1. Grinstein lecture/book visualizations
to liven/explain theory paper

2. 3.1. Preattentive Processing
Connection
2
Source unknown

3. 3.2. Theories of Preattentive Processing
Feature Integration Theory
http://www.idvbook.com/
(a) a boundary defined by a unique
feature hue is preattentively classified
as horizontal;
3
(b) a boundary defined by a
conjunction of features cannot be
preattentively classified as vertical

4. 4
Roland Rensink. “The Need for Attention to See Change.” http://www.psych.ubc.ca/∼rensink/flicker, March 2, 2003.

5. 5
Roland Rensink. “The Need for Attention to See Change.” http://www.psych.ubc.ca/∼rensink/flicker, March 2, 2003.

6. 4. Perception in Visualization
http://www.idvbook.com/
Examples of perceptually motivated multidimensional visualizations:
(a) visualization of intelligent agents competing in simulated e-commerce
auctions;
(b) visualization of a CT scan of an abdominal aortic aneurism;
(c) a painter-like visualization of weather conditions over the Rocky
Mountains
6

7. 3.3. Feature Hierarchy
Example: Line Width
7
Source unknown

8. Bar Chart
Bar length better than area size (actually only area height was used!)
Solution from Stephen Few‘s Perceptual Edge
8

9. Improved Bar Chart
Solution from Stephen Few‘s Perceptual Edge
9

10. 3.1. Position
http://www.idvbook.com/
Example visualizations: (left) using position to convey information. Displayed here is the
minimum price versus the maximum price for cars with a 1993 model year. The spread of points
appears to indicate a linear relationship between minimum and maximum price; (right) another
visualization using a different set of variables. This figure compares minimum price with engine
size for the 1993 cars data set. Unlike (left), there does not appear to be a strong relationship
between these two variables.
10

11. 3.2. Mark
This visualization uses
shapes to distinguish
between different car
types in a plot comparing
highway MPG and
horsepower. Clusters are
clearly visible, as well as
some outliers.
http://www.idvbook.com/
11

12. 3.3. Size (Length, Area and Volume)
This is a visualization of
the 1993 car models data
set, showing engine size
versus fuel tank capacity.
Size is mapped to
maximum price charged.
http://www.idvbook.com/
12

13. 3.4. Brightness
Another visualization of
the 1993 car models data
set, this time illustrating
the use of brightness to
convey car width (the
darker the points, the
wider the vehicle).
http://www.idvbook.com/
13

14. 3.5. Color
http://www.idvbook.com/
A visualization of the 1993
car models, showing the
use of color to display the
car’s length. Here length is
also associated with the yaxis and is plotted against
wheelbase. In this figure,
blue indicates a shorter
length, while yellow
indicates a longer length.
14

15. 3.6. Orientation
Sample visualization of
the 1993 car models data
set depicting using
highway milesper-gallon
versus fuel tank capacity
(position) with the
additional data variable,
midrange price, used to
adjust mark orientation.
http://www.idvbook.com/
15

16. 3.7. Texture
Example visualization
using texture to provide
additional information
about the 1993 car
models data set, showing
the relationship between
wheelbase versus
horsepower (position) as
related to car types,
depicted by different
textures.
http://www.idvbook.com/
16

17. 4.9. Senay and Ignatius (1994) VISTA
VISTA’s composition rules
Hikmet Senay and Eve Ignatius. “A Knowledge-Based System for Visualization Design.” IEEE
Comput. Graph. Appl. 14:6 (1994), 36–47.
17

18. 2. Two-Dimensional Data
A cityscape showing the density of air traffic over
the United States at a particular time period.
18

19. Landscapes
Example: News articles visualized as a landscape
• visualization of the data as
perspective landscape
• the data needs to be
transformed into a
(possibly artificial)
2D spatial representation
which preserves the
characteristics of the data

20. High-Dimensional Data
Parallel Coordinates

21. Parallel Coordinates

22. Parallel Coordinates (Example)
Baseball
League
Database
(1996)

23. Chernoff-Faces
Do you spot any trend?

24. Stick Figures
5-dim. Image
data from the
great lake region

25. 4.3. Visualization Techniques
OpenDX (http://www.opendx.org/)
A storm cloud visualization containing glyphs
showing wind direction and strength.
25

26. 4.3. Visualization Techniques
OpenDX (http://www.opendx.org/)
Flow data visualized using ribbons, with vorticity
mapped to twist.
26

27. 4.3. Visualization Techniques
OpenDX (http://www.opendx.org/)
Corresponding points from several time slices
can be joined to form streaklines.
27

28. 1.1. Space-Filling Methods
Jing Yang, Matthew O.Ward, Elke A. Rundensteiner, and Anilkumar Patro. “InterRing: A Visual Interface
for Navigating and Manipulating Hierarchies.” Information Visualization 2:1 (2003), 16–30.
A sample hierarchy and the corresponding treemap
display.
28

29. 1.1 Cushion Treemap
Idea: Use shading to construct a
surface which shape encodes
the tree structure.
The human visual system is
trained to interpret variations in
shade as illuminated surfaces .
see: H. van de Wetering and J. van Wijk. Cushion treemaps: Visualization of hierarchical information.In
Proceedings of the IEEE Symposium on Information Visualization (InfoVis), 2005.
29

30. 1.1 Newsmap
30

31. 1.1 Treemap
Bederson, B.B., PhotoMesa: a
zoomable image browser using
quantum treemaps and
bubblemaps, Proceedings of the
14th annual ACM symposium on
User interface software and
technology, pp 71-80, 2001, ACM
31

32. 1.1. Space-Filling Methods
Jing Yang, Matthew O.Ward, Elke A. Rundensteiner, and Anilkumar Patro. “InterRing: A Visual Interface
for Navigating and Manipulating Hierarchies.” Information Visualization 2:1 (2003), 16–30.
A sample hierarchy and the corresponding sunburst
display.
32

33. 2.1. Graphs-Drawing Conventions
Edge
oriented
Clustering
oriented
Orthogona
l
Hierarchica
l
Circular
Hierarchy
oriented
Node
oriented
ForcePictures from: Directed
www.tomsawyer.com
33

34. Hierarchical Edge Bundling
34

35. Hierarchical Edge Bundling
More details in the
paper:
• Bundling Strength
• Alpha blending
Danny Holten, Hierarchical Edge Bundles: Visualization of Adjacency Relations in
Hierarchical Data, IEEE TVCG, Vol 12, No 5, 2006 (Best Paper InfoVis 2006)
35

36. 3.2. Tabular Displays
Inxight Table Lens (http://www.inxightfedsys.com/products/sdks/tl/default.asp)
An example of Inxight Table Lens showing the cars data set
sorted first by car origin and then by MPG.
36

37. 5.2. Hybrid Approaches
Example: XMDV Tool
XMDV allows to dynamically link and brush scatterplot matrices, star icons,
parallel coordinates, and dimensional stacking (combination of geometric,
icon-based, hierarchical and dynamic techniques).
Matthew O. Ward, "Linking and Brushing.", Encyclopedia of Database Systems 2009: 1623-1626. http://davis.wpi.edu/xmdv/
37

38. 5.2. Guidelines for Using Multiple Views
• Rule of Complementary:
Use multiple views when
different views bring out
correlations and/or
disparities.
38

39. Georges Grinstein, UMass Lowell – Daniel

42. 1. Visualizing Spatial Data
• Type of map depends on the properties of the
data, for example:
Dot maps
Line diagrams
Land use maps[2]
Isoline maps[3]
Chloropleth maps
Surface maps[1]
[1] K. Crane, Spin transformations of discrete surfaces, 2011
[2] C. Power, Hierarchical fuzzy pattern matching for the regional comparison of land use maps, 2001
[3] I. Solis, Isolines: energy-efficient mapping in sensor networks, 2005
42

43. 8.3.1 Dot Map
A simple dot map of commercial wireless antennas in the USA
43

44. 2.1. Pixel Maps
0:00 am (EST)
6:00 am (EST)
10:00 pm (EST)
6:00 pm (EST)
The figures display U.S.
Telephone Call Volume
at four different times
during one day. The idea
is to place the first data
items at their correct
position and position
overlapping data points
at nearby unoccupied
positions.
Overlap-free visualization!
Daniel A. Keim, Christian Panse, and Mike Sips. “Visual Data Mining of Large Spatial Data Sets.” In Databases in Networked
Information Systems, Lecture Notes in Computer Science, 2822, Lecture Notes in Computer Science, 2822, pp. 201–215.
Berlin: Springer, 2003.
44

45. 3.2. Flow Maps and Edge Bundling
The visualization of traffic flows of the United States to
other countries suffers under visual clutter.
Arc maps try to avoid overlapping by mapping 2D lines
into 3D arcs.
Partially translucent arcs avoid overplotting.
K.C. Cox. 3D geographic network displays. ACM Sigmod Record, 1996
45

46. 3.2. Flow Maps and Edge Bundling
Flow maps are used to show the movement
of objects from one location to another.
They avoid overlapping by merging edges by,
for example, clustering.
(a) Minard’s 1864 flow map of wine exports from France [20]
(b) Tobler’s computer generated flow map of migration from California from 1995 - 2000. [18; 19]
(c) A flow map produced by our system that shows the same migration data.
D. Pahn et al. Flow map layout. Information Visualization, 2005.
46

47. 3.2. Flow Maps and Edge Bundling
The visualizations show IP flow traffic from external nodes on the outside to
internal nodes, visualized as treemaps on the inside. The edge bundling
visualization (right side) significantly reduces the visual clutter compared to
the straight line visualization (left side).
Fabian Fischer, Florian Mansmann, Daniel A. Keim, Stephan Pietzko, and Marcel Waldvogel. “Large-Scale Network Monitoring for Visual Analysis of Attacks.” In Visualization for Computer
Security: 5th International Workshop, VizSec 2008, Cambridge, MA, USA, September 15, 2008, Proceedings, Lecture Notes in Computer Science, 5210, pp. 111–118. Berlin: Springer- Verlag, 2008.
47

48. Flowstrates: Exploration of Temporal
Origin-Destination Data
Ilya Boyandin, Enrico Bertini, Peter Bak, Denis Lalanne. Flowstrates: An Approach for Visual Exploration of
Temporal Origin-Destination Data, EuroVis 2011
48

49. Applied “Force-Directed Edge Bundling”, Holten 2009
49

50. 10.2 Visualization techniques for serial data
Making a visualization time-dependent
Every visualization can be made time dependent by
providing several visualizations for several time points…
… in parallel
… as a sequence (Animation)
1980
1990
2000

51. 10.2 Visualization techniques for serial data
Time-Series Plot
One Parameter
Several Parameters

52. 10.2 Visualization techniques for serial data
Gantt Chart

53. 10.2 Visualization techniques for serial data
LifeLines
LifeLines for medical records. Consultations, manifestations, documents, hospitalizations and treatments are shown
in this record. Each doctor has a unique color. Line thickness shows severity and dosage.

54. 10.2 Visualization techniques for serial data
History Flow
a
u
t
h
o
rs
Text of
page
Editing history of the wikipedia „Microsoft“ page
History flow visualization

55. 10.2 Visualization techniques for serial data
ThemeRiver
ThemeRiver depicts thematic
variations over time within a large
collection of documents
•
•
horizontal distance between two
points
 time interval
•
total vertical distance
 collective strength of the selected
themes
•
Data: Collection of patents from one company
directed flow from left to right
 movement through time
colored currents
 individual themes

56. 10.2 Visualization techniques for serial data
Histogram vs. ThemeRiver
• Discrete values
• Exact values
• Hard to follow a single current
• Continuous flow
• Interpolation, approximation
• Easy to follow a single current
(curving continuous lines)

57. 10.2 Visualization techniques for serial data
Importance-Driven Visualization
Goal: Display large numbers of time series such that
• relative importance and hierarchy relations can be quickly
perceived
• the time series can easily be compared
(by arranging them in a regular layout)

58. 10.2 Visualization techniques for serial data
Importance-Driven Visualization
80 time series
from 9 different
S&P500 Industries
i-measure: volatility of stocks
color: normalized stock open price from green (low) through yellow (medium) to red (high)

59. 10.2 Visualization techniques for serial data
Space-Time Cube
The space-time cube: I. An example of the author’s travels on an average Thursday in Enschede, the Netherlands. II. The space-time cube’s
basics: a Space-Time Path and its footprint. The vertical line in the path represents the time a person remains at the same location, called
station. III. A Space-Time-Prism (STP) indicates the locations that can be reached in a particular time interval (the Potential Path Space (PPS)).
The projection of the PPS on the map results in the Potential Path Area (PPA).

60. Seesoft
color = statistic of interest, here: code age

61. Seesoft
Color is mapped to code
age.
Three representations of
code in the window:
- text
- line representation
- pixel representation

62. ThemeScape Document Visualization

63. ThemeScape Document Visualization
A themescape representation of
700 articles related to the
financial industry

64. Newsmap (Germany)

65. Text and Geo (1)
Chae et al. 2012
Seasonal Trend
Decomposition
WS 2011 / 12
Computational Methods for Document Analysis, Prof. Dr. D. A. Keim
65

66. Word Clouds – http://wordle.net/
4 years of GK publications at the University of Konstanz
(size of term corresponds to the frequency of the term within the publications)

67. Hyperbolic Browser
A hyperbolic browser
representation of
hierarchically ordered
collection of documents

68. 1.2. Selection Operators
- techniques for
selecting and
highlighting
objects and groups
of objects
point is selected
 highlighted
 and can be
dragged
- often to identify
the set of objects
that will be the
argument to some
action
68

69. 1.3. Filtering Operators
Dynamic Queries =
visual means of
specifying
conjunctions
e.g.:
FilmFinder
by C. Ahlberg and B. Shneiderman
- sliders or radio
buttons to select value
ranges for variables in
the Data Table
- cases for which all the
variables fall into the
specified ranges are
displayed
69

70. 1.3. Filtering Operators
XmdvTool (http://davis.wpi.edu/xmdv/)
Filtering rows and columns of the grades data set using XmdvTool.
70

71. 1.6. Connection Operators
interactive changes made in one visualization are
automatically reflected in the other visualizations
cases that are selected in one view…
… are automatically also selected in all
the other views
Screenshots of XMDV-Tool
71

72. Overview & Detail
Detail
Overview
72

73. 1. Screen Space
Perspective Wall
• The data outside the focal
area are perspectively
reduced in size
• The perspective wall is a
variant of the bifocal lense
display which horizontally
compresses the sides of the
workspace by direct scaling
Documents arranged on a Perspective Wall
73

74. 1. Screen Space - Fisheye
 original graph and fisheye view of the graph
 shows an area of interest quite large and with detail and the other areas
successively smaller and in less detail
 graph visualization using a fisheye perspective
74

75. 5. Data Structure Space
Wei Peng, Matthew O. Ward, and Elke A. Rundensteiner. “Clutter Reduction in Multi Dimensional Data Visualization Using Dimension Reordering.” In INFOVIS ’04: Proceedings of the IEEE
Symposium on Information Visualization, pp. 89–96. Washington, DC: IEEE Computer Society, 2004.
Example of shape simplification via dimension reordering. The left image shows the
original order, while the right image shows the results of reordering to reduce
concavities and increase the percentage of symmetric shapes.
75

76. 6. Visualization Structure Space – TableLens
TableLens with
distortion (expansion)
to show names
Visualization of a baseball database with a few rows being selected in full detail
76

77. 7. Animating Transformations
Example of a velocity curve
corresponding to the position curve,
with ease-in, ease-out movement.
Example of an acceleration curve
corresponding to the position curve,
with ease-in, ease-out movement.
77

78. 3. System Performance - Use Case (1)

Practice Fusion Medical Research Data
15,000 de-identified health records, 7 different tables (patients, diagnosis, medications, etc.)

Data handling and visualization functionality evaluation
Task: visualize the distribution of women’s pregnancy age

79. 3. System Performance - Use case (2)

VAST challenge 2011



1,023,057 geo-tagged microblogging messages with time stamps
map information for the artificial “Vastopolis” metropolitan area
Geo-spatial-temporal data analysis functionality evaluation
Spotfire
Tableau
Qlikview
JMP
Task: visualize the geo-referenced disease outbreaks over the given time span
79

80. VAST 2013 Examples

81. Purdue University
SPRING RAIN
81

85. INRIA (Perin)

86. Arizona State University (Lu)

87. University of Konstanz (el Assady)
2

88. University of Konstanz (Schreck)
2

89. University of Stuttgart (Kruger)
3

90. Middlesex University (O’Connor-Read)

91. Central South University (Zhao)

92. Middlesex University (Choudhury)
92

93. Purdue University
93

94. SAS

95. Central South University

96. Peking University and Universität Stuttgart

97. University of Konstanz

98. University of North Carolina Charlotte