information visualization
DESCRIPTION
Information Visualization. UI lab. 이 석 재. Goal. Data. Data transfer. Insight (learning, knowledge extraction). Method. Data. Data transfer. Insight. Map -1 visual → data insight. Map: data → visual. Visualization. Visual transfer. (communication bandwidth). Visual Mappings. - PowerPoint PPT PresentationTRANSCRIPT
Information Visualization
UI lab. 이 석 재
Data
Data transfer
Insight(learning, knowledge extraction)
Goal
Data
Visualization
Map: data → visual
Map-1 visual → data insight
Data transfer
Insight
Visual transfer
(communication bandwidth)
Method
Data
Visualization
Map: data → visual
Visual Mappings must be:• Computable (math)
visual = f(data)
• Comprehensible (invertible)data = f-1(visual)
• Creative!
Visual Mappings
Effectiveness
• User learnability: Learning time Retention time
• User performance: *** Performance time Success rates Error rates, recovery Clicks, actions
•User satisfaction: Surveys
Introduction
• To understand something is called “seeing” it.• Visual metaphors – a nexus of relationships between what
we see and what we think.• How have we increased memory, thought, and reasoning? -By the invention of external aids.
Multiplication Aids
• Visual and manipulative use of the external world amplifies cognitive performance.
• Why does using pencil and paper make such a difference? - What is hard is holding the partial results in memory until
they can be used. ->The visual representation, by holding partial results outside
the mind, extends a person’s working memory.
Multiplication Aids
• slider rule 1. an analogue interactive visual device that represents
quantities as scales with length proportional to their logarithms 2. actually does the visual computation
Navigation charts
• The map is not just a calculator, but also a storage device, storing for access enormous amounts of information naturally located near where they are needed for calculation.
• Diagrams can lead to great insight, but they can also lead to the lack of same.
• The decision depended on whether the temperature would make the O-rings that sealed the sections of the booster rockets unsafe.
Diagrams
Diagrams
Diagrams
INFORMATION VISUALIZATION
• VISUALIZATION Definition : The use of computer-supported, interactive,
visual representations of data to amplify cognition.
• Purpose : insight, not pictures
• Both of these visualizations show abstractions, but the abstractions are based on physical space=>SCIENTIFIC VISUALIZATION
ORIGINS OF INFORMATION VISUALIZATION
• Work in data graphics dates from about the time of Playfair(1750), who seems to be among the earliest to use abstract visual properties such as line and area to represent data visually (Tufte, 1983)
• Tukey (1977) began a movement from within statistics with his work on Exploratory Data Analysis. (Box plot)
• The first use of the term information visualization to our knowledge was in Robertson, 1989.
Active Diagrams
• The Periodic Table, originally developed by Mendeleyev, is an important diagrams in the development of chemistry.
• Figure 1.12 shows an information visualization based on the Periodic Table (Ahlberg, 1992) The user can set sliders that control which of the elements in the table will be highlighted.
LARGE-SCALE DATA MONITORING
• Information visualization to monitor and make sense of large amounts of dynamic, real-time data (decision-support application)
Information Chromatography
Visualization is used to detect telephone fraud
Information chromatography :
Patterns in the data are revealed by laying them out on a particular visual substrate.
Knowledge Crystallization
We have said that the purpose of information visualization is to use perception to amplify cognition
Knowledge Crystallization
Visualization Levels of Use
• Visualization on four levels of use(1) Visualization of the infosphere(2) Visualization of an information workspace
Visualization Levels of Use
• Visualization on four levels of use(3) Visual knowledge tools(4) Visual Objects
Cost Structure
Cost Structure
– Cost-of-Knowledge Characteristic Function
How Visualization Amplifies Cognition
(1) By grouping.(2) About a single element.(3) Easy for human
Mapping Data to Visual Form
• We can think of visualization as adjustable mapping from data to visual form to the human perceiver.
Data Table
• The usual strategy is to transform this data into relation that are mare structured and thus easier to map to visual form.
• Mathematical treatment omits descriptive information that is important for visualization
DATA TABLE
(case by variables arrays)
• Bertin(1977/1981)- cases -> objects- variables -> characteristics- function, input variable, output variable
Data Table
• Data table can undergo data transformations that affect their structure.
Data Table
• Data table can describe hierarchical and network data.
Data Table
• N = nominal variableO = ordinal variableQ = quantitative variable
• Elementary choices for data transformations derive from the variables types(Q->O, O->N, N->O)
• Subtypes that represent important properties(Qt = Quantitative Time)
Variable Type
• Metadata is descriptive information about data.• Metadata can be important in choosing visualization• An important form of metadata is the structure of a Data
Table.• Additional metadata could be explicitly to the Data Table by
adding
Metadata
Data Transformations
• Concatenated to form chains of aggregation and classing as part of the knowledge crystallization
• Can be used to detect more patterns
Data Transformations
Aggregation cycle
Data Transformations
Visual Structures
• Data tables Visual Structures Mapped with Marks and Graphical properties
• Effectiveness
Data Transformations
Visual Stuctures
Level of the visual system
1st level : Retina
- Retina is good at detecting movement or other changes
2nd level : foveola ( 황반 )
- preattentive and stereoscopic processing
3rd level : within the foveola
- 황반의 중심부에 움푹 패인 부분 ( 중심와 )
Perception
Visual information processing
Controlled processing : Textual description
- detailed, serial, low capacity, slow
Automatic processing : Poping out during search
- Parallel, high capacity, fast
Interaction among the visual codings of information
- Produce patterns
Visual Stuctures
The most fundamental aspect of a Visual Structure is its
use of space
: Spatial position is a good visual coding of data
Visual Stuctures
Spatial Substrate
Several techniques to increase the amount of
information
Composition : orthogonal placement of axes, creating a 2D
space
Alignment : Repetition of an axes at a different position in
the space
Visual Stuctures
Folding : continuation of an axis in an orthogonal dimension
Visual Stuctures
Recursion and Overloading
Visual Stuctures
View structureView structure
Connection and Encloser
1. Connection
Link connection
2. Encloser
Link encloser
Retinal Properties
- retina properties
- 자동적으로 process 되는 visual feature
- relative effectiveness of different retinal properties
Temporal encoding
- Some variable time
View structureView structure
Location Probe
View TransformationView Transformation
Viewpoint control - zoom, pan - overview + detail
Distortion - focus + context view - bifcoal lens
Distortion - focus + context view - bifocal lens
View TransformationView Transformation
Interaction and transformation controls
• Hyperbolic tree 에서 node 는 마우스를 이용하여 display의 중앙으로 드래그 할 수 있음 .