by Task Taxonomy: 3D World Data
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Data Type by Task Taxonomy: 3D World Data
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The seven data types
4. Multidimensional data
-
-
N-dimensional, in which items with N attributes are
points in ND space
Examples: most relational or statistical database
contents
Representation is in 2D or 3D (with some issues related
to disorientation and occlusion), with additional attributes
controlled by sliders or buttons
User tasks include finding patterns such as correlations
among pairs of variables, clusters, gaps, and outliers
Parallel coordinate plots are examples of compact MD
techniques: each parallel vertical axis is a dimension,
and each item is a line connecting values in each
dimension
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Data Type by Task Taxonomy:
Multidimensional Data [Tableau Software]
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Data Type by Task Taxonomy:
Multidimensional Data (cont.) [Table Lens]
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The seven data types
5. Temporal data
-
Very common data type, usually 1D linear data + time
stamps
Examples: weather data, electrocardiograms, stock
market prices
Items have a start and end time and may overlap
User tasks: find items before, during, or after some
event, plus the 7 basic tasks
Sometimes several time series are combined
Applications range from scientific data visualization to
project management
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Data Type by Task Taxonomy: Temporal Data
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Data Type by Task Taxonomy: Temporal Data
(cont.)
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The seven data types
6. Tree data
-
-
Hierarchies or tree structures
Each item except the root has a link to a parent
Items and links to parents can have multiple attributes
User tasks include the 7 basic tasks on items and links,
plus exploration of structure, e.g., shallow or deep
hierarchy
Representation include usual tree graphs (e.g., degree
of interest tree on the next slide), node-and-link
diagrams, treemaps, and the outline style of indented
labels used for example in Windows File Explorer
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Data Type by Task Taxonomy: Tree Data
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Data Type by Task Taxonomy: Tree Data (cont.)
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The seven data types
7. Network data
-
-
Used when relationships among items cannot be
captured properly with tree structures
Items are linked to an arbitrary number of other items in
a network
User tasks: finding the shortest or least costly paths,
traversing or navigating the network
Representations include node-and-link diagrams and
matrices of items with cells showing potential links
between the items (plus attributes on the link)
New interest in this topic has been spawned by
visualization of social networks
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Data Type by Task Taxonomy: Network Data
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The seven basic tasks
1. Overview task - users can gain an overview of the
entire collection
2. Zoom task - users can zoom in on items of
interest
3. Filter task - users can filter out uninteresting items
4. Details-on-demand task - users can select an
item or group to get details
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The seven basic tasks
5. Relate task - users can relate items or groups
within the collection
6. History task - users can keep a history of
actions to support undo, replay, and progressive
refinement
7. Extract task - users can allow extraction of
sub-collections and of the query parameters
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Challenges for Information Visualization
• Importing and cleaning data
• Combining visual representations with
textual labels
• Finding related information
• Viewing large volumes of data
• Integrating data mining
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Challenges for Information Visualization
(cont’d)
• Integrating with analytical reasoning
techniques
• Collaborating with others
• Achieving universal usability
• Evaluation
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Challenges for Information Visualization
(cont.)
•
Combining visual representations with textual labels
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Challenges for Information Visualization (cont.)
•
Viewing large volumes of data
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Challenges for Information Visualization
(cont.)
•
Integrating with
analytical
reasoning
techniques
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