Introduction to Information Visualization Human-Computer Interaction - Bogor Agricultural University (IPB) 2015 ✤
http://www.infovis-wiki.net/images/8/8e/Infovis-wiki_tagcloud_20090827.png
Referensi
Success Story Illustration of John Snow’s deduction that a cholera epidemic was caused by a bad water pump, circa 1854. Horizontal lines indicate location of deaths.
From Visual Explanations by Edward Tufte, Graphics Press, 1997
London Underground Map 1927
4
London Underground Map 1990s
5
Introduction to Visual Representation http://www.informationisbeautiful.net/2011/vintage-infoporn-no-1/
12 BISON
11
8 DEWASA
4 ANAK
12
# Adults
# calfs
13
1
3 4 2
5 7
6 8 9
12
10 11 15
Egyptian Numerals
Which chart? Gender
Total
Male
36
Female
64
Male 36% Female 64%
70
64 52,5
35
36
17,5
0 Male
Female
36
Male
64
Female
0
17,5
35
52,5
70
70
52,5
35
17,5
0 Male
Female
70
52,5
35
17,5
0 Male
Female
First Impression
A
B
2 dari 5 orang bersifat pemalu
When do I take my drugs? 10 - 30% error rate in taking pills, same for pillbox organizers
Inderal Lanoxin Carafate Zantac Quinag Couma
- - - - - -
1 tablet 3 times a day
1 tablet every a.m.
1 tablet before meals and at bedtime
1 tablet every 12 hours (twice a day)
1 tablet 4 times a day
1 tablet a day
A
B Adapted from Donald Norman
Which folder has the most documents? right menu
+ properties
Windows 95 File Viewer
Explorative Analysis
Confirmative Analysis
Information Visualization is the use of computer-supported interactive visual representations of abstract data to amplify cognition.
Scientific Visualization
✤
Scientific visualization is a discipline that aims to visually represent the results of scientific experiments or natural phenomena
Creating Visual Representations Graph Words: A Free Visual Thesaurus Of The English Language
A Reference Model Date
Preprocessing and Data Transformations
filtering, calculations, add attributes
Case
Visual Mapping ✤
which visual structures to use to map the data and their location in the display area
✤
Three structures must be defined: ✤ 1. spatial substrate, ✤ 2. graphical elements, ✤ 3. graphical properties
Views Problem : very large amount of data? zooming, panning, scrolling, focus + context, magic lenses
Visual Variables Date
Jock D. Mackinlay
Mackinlay (simplified)
Jacques Bertin
Bertin’s Visual Variables Characteristics of visual symbols How we distinguish between them
Slides by Sheelagh Carpendale, University of Calgary
Visual variables - attributes position – changes in the x, y (z) location
size – change in length, area or repetition
shape – infinite number of shapes
value – changes from light to dark
orientation – changes in alignment
colour – changes in hue at a given value
texture – variation in pattern
motion
Saul Greenberg
Visual variables - characteristics Different variable attributes may be:
– selective is a change enough to allow us to select it from a group?, If a mark changes in this variable and as an effect can be selected from the other marks easily the visual variable is said to be selective.
– associative is a change enough to allow us to perceive them as a group? Several marks can be grouped across changes in other visual variables.
– quantitative is there a numerical reading obtainable from changes in this variable? If the difference between two marks in this variable can be interpreted numerically, the visual variable is quantitative.
Saul Greenberg
Visual variables - characteristics Different variable attributes may be:
– order are changes in this variable perceived as ordered? If the variable supports ordered reading it is an ordered visual variable. This means that a change could be read as more or less (e.g. in size you can order marks according to their area).
– length across how many changes in this variable are distinctions perceptible? The length defines how many values the variable features. For example how many shades of grey can be recognised.
Saul Greenberg
Position selective associative quantitative
100
order length 0
0
10
Size selective associative quantitative
4X
=
?
order >
>
>
length – theoretically infinite but practically limited – association and selection ~ 5 and distinction ~ 20
>
>
>
Shape selective associative quantitative order
>
>
>
length - infinite variation
>
>
>
>
Shape
Value selective associative quantitative order
>
>
How many 5’s?
385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024
[Slide adapted from Joanna McGrenere http://www.cs.ubc.ca/ ~joanna/ ]
Saul Greenberg
How many 5’s?
385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024
Saul Greenberg
Color
Orientation selective associative quantitative order length – ~5 in 2D; ? in 3D
