Lecture 7: Single View Methods Information Visualization CPSC 533C, - - PowerPoint PPT Presentation

▶
lecture 7 single view methods
SMART_READER_LITE
LIVE PREVIEW

Lecture 7: Single View Methods Information Visualization CPSC 533C, - - PowerPoint PPT Presentation

Apr 03, 2023 18 likes •359 views

Lecture 7: Single View Methods Information Visualization CPSC 533C, Fall 2011 Tamara Munzner UBC Computer Science Wed, 28 September 2011 1 / 34 Required Readings Chapter 5: Single View Methods Trellis paper moved to Multiple Views on Monday

slide-1
SLIDE 1

Lecture 7: Single View Methods

Information Visualization CPSC 533C, Fall 2011 Tamara Munzner

UBC Computer Science

Wed, 28 September 2011

1 / 34

slide-2
SLIDE 2

Required Readings

Chapter 5: Single View Methods Trellis paper moved to Multiple Views on Monday

2 / 34

slide-3
SLIDE 3

Further Reading

Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization. Friendly and Denis. http://www.math.yorku.ca/SCS/Gallery/milestone/ Bars and Lines: A Study of Graphic Communication. Zacks and

  • Tversky. Memory and Cognition 27(6):1073-1079, 1999.

Multi-Scale Banking to 45 Degrees. Heer and Agrawala. IEEE TVCG 12(5) (Proc. InfoVis 2006), Sep/Oct 2006, pages 701-708. Overview Use in Multiple Visual Information Resolution Interfaces. Lam, Munzner, and Kincaid. Proc. InfoVis 2007. VisDB: Database Exploration using Multidimensional Visualization. Keim and Kriegel. IEEE CG&A, 1994

3 / 34

slide-4
SLIDE 4

Principles, Methods, and Techniques...

part 1: principles (3 chapters)

why underlying many design decisions data, visual encoding, interaction

part 2: methods (4 chapters)

what are the axes of the (current) design space taxonomy of design considerations

how many views? single, multiple how to reduce what’s shown? data, dimensions

part 3: techniques (3 lectures [∼4 chapters...])

analyze techniques by which methods/principles used tables, graphs, (text/logs), spatial grouped by data type to follow nested model

technique level design happens after data type chosen at abstraction level

4 / 34

slide-5
SLIDE 5

... and Practice

part 4: practice (2 lectures)

problem identification and task abstraction validation at problem, abstraction, technique levels research process/papers

5 / 34

slide-6
SLIDE 6

Experiment

which lecture style works best? summarize chapters thoroughly

last several lectures if book doing its job, maybe other choices viable!

summarize lightly

also bring up other ideas/approaches more time for discussion trying this today

end of class: get feedback from you

6 / 34

slide-7
SLIDE 7

Single View Methods

all information integrated in one view basic visual encodings

spatial position color

  • ther channels

pixel-oriented techniques

visual layering

global compositing item-level stacking

glyphs

7 / 34

slide-8
SLIDE 8

Spatial Position

most statistical graphics

bar chart, histogram

8 / 34

slide-9
SLIDE 9

Spatial Position

most statistical graphics

bar chart, histogram, dot plot, line chart

9 / 34

slide-10
SLIDE 10

Statistical Graphics

heavy focus on spatial position for visual encoding long history for paper-based views of data

springboard for infovis http://www.datavis.ca/milestones/

many ways to make interactive (more later) many ways to refine/improve/combine

10 / 34

slide-11
SLIDE 11

Line Charts

invented by William Playfair (1759-1823)

also bar charts, pie charts, ...

http://labspace.open.ac.uk/file.php/1872/Mu120 3 021i.jpg http://www.math.yorku.ca/SCS/Gallery/images/playfair-wheat1.gif

11 / 34

slide-12
SLIDE 12

Banking to 45 Degrees

previous work by Cleveland perceptual principle: most accurate angle judgement at 45 degrees pick line graph aspect ratio (height/width) accordingly

[www.research.att.com/∼rab/trellis/sunspot.html]

12 / 34

slide-13
SLIDE 13

Multiscale Banking to 45

frequency domain analysis find interesting regions at multiple scales

[Multi-Scale Banking to 45 Degrees. Heer and Agrawala, Proc InfoVis 2006 vis.berkeley.edu/papers/banking]

13 / 34

slide-14
SLIDE 14

Choosing Aspect Ratios

FFT the data, smooth by convolve with Gaussian find interesting spikes/ranges in power spectrum cull nearby regions if too similar, ensure overview shown create trend curves for each aspect ratio

14 / 34

slide-15
SLIDE 15

Multiscale Banking to 45

[Multi-Scale Banking to 45 Degrees. Heer and Agrawala, Proc InfoVis 2006 vis.berkeley.edu/papers/banking]

15 / 34

slide-16
SLIDE 16

Bar vs Line Charts

line implies trend, do not use for categorical data

  • Female

Male 10 20 30 40 50 60 Height (inches) Female Male 10 20 30 40 50 60 Height (inches) 10-year-olds 12-year-olds 10 20 30 40 50 60 Height (inches)

  • 10-year-olds

12-year-olds 10 20 30 40 50 60 Height (inches)

[Fig 2. Zacks and Tversky. Bars and Lines: A Study of Graphic Communication. Memory and Cognition 27(6):1073-1079, 1999.]

16 / 34

slide-17
SLIDE 17

Scatterplots

encode two input variables with spatial position show positive/negative/no correllation between variables show clusters: clumpiness/density, shape, overlap

[http://upload.wikimedia.org/wikipedia/commons/0/0f/Oldfaithful3.png]

17 / 34

slide-18
SLIDE 18

Scatterplots

  • r compare correlation/clusters for two position attributes

against more attributes encoded with color/shape

[Fig 1c. Robertson et al. Effectiveness of Animation in Trend Visualization. IEEE

  • Trans. on Visualization and Computer Graphics 14(6):1324-1332 (Proc. InfoVis08),

2008.]

18 / 34

slide-19
SLIDE 19

Colormap Taxonomy

http://www.colorbrewer.org

[Brewer, www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]

19 / 34

slide-20
SLIDE 20

Rainbows: The Good, The Bad, The Ugly

[Fig 1. Rogowitz and Treinish. Data visualization: the end of the rainbow. IEEE Spectrum 35(12):52-59 1998.] [Fig 2,1. Bergman and Rogowitz and Treinish. A Rule-based Tool for Assisting Colormap Selection. Proc. IEEE Vis 1995, p 118-125.] [Kindlmann. http://www.cs.utah.edu/ gk/lumFace]

20 / 34

slide-21
SLIDE 21

Accuracy/InfoDensity Tradeoff: Position/Color

!"#$ !"#%

$% $& gh12 gh23

  • -.778../112

!778../778

  • -.556../001

!889../334

  • -.334../889

!556../445

  • -.556../223

!334../001

  • -.445../445

!445../667 !001../001 !445../223

  • -.334../445

!223../334

  • -.112../556

!334../99:

st12 st23 st34 st45 st56 st67 st78 st89

[Fig 4b,4a. Meyer et al. Pathline: A Tool for Comparative Functional Genomics.

  • Proc. EuroVis 10, p 1043-1052.]

21 / 34

slide-22
SLIDE 22

Tradeoff: Empirical Study

[Fig 1. Lam, Munzner, and Kincaid. Overview Use in Multiple Visual Information Resolution Interfaces. Proc. InfoVis 2007]

22 / 34

slide-23
SLIDE 23

Study: Control Room Scenario

Which location has the highest power surge for the given time period? (find extreme value, y-dimension) A fault occurred at the beginning of this recording, and resulted in a temporary power surge. Which location is affected the earliest? (find extreme value, x-dimension)

[Lam, Munzner, and Kincaid. Overview Use in Multiple Visual Information Resolution

  • Interfaces. Proc. InfoVis 2007]

23 / 34

slide-24
SLIDE 24

Study: Findings

tasks

Max: simple, local, no comparison Most: complex, dispersed, no comparison Shape: complex, local, comparison Compare: simple, local, comparison

results

low-res / high-density used:

simple/local targets

(other findings about focus+context vs overview/detail)

see also horizon graphs study

24 / 34

slide-25
SLIDE 25

Pixel-Oriented Methods: VisDB

how to draw pixels?

sort, color by relevance

local ordering spiral 2D

[VisDB: Database Exploration using Multidimensional Visualization, Keim and Kriegel, IEEE CG&A, 1994]

25 / 34

slide-26
SLIDE 26

VisDB Windows

grouped dimensions separate dimensions

[VisDB: Database Exploration using Multidimensional Visualization, Keim and Kriegel, IEEE CG&A, 1994]

26 / 34

slide-27
SLIDE 27

VisDB Results: Separate Dimensions

spiral 2D

[VisDB: Database Exploration using Multidimensional Visualization, Keim and Kriegel, IEEE CG&A, 1994]

27 / 34

slide-28
SLIDE 28

VisDB Results: Grouped Dimensions

[VisDB: Database Exploration using Multidimensional Visualization, Keim and Kriegel, IEEE CG&A, 1994]

28 / 34

slide-29
SLIDE 29

Visual Layering

beyond simple use of visual channels method variants

global compositing: everything superimposed item-level stacking

major consideration

static layers: disjoint ranges in channels safest dynamic/interactive layers: more freedom

29 / 34

slide-30
SLIDE 30

Visual Layering: Constellation

global compositing, dynamic layers video

[Munzner. Constellation: Linguistic Semantic Networks. Interactive Visualization of Large Graphs and Networks (PhD thesis) Chapter 5, Stanford University, 2000, pp 87-122. http://graphics.stanford.edu/papers/munzner thesis]

30 / 34

slide-31
SLIDE 31

Glyphs

compound marks macro (small picture) vs micro (texture) channel questions

separability effectiveness principle: importance matching

[Fig 9. Information Rich Glyphs for Software Management, IEEE CG&A 18:4 1998] [Fig 2. Smith and Grinstein and Bergeron. Interactive data exploration with a

  • supercomputer. Proc. IEEE Visualization (Vis) 1991, p. 248-254]

31 / 34

slide-32
SLIDE 32

Questions/Discussion

32 / 34

slide-33
SLIDE 33

Experiment: Feedback

which lecture style works best? summarize chapters thoroughly

last several lectures if book doing its job, maybe other choices viable!

summarize lightly

more time for other/further ideas/approaches more time for discussion trying this today

your preferences?

33 / 34

slide-34
SLIDE 34

Reading For Next Time

Chapter 6: Multiple View Methods The Visual Design and Control of Trellis Display. R. A. Becker, W.

  • S. Cleveland, and M. J. Shyu (1996). Journal of Computational

and Statistical Graphics, 5:123-155.

34 / 34