25. Design Considerations for Optimizing Storyline Visualizations
Yuzuru Tanahashi and Kwan-Liu Ma - 2012
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
26. Design Considerations for Optimizing Storyline Visualizations
Yuzuru Tanahashi and Kwan-Liu Ma - 2012
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06327274
30. information visualisation
“... is the use of computer-
supported, interactive, visual
representations of abstract data
to amplify cognition”
Information Visualization Definition
31. “information visualisation is the use of
computer-supported, interactive,
visual representations of abstract
data to amplify cognition”
. automatic/automated algorithm
. versus custom or hand-made (sketching!)
. facilitates high complexity, analytics, ...
32. “information visualisation is the use of
computer-supported, interactive,
visual representations of abstract
data to amplify cognition”
. to make assumptions, test hypotheses
. to allow individualized exploration scenarios
. while and during the exploration itself
33. “information visualisation is the use of
computer-supported, interactive,
visual representations of abstract
data to amplify cognition”
. just ‘representing’ values or conveying meaning?
. guiding users, show example insights, highlighting
. engagement? involvement? immersion?
34. “information visualisation is the use of
computer-supported, interactive,
visual representations of abstract
data to amplify cognition”
. data without natural representation
. requires metaphor to be perceived
. complexity: size, dimensionality, time-variance
35. because the data is abstract...
“the challenge is to invent
new metaphors for
presenting information &
developing ways to
manipulate these
metaphors to make sense
out of the information...”
Information Visualization ‘Design’ Challenge
36. “information visualisation is the use of
computer-supported, interactive,
visual representations of abstract
data to amplify cognition”
. analytics versus communication
. creating insights: new, valuable, deep,...
. requires different kinds of visuals, interactivity, ...
40. Choice of “Metaphor”
. can be potentially seemingly “useless”
. yet receive a lot of interest
. how to interpret “useful”?
. persuasiveness of visual representations?
41. data insight
10010110 knowledge
transfer
data mapping
mapping
inversion
visualisation comprehension
!
visual transfer
Visual Mapping Methodology
42. Visualization as a “Medium”
. scientific visualization
. data graphics
. infographics
. information design
. data art
48. The Jobless Rate for People Like You - The New York Times
http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html
49. Four Ways to Slice Obama’s 2013 Budget Proposal - The New York Times
http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html
50. Spotlight on Profitability - Information is Beautiful Competition Entry (not winning...)
http://www.informationisbeautifulawards.com/2012/02/hollywood-visualisation-challenge-
design-shortlist/
http://szucskrisztina.hu/images/holly.png
83. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
84. Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
IEEE International Conference on Information Visualisation (IV'07), pp. 87-92.
85. Narrative Visualization: Telling Stories with Data
Edward Segel and Jeffrey Heer
http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
Genres of Narrative Visualization, Balancing Author-Driven versus Reader-Driven Stories
86. Narrative Visualization: Telling Stories with Data
Edward Segel and Jeffrey Heer
http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
87. Our Irresistible Fascination with All Things Circular
http://www.perceptualedge.com/articles/visual_business_intelligence/
our_fascination_with_all_things_circular.pdf
88. Our Irresistible Fascination with All Things Circular
http://www.perceptualedge.com/articles/visual_business_intelligence/
our_fascination_with_all_things_circular.pdf
89. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
90. Aesthetic Effect in Data Visualization - Nick Cawthon and Andrew Vande Moere - 2007
97. Goal
•visualizationimpact of style in information
to measure
• by comparing 3 different ‘design alternatives’
• in terms of visual and interactive style
• style demonstrators based on real-world
examples
• then contrasted resulting insights against each
other
Evaluating the Effect of Style in Information Visualization
Andrew Vande Moere, Martin Tomitsch, Christoph Wimmer, Christoph Boesch, and Thomas
Grechenig, IEEE Infovis 2012
112. Conclusions
•style impacts perception of usability
• analytical style was perceived as more
understandable, clear, enjoyable, engaging,
useful, functional, ...
•style does not impact insight depth
• participants were able to overcome huge
incomprehensibility issues of ART, and in a
minimum amount of time
• style has impact on ‘kind’ of insights
• analytical focus of facts versus meaning of
content, explanation of reasoning, ...
113. Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
114. Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
115. your technique
Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
116. Design Study Methodology: Reflections from the Trenches and the Stacks
Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Infovis 2012
http://www.cs.ubc.ca/nest/imager/tr/2012/dsm/
121. 1. Show comparisons, contrasts, differences
2. Show causality, mechanism, explanation,
systematic structure
3. Show multivariate data; that is, show more
than 1 or 2 variables
4. Completely integrate words, numbers, images,
diagrams
5.Thoroughly describe the evidence: title, authors
and sponsors, data sources, add measurement
scales, highlight relevant issues
6.Analytical presentations ultimately stand or fall
depending on the quality, relevance and
integrity of their content
Principles for the Analysis and Presentation of Data - Tufte
123. 2. Show causality, mechanism,
explanation, systematic structure
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
124. 3. Show multivariate data; that is,
show more than 1 or 2 variables
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
125. 4. Completely integrate words,
numbers, images, diagrams
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
126. 5.Thoroughly describe the
evidence: title, authors and
sponsors, data sources, add
measurement scales, highlight
relevant issues
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
127. 6.Analytical presentations
ultimately stand or fall depending
on the quality, relevance and
integrity of their content
French Invasion of Russia (Minard, +-1864)
Napoleon Retreat (Minard, +-1864)
130. • Lau A. and Vande Moere A. (2007), "Towards a Model of Information Aesthetic Visualization",
IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich, Switzerland, pp.
87-92.
• Cawthon N. and Vande Moere A. (2007), "The Effect of Aesthetic on the Usability of Data
Visualization", IEEE International Conference on Information Visualisation (IV'07), IEEE, Zurich,
Switzerland, pp. 637-648.
• Vande Moere A., Tomitsch M., Wimmer C., Boesch C. and Grechenig T. (2012), "Evaluating the
Effect of Style in Information Visualization", IEEE Transactions on Visualization and Computer
Graphics, 18(12), December 2012, pp.2739-2748.
• Sedlmair, Michael; Meyer, Miriah; Munzner, Tamara; , "Design Study Methodology: Reflections
from the Trenches and the Stacks”, IEEE Transactions on Visualization and Computer Graphics,
18 (12), pp.2431-2440.
• Edward Segel, Jeffrey Heer, Narrative Visualization: Telling Stories with Data, IEEE Trans.
Visualization & Comp. Graphics (Proc. InfoVis), 2010.
• Jeffrey Heer, Michael Bostock,Vadim Ogievetsky, A Tour through the Visualization Zoo
http://queue.acm.org/detail.cfm?id=1805128
...
131. • http://www.tableausoftware.com/public/
first explorations of dataset for insights
•
http://visualizing.org
http://www.informationisbeautifulawards.com/
check winning entries!
• http://infosthetics.com
http://flowingdata.com/
blog with wide selection
• http://selection.datavisualization.ch/
collection of good tools!
• http://thewhyaxis.info
http://www.perceptualedge.com/blog/
what is good, what is bad, and why?
• http://moritz.stefaner.eu/
http://www.periscopic.com/
http://stamen.com/
http://tulpinteractive.com/
high quality infovis examples...
