Ben Shneiderman & Catherine Plaisant in collaboration withMaxine S. Cohen and Steven M. Jacobs

1. © 2010 Pearson Addison-Wesley. All rights reserved.
Addison Wesley
is an imprint of
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
CHAPTER 14:
Information Visualization

2. 1-3
© 2010 Pearson Addison-Wesley. All rights reserved.
Information Visualization
• Introduction
• Data Type by Task Taxonomy
• Challenges for Information Visualization
14-3

3. 1-4
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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.
• 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, possibly extracted from far larger
datasets.
14-4

4. 1-5
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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.
- Overview first, zoom and filter, then details on demand.
- Overview first, zoom and filter, then details on demand.
14-5

5. 1-6
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Data Type by Task Taxonomy
14-6

6. 1-7
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Data Type by Task Taxonomy: 1D Linear Data
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7. 1-8
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Data Type by Task Taxonomy: 1D Linear Data
(cont.)
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8. 1-9
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Data Type by Task Taxonomy: 1D Linear Data
(cont.)
14-9

9. 1-10
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Data Type by Task Taxonomy: 2D Map Data
14-10

10. 1-11
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Data Type by Task Taxonomy: 2D Map Data
(cont.)
14-11

11. 1-12
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Data Type by Task Taxonomy: 3D World Data
14-12

12. 1-13
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Data Type by Task Taxonomy:
Multidimensional Data
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Data Type by Task Taxonomy:
Multidimensional Data (cont.)
14-14

14. 1-15
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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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Data Type by Task Taxonomy: Tree Data
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17. 1-18
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Data Type by Task Taxonomy: Tree Data (cont.)
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Data Type by Task Taxonomy: Network Data
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19. 1-20
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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
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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20. 1-21
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Challenges for Information Visualization
14-21
• Importing and cleaning data
• Combining visual representations with textual
labels
• Finding related information
• Viewing large volumes of data
• Integrating data mining
• Integrating with analytical reasoning techniques
• Collaborating with others
• Achieving universal usability
• Evaluation

21. 1-22
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Challenges for Information Visualization
(cont.)
14-22
• Combining visual representations with textual
labels

22. 1-23
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Challenges for Information Visualization
(cont.)
14-23
• Viewing large volumes of data

23. 1-24
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Challenges for Information Visualization
(cont.)
14-24
• Integrating with
analytical
reasoning
techniques