2. Moritz Stefaner
1998 – 2002
2002 – 2005
2005 – 2007
2007 – 2009
2004 –
Web designer
BSc Cognitive Science
MA Interface Design
Research assistant FH Potsdam
Freelance information visualizer
working for Skype, World Economic Forum, dpa, Yahoo…
5. First quantitative abstractionsKirkuk, Iraque. The original was
carved in stone.
Herodotus. Anaximander of
Miletus (c.610BC-546BC).
c. 950
First known record of showing
variables graphically
(position of the sun, moon and
planets during the year).
c. 1350
The French Bishop Nicole Oresme
(1323-1382) proposes the use of bar
graphics to demonstrate a variable
that depends on another value.
M Denis and MJ Friendly : “Milestones in the History of Data Visualization”
30. Hierarchical Edge Bundling
Danny Holten: Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data, IEEE Transactions on
Visualization and Computer Graphics, Vol. 12, No. 5, September/October 2006
Danny Holten and Jarke J. van Wijk: Force-Directed Edge Bundling for Graph Visualization, Eurographics/ IEEE-VGTC
Symposium on Visualization 2009, Volume 28 (2009), Number 3
52. First guesses of what might be interesting
Ein-
reichung
X
Ein-
reichung
X
X
X
X
X
Winner?
Winner?
Keywords
XKategorie
Jahr X
X XXLand
XAutor XX
KeywordsKategorieJahrLandAutor
53. What’s in the databases http://well-formed-data.net/archives/306/dbcounter-quick-visual-database-stats
54.
55.
56.
57.
58.
59.
60. Wattenberg. A Note on Space-Filling Visualizations and Space-Filling Curves .
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61.
62.
63. Looking at Data Through the Eyes of Experts
/Theory/In/Practice
Beautiful
Visualization
Edited by
Julie Steele
& Noah Iliinsky
99. “The pure and simple truth is
rarely pure and
never simple.”
Oscar Wilde
100. New York Times graphics http://www.nytimes.com/interactive/2010/02/02/us/politics/
101. Segel and Heer. Narrative Visualization: Telling Stories with Data.
ucture
ation structure be-
approach, initially
ns, or written articles to introduce the vi-
no text is used at all, as the visualization
ing default view or annotations. Once the
is complete, the visualization opens up to
the user is free to interactively explore the
es a martini glass, with the stem represent-
driven narrative and the widening mouth
he available paths made possible through
Using this image, we can think of varying
ion, observation, article) as corresponding
ort, long), and varying degrees of reader-
path-choosing) corresponding to different
ese permutations, the general structure re-
es a martini glass, with the stem represent-
driven narrative and the widening mouth
he available paths made possible through
Using this image, we can think of varying
ion, observation, article) as corresponding
ort, long), and varying degrees of reader-
path-choosing) corresponding to different
se permutations, the general structure re-
or-driven narrative functioning first, then
reader-driven interactions. The authoring
jumping off point for the reader’s interac-
ations, and themes suggesting the types of
re on his own. This structure is the most
ive visualizations we examined.
how
tructure follows a
t incorporates in-
in the confines of each slide. This structure
xplore particular points of the presentation
next stage of the story. We saw this struc-
yle, again communicating author-intended
g the user to interact with the display.
work well with both complex datasets and
atasets, this structure allows the author to
a-dimensions and manipulations step-by-
ser only moves forward in the presentation
nd allows the user to repeat steps if desired.
s format allows the author to draw discrete
nt story segments, similar to a cut in film.
alization structure
nd then allows the
cular instances of that theme to reveal addi-
es. For instance, the theme might be “His-
1] and the visualization will allow the user
r bear market to learn more about its his-
North Korean Prison Camps” [A38] may
e about individual camps by clicking on a
p. This structure puts more emphasis on the
ting the user dictate what stories are told
Martini glass structure
Drill–down story
Interactive slideshow