introduction to information visualizationweb.ipb.ac.id/~f_ardiansyah/imk/bahan kuliah/imk...
TRANSCRIPT
2015
Introduction to Information VisualizationHuman-Computer Interaction - Bogor Agricultural University (IPB)
✤ http://www.infovis-wiki.net/images/8/8e/Infovis-wiki_tagcloud_20090827.png
Referensi
From Visual Explanations by Edward Tufte, Graphics Press, 1997
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.
Success Story
4
London Underground Map 1927
5
London Underground Map 1990s
Introduction to Visual Representationhttp://www.informationisbeautiful.net/2011/vintage-infoporn-no-1/
11
12BISON
12
8 4DEWASA ANAK
13
# Adults # calfs
15
12
12
34
5 6
7 8
910
11
Egyptian Numerals
Which chart?
Gender Total
Male 36
Female 64
Female64%
Male36%
0
17,5
35
52,5
70
Male Female
64
36
Male
Female
0 17,5 35 52,5 70
64
36
0
17,5
35
52,5
70
Male Female
0
17,5
35
52,5
70
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 - 1 tablet 3 times a day Lanoxin - 1 tablet every a.m.Carafate - 1 tablet before meals and at bedtimeZantac - 1 tablet every 12 hours (twice a day)Quinag - 1 tablet 4 times a dayCouma - 1 tablet a day
Adapted from Donald Norman
A
B
right menu+ properties
Which folder has the most documents?
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
Graph Words: A Free Visual Thesaurus Of The English Language
Creating Visual Representations
Date
A Reference Model
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
ViewsProblem : very large amount of data? zooming, panning, scrolling, focus + context, magic lenses
Date
Visual Variables
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
Saul Greenberg
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.
Position
selective
associative
quantitative
order
length
100
0100
selective
associative
quantitative
order
length –theoretically infinite but practically limited –association and selection ~ 5 and distinction ~ 20
Size
> >> > > >
=4 X ?
selective
associative
quantitative
order
length - infinite variation
Shape
>> >> > > >
Shape
Value
selective
associative
quantitative
order
length –theoretically infinite but practically limited –association and selection ~ < 7 and distinction ~ 10
< < < < < <
Saul Greenberg
Just-Noticeable Difference
• Which is brighter?
Saul Greenberg
Just-Noticeable Difference
• Which is brighter?
(130, 130, 130) (140, 140, 140)
Color
selective
associative
quantitative
order
length –theoretically infinite but practically limited –association and selection ~ < 7 and distinction ~ 20
> > > > > > >>
Saul Greenberg
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
Color
Orientation
selective
associative
quantitative
order
length –~5 in 2D; ? in 3D
< < < < < < <?
Texture
selective
associative
quantitative
order
length –theoretically infinite
> > > >
Motion
selective –motion is one of our most powerful attention grabbers
associative –moving in unison groups objects effectively
quantitative –subjective perception
order
length –distinguishable types of motion??
Motion
Quiz: Visualisasi Jadwal Penerbangan Jakarta - Sorong
Perhitungkan perbedaan waktu antar kota, lama penerbangan, transit, stop over. Jam Berangkat dan Datang sesuai dengan wilayah waktu kota tersebut (WIB/WITA/WIT) GA : Garuda IndonesiaQZ : AirAsia
Ujian (selengkapnya di LMS)
✤ Buatlah Visualisasi Informasi berdasarkan data pada buku STATISTIK INDONESIA 2014 yang dapat diunduh di http://bps.go.id
✤ Ambil data (bebas) yang dapat anda representasikan dalam bentuk visual.
✤ Hasil diunggah ke blog/website pribadi
✤ Waktu pengerjaan 2 minggu
Kriteria Penilaian
1. Akurasi Data yang ditampilkan, disertai sumber data (detil halaman) - 10 pt
2. Interaktivitas - 10 pt3. Pemilihan Visual Variables -20 pt4. Jumlah variabel / complexity - 20 pt5. Usabilitas / Kemudahan pembacaan dan penggunaan /readable
figure - 20 pt6. Aestetik (warna dan desain, menerapkan konsep desain) - 15 pt7. Availability (tersedia di blog / web site ) - 5pt
Referensi
✤ http://infosthetics.com/
✤ http://www.informationisbeautifulawards.com/
✤ http://www.informationisbeautiful.net/
✤ http://visualizing.org/
Tools
✤ Gephi : gephi.github.io/
✤ Piktochart : http://piktochart.com/
✤ visme.co
✤ canva.com
✤ easel.ly
✤ infogr.am
✤ venngage.com
✤ vizualize.me
✤ http://www.dipity.com/
Memory
Preattentive Properties
Color
Form
Spatial Position
Movement
Mapping Data to Preattentive
Postattentive Processing
Gestalt Principles
Figure & Ground
Proximity
Similarity
Aesthetics
Presentation
Explorative Analysis
Confirmative Analysis
Information Visualization
From Data to Wisdom
Scientific Visualization
Criteria for Good Visual Representation
Graphical Excellence
Graphical Integrity
Maximize the Data-Ink Ratio
Aesthetics
Presentation
Explorative Analysis
Confirmative Analysis
Information Visualization
From Data to Wisdom
Scientific Visualization
Criteria for Good Visual Representation
Graphical Excellence
Graphical Integrity
Maximize the Data-Ink Ratio
Aesthetics
Presentation
Explorative Analysis
Confirmative Analysis
Information Visualization
From Data to Wisdom
Scientific Visualization
Criteria for Good Visual Representation
Graphical Excellence
Graphical Integrity
Maximize the Data-Ink Ratio
Aesthetics
Presentation
Explorative Analysis
Confirmative Analysis
Information Visualization
From Data to Wisdom
Scientific Visualization
Criteria for Good Visual Representation
Graphical Excellence
Graphical Integrity
Maximize the Data-Ink Ratio
Aesthetics
Analytic Methods
Empirical Methods