introduction to visualization - noderīgi resursi rtu ditf …ditf.afraid.org/ditf/2...
TRANSCRIPT
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
INTRODUCTIONINTRODUCTION
TOTO
VISUALIZATIONVISUALIZATION
Department of Modelling and Simulation
Riga Technical University
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
•• Visualization finds ancestry in pictogramsVisualization finds ancestry in pictograms– E.g. travel, Da Vinci’s airplanes, architecture – human
generated
History
Leonardo da Vinci's drawing of a helicopter
One of Leonardo da Vinci’s flying machines - the ornithopter.
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
History
•• ComputerComputer--generated since late 40generated since late 40’’ss– Large tables expressed as plots, statistical data for exploration
•• Mid 80Mid 80’’s: need and opportunity grows: need and opportunity grow– Increased data rates from
•• measuring devices: e.g. space missions, medical measuring devices: e.g. space missions, medical instrumentsinstruments
•• scientific computing: e.g. start of supercomputer scientific computing: e.g. start of supercomputer centers, computational sciences (CFD or Molecular centers, computational sciences (CFD or Molecular Modeling)Modeling)
– Mature and cheap technology: powerful graphical workstations, color, sufficient memory and storage
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
•• Committee on Committee on ““Graphics, Image Graphics, Image Processing, and WorkstationsProcessing, and Workstations”” (1986)(1986)– Sponsored by NSF / Division of Advanced Scientific
Computing
•• Goal of committeeGoal of committee– Recommendations to HW / SW builders to improve scientific
productivity
•• Result of committeeResult of committee– Recommendations to research communities to develop new
concepts / techniques for “Visualization in Scientific Computing (ViSC)”
History
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
•• ““To visualizeTo visualize””: form a mental vision, image, or picture of : form a mental vision, image, or picture of (something not visible or present to sight, or of an (something not visible or present to sight, or of an abstraction); to make visible to the mind or imagination abstraction); to make visible to the mind or imagination [The Oxford English Dictionary, 1989][The Oxford English Dictionary, 1989]
•• ““VisualizationVisualization””: a computer generated image or collection : a computer generated image or collection of images, possibly ordered, using a computer of images, possibly ordered, using a computer representation of data as its primary source and a human representation of data as its primary source and a human as its primary target.as its primary target.
•• ComputerComputer--generated visualizations meant to be viewed by a generated visualizations meant to be viewed by a human includes various flavors of visualization:human includes various flavors of visualization:
•• Data VisualizationData Visualization•• Scientific VisualizationScientific Visualization•• Information VisualizationInformation Visualization•• Software VisualizationSoftware Visualization
Definitions
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Definitions
•• Mapping from computer representations to perceptual Mapping from computer representations to perceptual (visual) representations, choosing encoding techniques to (visual) representations, choosing encoding techniques to maximize human understanding and communicationmaximize human understanding and communication
Mapping Process
DomainNumbers / Data
DomainPicture(s)
RealityComputer
representationof reality
Picture(s) Viewers
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Visualization and adjacent Disciplines
•• Computer GraphicsComputer Graphics– Efficiency of algorithms (CG) versus effectiveness of
use (V)
•• Computer VisionComputer Vision– Mapping from pictures to abstract description (CV)
versus mapping from abstract description to picture domain (V)
•• Image ProcessingImage Processing– Mapping from data domain to data domain (IP) versus
mapping from data domain to picture domain (V)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Visualization and adjacent Disciplines
•• (Visual) Perception (Visual) Perception – General and scientific explanation of human abilities
and limitations (VP) versus goal oriented use of visual perception in complex information representation.
•• Art and DesignArt and Design– Aesthetics and style (AD) versus expressiveness and
effectiveness of data (V)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
A Model of Perceptual Processing
A three-stage model of human visual information processing
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Goals in Visualization
•• Exploration / exploitation of data and informationExploration / exploitation of data and information•• Enhancing understanding of concepts and Enhancing understanding of concepts and
processesprocesses•• Gaining new (unexpected, profound) insightsGaining new (unexpected, profound) insights•• Making invisible visibleMaking invisible visible•• Effective presentation of significant featuresEffective presentation of significant features•• Quality control of simulations, measurementsQuality control of simulations, measurements•• Increasing scientific productivityIncreasing scientific productivity•• Medium of communication / collaborationMedium of communication / collaboration
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Sample Applications
•• GeophysicsGeophysics, e.g. , e.g. ““The Visualization of StormThe Visualization of Storm””•• BiochemistryBiochemistry, e.g. the visualization of DNA, molecules, or , e.g. the visualization of DNA, molecules, or
crystalscrystals•• Engineering and PhysicsEngineering and Physics, e.g. the visualization of a helicopter , e.g. the visualization of a helicopter
turbine, of a wind tunnel, of the Big Bang, of Finite Elements turbine, of a wind tunnel, of the Big Bang, of Finite Elements Analysis computationsAnalysis computations
•• Sociology and PoliticsSociology and Politics, e.g. the visualization of census data, of , e.g. the visualization of census data, of vote distributions or the spread of aidsvote distributions or the spread of aids
•• MathematicsMathematics, e.g. the visualization of , e.g. the visualization of splinessplines•• Information TechnologyInformation Technology, e.g. the visualization of the web, the , e.g. the visualization of the web, the
visualization of retrieved documents from queryvisualization of retrieved documents from query
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Visualization
This graphic is an adaptation of M. Charles Joseph This graphic is an adaptation of M. Charles Joseph MinardMinard’’ss ““March of the Napoleon March of the Napoleon ArmyArmy”” by Sunny McClendon, as part of an Information Design Class at tby Sunny McClendon, as part of an Information Design Class at the University he University of Texas at Austinof Texas at Austin
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Visualization
““Study of a Numerically Modeled Severe StormStudy of a Numerically Modeled Severe Storm””, Video by , Video by WilhelmsonWilhelmson, Robert et al. , Robert et al. (Department of Atmospheric Studies and NCSA)(Department of Atmospheric Studies and NCSA)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Visualization
Simulation of Shuttle debris trajectory. A 1.67 pound slab of inSimulation of Shuttle debris trajectory. A 1.67 pound slab of insulating foam is seen sulating foam is seen falling off the external tank after falling off the external tank after Columbia'sColumbia's launch and hitting the left wing. The launch and hitting the left wing. The Columbia Accident Investigation Board (CAIB) has identified a deColumbia Accident Investigation Board (CAIB) has identified a debris event like this bris event like this as the most likely cause of the as the most likely cause of the ColumbiaColumbia disaster. This image was used in the disaster. This image was used in the CAIB'sCAIB'sfinal report. (NASA Advanced Supercomputing Division)final report. (NASA Advanced Supercomputing Division)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Visualization
External Truck Aerodynamics Animation. Models such as this will lead to modifications that increase fuel efficiency by minimizing drag force. Reducing drag force will also lessen environmental pollution and improve stability and vehicle control. (Old Dominion University)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Visualization
Simulation and Animation Technology for Addressing Environmental Homeland Security Concerns (HidroGeoLogic, Inc.)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Impact of Future Technology
•• Next generation PCsNext generation PCs•• Next generation storage systemsNext generation storage systems•• Next generation display technologiesNext generation display technologies•• Next generation communication systemsNext generation communication systems•• Next generation analytic toolsNext generation analytic tools•• Limitation of human capacityLimitation of human capacity•• Improved understanding of psychological and Improved understanding of psychological and
perceptual issuesperceptual issues
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
•• The data := The data := ““data generated from mathematical data generated from mathematical models or computations and from human and models or computations and from human and machine collectionmachine collection””
Data and the world being modeledData and the world being modeled– Establish valid and reliable relationship between data
and world– Scientific objectives and method
•• Attempt to explain the real worldAttempt to explain the real world•• UnderstandingUnderstanding•• PredictionPrediction•• Create a model of the worldCreate a model of the world•• Acquire data to verify or refine the modelAcquire data to verify or refine the model
Definition and Goals
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
•• Data model = conceptual view of dataData model = conceptual view of dataCharacterize data by e.g.Characterize data by e.g.
–– GeometryGeometry–– TopologyTopology–– ValueValue
•• Advantage of data modelsAdvantage of data models– Discipline independent view of data– Choose expressive visualization technique– Avoid “mental road blocks”
Definition and Goals
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
Overview of selected data characteristics
•• Nominal, ordinal, quantitativeNominal, ordinal, quantitative•• Point, scalar, vectorPoint, scalar, vector•• ““continuouscontinuous”” datadata•• Topology / structure for nonTopology / structure for non--continouscontinous
datadata•• Data reliabilityData reliability•• Valid range of dataValid range of data•• Time descriptorsTime descriptors
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
Nominal, ordinal, quantitative
•• Nominal data Nominal data –– members of certain class, e.g. members of certain class, e.g. [Riga, [Riga, TalsiTalsi, , VentspilsVentspils, , LiepajaLiepaja], or [Maple, Birch, ], or [Maple, Birch, Oak]Oak]– Effective visual attributes: color (hue), symbol
•• Ordinal data Ordinal data –– related by order, e.g. [low, medium, related by order, e.g. [low, medium, high], or [tiny, small, medium, large]high], or [tiny, small, medium, large]– Effective visual attributes: brightness, size, (color – hue, if
meaningful to observer)
•• Quantitative data Quantitative data –– carry precise numerical value, carry precise numerical value, e.g. [2.3, 4.56, 0.8, 2.5Ee.g. [2.3, 4.56, 0.8, 2.5E--35]35]– Effective visual attributes: position, length
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Point, Scalar, Vector
The Data
Syntactical categories, additionally characterized by dimensionsSyntactical categories, additionally characterized by dimensions
•• PointPoint– Each data element is considered a position in n-dimensional
space– Example: measurements of leaves: [length, width, tree type,
age], e.g. [2.3, 1.2, B, 1], [4.3, 2.2, B, 3], [1.5, 1.5, M, 1], [3.0, 2.9, M, 3], …
– Expressive visualizations: scatter plots, glyphs
•• ScalarsScalars– Each data element has a numeric expression– Example: topography of terrain, expressed as 2-d field
containing elevations
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
Point, Scalar, Vector
•• Scalar arrays Scalar arrays –– often often ““discrete samples of discrete samples of continuous functionscontinuous functions””– Usually 1 (linear), 2 (image), or 3 (volumetric) dimensional data
sets; samples in equidistant or non-equidistant steps– Expressive visualizations: line graph, shaded surface, volume
viewing
•• VectorsVectors– Each data element is considered a straight directed line with a
certain length (magnitude) in n-dimensional space– Examples: direction of particle flow in channel– Expressive visualizations: arrows, stream lines, particle tracks
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
“Continuous” Data
““ContinuousContinuous”” data can be represented by (samples of)data can be represented by (samples of)function: function: yiyi = = fifi (X), where X = (x1 , x2 , x3 , ..., (X), where X = (x1 , x2 , x3 , ..., xnxn ); i=[1,...,m]); i=[1,...,m]
x .... independent variables; x .... independent variables; e.ge.g space, time, spectral (space, time, spectral (““dimensionsdimensions””))y .... dependent variables (y .... dependent variables (““parametersparameters””))
Comes in regular and irregular formatsComes in regular and irregular formats
Expressive visualizations of functions: Expressive visualizations of functions: similar to scalar, similar to scalar, quantitative, ordinalquantitative, ordinal
Interpolation methods: Interpolation methods: must be meaningful in problem spacemust be meaningful in problem space
Computation time Computation time for visualization techniques faster on regular gridsfor visualization techniques faster on regular grids
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Topology / structure of non-continous data
The Data
• Types of topology/structure, e.g.– Sequential (text)– Hierarchical– Relational– Single points and connectors
• Examples and corresponding expressive visualizations– Molecules (e.g. ball-and-stick model)– Data bases (cone tree; perspective wall)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
Other data characteristics in a data model
• Data reliability– Missing data or unreliable data
• expressive visualizations: error bars; indicate borders between• real/missing data• careful with interpolation
• Valid range of data– min / max / mean / median
• Time descriptors– Various meanings of time: simulation time, simulated/actual
time frame, computation time, recording and playback time, user's time frame
– “time models” to support time conversions necessary to synchronize
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
The Data
Examples
Perspective Wall and Cone Tree: from CACM April 1993, InformationVisualizer by Robertson, Stuart and Mackinlay.
• Complex data sets and their visual counterparts, e.g- scientific visualization - proteins- software - web pages
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Mapping
Visual Data Mapping
•• Need for systematic strategies (concepts, methodologies, Need for systematic strategies (concepts, methodologies, intelligent visualization systems) to exploit dataintelligent visualization systems) to exploit data
Hardware / Software User: abilities,disabilities, desires
+Visualization Goal
Numbers / Data
Graphical objects+
Visual attributesPicture(s)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Histograms (1-d and 2-d), Pie and Bar Charts
• Representative data characteristics– 1-d arrays of scalars, continuous or discrete data values
•• TechniquesTechniques– Bar chart: length of bar indicates value of (class of) items– 1-d histogram: length of bar indicates number of elements in
sub-category– 2-d histogram: brightness/color indicates number of elements
in sub-category– Pie chart: sector of circle indicates values of (class of) items
as fractions of a whole
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Histograms, Pie and Bar Charts
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Line Graphs
• Representative data characteristics– 1-d, (continuous), scalar data arrays, e.g. y = f(x)
• Technique– Curve drawn through single data points
• Special note on effectiveness– No mental interpolation necessary– Use interpolation method meaningful to problem space
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Line Graphs
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
(n-dimensional) Scatter Plots
• Data characteristics: multivariate data space, such as botanical observationsTechnique– Define coordinate system appropriate for data– Project data and coordinate system to display space– Use points or symbols to define data element locations
• Effectiveness– Position is primary visual cue– Animation (change of view point) for 3-d effect– Dimensions > 2: use projections (“Grand Tour”)
Interaction: control over view point, rotation, “rocking”; “conditional box”
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Example of 4-d Scatter Plot
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Glyphs / Icons
• Representative data characteristics– Multivariate data spaces, such as computer performance
measurements, census data
• Technique– Define 1, 2, or 3 data variables as spatial dimensions– Compose small graph (glyph/icon) for each additional variable– Display each glyph as “complex pixel” in 1,2,or 3d space
• Special note on effectiveness– Distinguish between macroscopic/microscopic interpretation
of glyphs– Several visual attributes used in each glyph– “The whole is greater than the sum of its parts” (Gestalt
Theory)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Chernoff faces. Different data variables are mapped to the sizes and shapes of different facial features.
Representation
Example of Glyphs
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Contour Lines (Isolines)
• Representative data characteristics– 2-d scalar data arrays, e.g. z = f(x,y), such as elevation map
• Technique– Trace lines of constant value (= threshold value) of 2-d raster
• Special note on effectiveness– Annotate selected isolines
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Contour Plots
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Contour Plots
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Surface View
Representation
• Representative data characteristics– 2-d scalar data arrays, e.g. z = f(x,y), such as elevation map
• Wireframe technique– Treat “z” as elevation over 2-d terrain and use projection from
3-d to 2-d– Project mesh of lines parallel to x and y axes
• Shaded surface technique– Treat “z” as elevation over 2-d terrain and use projection from
3-d to 2-d– Project each data element / remove hidden surfaces– Assign grey value / color value
• Special note on effectiveness– Source of grey value/color value must be transparent to viewer
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Example of Surface Plot
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Image Display
Representation
• Representative data characteristics– 2-d scalar data arrays, e.g. z = f(x,y), such as LANDSAT image
• Technique– Straightforward: map each 2-d data element to brightness or
color of screen pixel
• Special note on effectiveness– Color / brightness scale necessary
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Color Transformations
• Representative data characteristics– up to three scalar data arrays defined over same two
dimensions, e.g. zi = f(x,y), i=1,2,3 such as three TM (Thematic Mapper) channels of same terrain
• Technique– choose same technique (e.g. image display or surface) for
each data array– read zk = f(i,j), k=1,2,3 for each pixel location on screen,
resulting in 3 brightness values (z1, z2, z3)– use (z1, z2, z3) as coordinates to color space, e.g. RGB, HSV
\xdf ‘color’– use ‘color’ to paint pixel at screen location (i, j)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Color Transformations
Digital Elevation Map of Oetztal, Austria: hue is elevation; intensity is illumination
RGB transformation of three IRAS imagesData by NASA/JPL
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Volume Slices
• Representative data characteristics– 3-d scalar data arrays, e.g. w = f(x,y,z), such as medical scans
of human organs
• Technique– intersect 2-d plane(s) with volume– use image display for visual representation– project planes to screen
• Special note on effectiveness– use appropriate coordinate system to depict location of
plane(s) in volume– animation (change of view point), hidden surfaces and
perspective geometry for 3-d effect
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Examples of Volume Slices
Specimen from Visible Human Male –Head subset
Specimen from Visible Human Male –Thorax subset
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Surface Rendering
• Representative data characteristics– 3-d scalar data arrays, e.g. samples of w = f(x,y,z), where w
(voxel value) might indicate color, opacity, density, material, or time
• Technique– surface reconstruction (define surfaces in 3-d raster) (e.g. by
using marching cubes algorithm or surface detection)– surface rendering (illumination, shading, projection)
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Volume Viewing
• Representative data characteristics– 3-d scalar data arrays
• • Technique– Project volumetric data elements onto the display space, by
either– Backward projection (object-order): scan voxel space and
project to screen– Forward projection (image-order): scan screen pixels and
determine voxel contributions– Combination– Assign pixel brightness/color
• Special note on effectiveness– Transparency / translucency
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Example of Volume Rendering
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Example of Direct Volume Rendering
Materials Science: microstructures
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Representation
Arrows
• Representative data characteristics– Vector fields
• Technique– Use arrow as glyph, vary following attributes of arrow
depending on variables: direction/length/width/reflection properties of shaft, type/color of arrow head
• Special note on effectiveness– Avoid cluttering by reducing amount of data to display– Additional problems in 3-d through directional ambiguity
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Examples of Vector Plots
Representation
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Interaction Issues
General Considerations
• Interaction := user’s actions during the visualization process
• Various forms of interaction– change of visuals: color, view point, shading techniques– change of visualization techniques– change of parameters for data generation (interactive steering)– exploration of data: explore data values; remove occluding
data
IntroductionIntroduction to to VisualizationVisualization
©© Arnis Lektauers, 2005Arnis Lektauers, 2005
Interaction Issues
Human Performance Limitations
• users prefer shorter response times• feedback to user in less than 0.1 sec for continuous user input• response times > 15 sec are disruptive• shorter response times leads to shorter user think time• faster pace may increase productivity, but may increase error
rates• response time should be appropriate to task (changes from
complex task to atomic actions)• empirical tests can help to set suitable response times