Textual Data; Visual Variables - PowerPoint PPT Presentation

1 / 40
About This Presentation
Title:

Textual Data; Visual Variables

Description:

Textual Data; Visual Variables Using visual variables (1) (cf. tutorial) Sameness of a visual element implies sameness of what the visual element represents ... – PowerPoint PPT presentation

Number of Views:114
Avg rating:3.0/5.0
Slides: 41
Provided by: unitu155
Category:

less

Transcript and Presenter's Notes

Title: Textual Data; Visual Variables


1
Textual Data Visual Variables
2
Administration
  • Sprechstunden
  • T 14-15, Th 16-17
  • (on the web site)
  • Online collaboration tools?
  • e.g. wikis

3
Languages
  • What languages do people know, and to what
    extent? (Basic, Intermediate, Advanced)
  • CuC
  • Advanced English
  • Intermediate French, German, Italian, Spanish
  • Basic Bambara, Fula, Dogon

4
Visualizing the metadata
  • How might we visualize this metadata?
  • Better how do we think about visualizing it?
  • Important aspects
  • What is the data? (The data)
  • Who will be using it? (The user)
  • What will they be trying to do? (The task)
  • cf. Data repository project

5
The project
  • Goal develop a scientific visualization of some
    kind of linguistic data
  • Start thinking about what kind of data you want
    to visualize, and where you'll get it
  • Who Small groups
  • If you are inexperienced in programming, work
    with someone who is more experienced
  • Progress? Questions?

6
Current visualizations
  • What LInfoVis visualizations have you used?
  • For what purposes?
  • Did/do you wish they could be better? How?

7
About the references in the tutorial
  • One more
  • Ware, Colin. 2004. Information Visualization,
    Second Edition Perception for Design.
  • Bertin seminal work on visual variables
  • Card, McKinlay, Shneiderman, Ware Important
    research contributions to InfoVis
  • Hearst Interesting combination of InfoVis, UI
    design, information retrieval
  • Tufte Very influential (sometimes controversial)
    about presentation of information

8
Data types(cf. Tutorial, Hearst, 2009)
  • Quantitative data numbers, etc. that can be
    processed arithmetically
  • Categorical data everything else
  • Interval ordered data with measurable distances
    (e.g. months)
  • Ordinal ordered data without measurable
    distances (e.g. hot-warm-cold)
  • Nominal data without (relevant) organization
    (e.g. weather types, a collection of names)
  • Hierarchical data without order, arranged into
    subsuming groups (e.g. mammals, bear ,
    cat lynx,,, , etc.) cf. GermaNet
  • ? Quantitative, interval, and ordered data are
    easier to convey visually than nominal data.

9
More on hierarchical data
  • Some hierarchical data in linguistics is a bit
    more complicated.
  • Consider a linguistic analysis tree.
  • data without order
  • arranged into subsuming groups
  • Now consider a treebank (collection of trees)
  • data without order (trees dont have, but what
    about metadata?)
  • Arranged into subsuming groups (possible, but not
    natural)

10
Textual data(cf. tutorial)
  • We are interested in the information/properties
    of textual elements e.g. word frequency,
    syntactic structure, emotion content, etc.
  • However, in many cases, the actual textual items
    are important for understanding the information,
    so they must be indicated in the visualization.
  • The categorical nature of text, and its very
    high dimensionality, make it very challenging to
    display graphically. (Hearst, 2009)

11
Non-mappability of text (cf. tutorial)
  • The problem is less about the information about
    textual elements, but the textual elements
    themselves they take up space.
  • Textual items are not mappable
  • We (usually) cannot effectively represent them by
    something else meaningful (e.g. shape, color,
    position, etc.)
  • Textual items are too variable (Hearsts high
    dimensionality) and too complex to be reduced to
    a more compact representation, even a label
  • The details of the textual items are often
    crucial to understanding the data (e.g. context
    in a concordance)

12
What to do? (cf. tutorial)
  • This is a huge challenge for LInfoVis!
  • Not a solved problem
  • Interactive visualizations will be the key to the
    solution(s)
  • Show only some of the data, and interact to get
    more
  • Data filtering/selection will also be key
  • Cf. Data filtering/selection in InfoVis more
    generally
  • Need domain and task specific information

13
Visual variables
  • A visual variable is a visual property that can
    be varied in order to convey (encode) information.

14
Visual data transcription visual variables
(from tutorial)
Value Brightness
Taken from M. Carpendale, "Considering visual
variables as a basis for information
visualisation, Dept. of Computer Science,
University of Calgary, Canada, Tech. Rep.
2001-693-16, 2003, Table 1.
14
15
Visual variables characteristics (1) (from
tutorial)
  • 5 key characteristics
  • Selectivity Different values are easily seen as
    different
  • Is A different from B?
  • Worst case visual properties of all objects need
    to be looked at one by one

15
16
Visual variables characteristics (2) (from
tutorial)
  • Associativity Similar values can easily be
    grouped together
  • Is A similar to B?
  • Positioning gt size, brightness gt color,
    orientation (for points) gt texture gt shape

Full selectivity / associativity
No selectivity / associativity
16
17
Visual variables characteristics (3) (from
tutorial)
  • Order Different values are perceived as ordered
  • Is A more/greater/bigger than B?
  • Size and brightness are ordered
  • Orientation, shape, texture are not ordered
  • Hue is somewhat ordered

17
18
Visual variables characteristics (4) (from
tutorial)
  • Quantity A number can be deduced from
    differences
  • How much is the difference between A and B?
  • Position is quantitative, size is somewhat
    quantitative
  • The other variables are not quantitative

18
19
Visual variables characteristics (5) (from
tutorial)
  • Length The number of distinctions possible using
    the variable
  • How many different things can we represent with
    this variable?
  • Shape, Texture infinite, but
  • Brightness, hue 7 (Association) 10
    (Distinction)
  • Size 5 (Association) -20 (Distinction)
  • Orientation 4

19
20
An experiment
21

Made with http//www.wordle.net/
22
Made with http//www.tocloud.com/
23
Combining visual variables
  • When we use 2 visual variables for an element,
    how independently do we perceive the two
    variables?
  • If we perceive them separately, they are
    separable.
  • If we perceive them together, they are integral.

24
Test 1

C
B
A
25
Test 2

C
B
A
26
Integral/Seperable
  • Red/green and yellow/blue
  • Width and height
  • Size and orientation
  • Color and shape
  • Color and motion


  • Ware 2004181
  • Color and location

27
Integral/Seperable
  • Red/green and yellow/blue
    More integral
  • Red/green and black/white
  • Width and height
  • Shape and size
  • Color and size
  • Shape and direction of motion
  • Color and shape
  • Color and direction of motion
  • Position and size OR shape OR color More
    separable


  • cf. Ware 2004180

28
Gestalt psychology and perception
  • Early 20th century, with a lot of work on aspects
    of visual perception and how people organize what
    they see.
  • Relevant for visualization
  • Reification (somewhat)
  • we perceive more information than
  • is present
  • e.g. illusory contours
  • Source http//en.wikipedia.org/wiki/FileKanizsa_
    triangle.svg

29
Gestalt Principles of Grouping
  • Some basic sources
  • http//psychology.about.com/od/sensationandpercept
    ion/ss/gestaltlaws.htm
  • http//en.wikipedia.org/wiki/Principles_of_groupin
    g
  • http//en.wikipedia.org/wiki/Gestalt_psychology
  • More detail
  • http//www.scholarpedia.org/article/Gestalt_princi
    ples

30
Gestalt Principles of Grouping
  • Similarity
  • Objects with common visual attributes are
    perceived as being part of the same group
  • Source
  • http//psychology.about.com/od/sensationandpercept
    ion/ss/gestaltlaws_2.htm

31
Gestalt Principles of Grouping
  • Proximity
  • Objects that are near each other in space (or
    time) are perceived as forming a group.

O O O O O
O O O O O
O O
32
Gestalt Principles of Grouping
  • Continuity
  • A pattern of objects (in space, time) tends to be
    continued.
  • Source http//www.scholarpedia.org/article/Gestal
    t_principles

33
Gestalt Principles of Grouping
  • Law of Common Fate
  • Objects moving in the same direction are
    perceived as a group.
  • Note Difficult to use in (L)Infovis, since we
    need to be able to focus easily on the
    information.

34
Using visual variables (1) (cf. tutorial)
  • Sameness of a visual element implies sameness of
    what the visual element represents
  • (Tufte, 2006)
  • Cf. Principles of grouping

35
Using visual variables (2) (cf. tutorial)
  • Characteristics of visual variables determine
    their use
  • e.g. Ordered values have to be represented by
    ordered visual variables

36
Using visual variables (3) (cf. tutorial)
  • Be consistent concerning relations of similarity,
    proportion and configuration

37
Using visual variables (4) (cf. tutorial)
  • Adhere to conventional uses of visual variables
  • e.g. in cartography use blue color for water
  • Scales should be made up of visually equidistant
    values of a variable

38
Using visual variables (5) (cf. tutorial)
  • The full range of a visual variable should be
    used
  • e.g. when using shades of gray, use from white to
    black
  • The number of visual variables of a visualization
    should correspond to the dimensionality of the
    represented information
  • But sometimes dual encoding can be useful

39
Using visual variables (6)
  • When combining two visual variables, if people
    should be able to analyze the two attributes
    independently, then separable variables should be
    used.

40
For next time
Write a Comment
User Comments (0)
About PowerShow.com