Visualization Background and Theory

Transcription

Visualization Theory
MiriamButt&Dominik Sacha
LingVis:VisualAnalytics forLinguistics
DGfS2016|24.-26.2.2016
For More Detailed Information
• IDVbook
• CoversallimportantInfoVis aspects
• Goodstartingpoint
• AlsousedinourInfoVis lectures
2
Contents
• Introduction
• DataFoundations
• KeyAspectsofVisualization
• DesigningVisualizations
• InteractiveInformationVisualization
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4
Introduction
Introduction – Goals for Visualization
• Presentation(Communication)
• Startingpoint:factstobepresentedarefixedapriori
• Process:choiceofappropriatepresentationtechniques
• Result:high-qualityvisualizationofthedatatopresentfacts
• ConfirmatoryAnalysis
• Startingpoint:hypothesesaboutthedata
• Process:goal-orientedexaminationofthehypotheses
• Result:visualizationofdatatoconfirmorrejectthehypotheses
• ExploratoryAnalysis
• Startingpoint:nohypothesesaboutthedata
• Process:interactive,usuallyundirectedsearchforstructures,trends
• Result:visualizationofdatatoleadtohypothesesaboutthedata
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6
Introduction – Steps for Visualizing Data
DataType
Questions
Requirements
Preprocessing
Design
Pro/Cons
Find outwhatkind ofdatayouhave.
Which questions shallbeansweredbythedata?
Deriverequirementsfromthesetasks.
Preprocessdata(missing data,outliers, transformations,…).
Consider theprosandconsofdifferentvisualization
techniques (effectiveness, familiarity…)
Designavisualization system.
Introduction – Steps for Visualizing Data - Pipeline
“ReferenceModel forInformation Visualization”
S.Card,J.Mackinlay,andB.Shneiderman. 1999.ReadingsinInformationVisualization:UsingVision toThink.Chapter1.
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8
Data Foundations
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Data Foundations – Data Types
Ordinal
Nominal
•
•
Noquantitativerelationship
betweencategories
Classificationwithout
ordering
•
•
Attributescanberankordered
Distancesbetweenvalues
donothaveanymeaning
à Nointrinsicorder
à Orderedinasequence
e.g.sports, nationality
e.g.placesorderedbynoise
References:C.Ware.2012.InformationVisualization:PerceptionforDesign.Chapter1.
Numeric
•
•
•
Attributescanberankordered
Distancesbetweenvalues
haveameaning
Mathematicaloperationsare
possible
e.g.heightof aperson
Data Preprocessing – Real World Data is Dirty, What’s Wrong?
UniqueID
Gender
Age
Smoking
Habits
Time
323
0
21
0
1h 40s
435
1
34
2
1h 55s
123
0
3
90min
352
1
25
0
1h 43s
674
1
25
1
0,4day
865
0
18
3
1h 50s
341
X
41
2
1h 33s
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Data Foundations – Preprocessing
• DataCleaning
• MissingValues
• Noise…
• Normalization
• Commonscale(0-1)
• E.g.,colormapping
• Segmentation
• Dividedatainmeaningfulparts
• DataReduction
• Sampling
• Dim.Reduction
Ø Bigpartofourdailywork!
Ø Methodshavetobeappliedcarefully
Ø Analystsneedstoknowwhichtechniqueshavebeenapplied!(LossofInformation)
Data Foundations – Linear vs. Logarithmic Normalization
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Data Foundations – Logarithmic Normalization
AdvantagesandDisadvantages?
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Data Foundations – Practically Speaking
• Thinkcarefullyaboutappropriatepreprocessingmethods
• Considerdatatype,class,tasksanddataquality
• “Tailordatatotheanalysisgoals”
• Keeptrackandcommunicatewhichpreprocessings havebeenapplied
andconsidertheirimpactsonthefinaldataproduct
• Poorlydesignedpreprocessings maycreateartificialpatternsinthe
dataleadingtowrongconclusionsoranalysisresults
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Key Aspects of Visualization
Visualization Basics
• Perceptual/Pre-AttentiveProcessing
• Certainlowlevelvisualaspectsarerecognizedbeforeconscious awareness
• GestaltLaws
• Thetendency toperceive elementsasbelongingtoagroup,basedoncertain
visualproperties
• VisualVariables
• Thedifferentvisualaspectsthatcanbeusedtoencode information
Reference:Slidesprovided byChrisCuly (http://ling.uni-konstanz.de/pages/home/butt/main/material/esslli14-vis/CuC_slides/reveal-based/theory.html#/title)
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Preattentive Processing
• Perception of visual features managed by the low-levelvisual
machinery
• Extremely fast:<200msec (the eyes take >200msec to initiate
movement)=>processed inparallel
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Preattentive Processing (color)
http://www.idvbook.com/
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Preattentive Processing (shape)
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Serial Processing
• Finding the red circle requires serial processing
• Thered circle is aconjuctive target (composed of non-unique features)
• Demo:http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Gestalt Laws
Perceptual laws about how we group visual objects together to formvisual entities.
1.
Lawof Proximity
2.
Lawof Similarity
3.
Lawof Connectedness
4.
Lawof Continuity
5.
Lawof Symmetry
6.
Lawof Closure and CommonRegion
7.
Lawof Figure and Ground
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Gestalt Laws - Proximity
Closeobjectsareperceptuallygrouped.
ColinWare,InformationVisualization:Perception for Design,MorganKaufmann
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Gestalt Laws - Similarity
Similarobjectsareperceptuallygrouped.
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Gestalt Laws - Connectedness
•
•
•
Connectedobjectsareperceptually
grouped.
Canbemorepowerful thanproximity,
color, sizeorshape.
Usedinnode–link diagrams
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Gestalt Laws - Continuity
Itiseasiertoconstructvisualobjects
outofvisualelementsifthe
connection issmooth andcontinuous.
Lineswithabruptchangesindirection
arehardertoread.
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Gestalt Laws - Symmetry
•
•
powerful organization principle
symmetricformsbuild avisual
whole
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Gestalt Laws – Closure and Common Region
Wepreferperceptualsolutions
withclosedcontours.
Weseearing behind a
rectangleratherthanabroken
ring.
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Gestalt Laws – Closure and Common Region
Eulerdiagramtousing color
andtexturetohighlight
contours.
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Gestalt Laws – Figure and Ground
•
•
•
•
figure:objectlike, perceivedtobein
theforeground.
ground:whateverliesbehind the
figure.
fundamental toidentify objects.
allGestaltlawscontribute tocreatea
figure
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Some more examples you might know
Closure
https://de.wikipedia.org/wiki/WWF#/media/Fil e:WWF
_Logo.svg
Figure Ground
https://en.wikipedia.org/wiki/Finder_%28software%29#/medi
a/File:Mac_Finder_icon_%28OS_X_Yosemite%29.png
Proximity,Closure
https://de.wikipedia.org/wiki/IBM#/media/File:IBM_logo.svg
Marks and Visual Variables
JacquesBertin:
• Amark is"somethingthatisvisibleandcanbeused...toshow
relationshipswithinsetsofdata"
• Visualvariables are"thedifferentwaysthatamarkcanbevaried"
Reference:
S.Carpendale.2003.Considering VisualVariablesasaBasisforVisualization.Researchreport2001-693-16,
DepartmentofComputer Science,UniversityofCalgary.
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The Eight Visual Variables
1.
2.
3.
4.
5.
6.
7.
8.
Position
Mark/Shape
Size(Length,AreaandVolume)
Brightness
Color
Orientation
Texture
Motion
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The Eight Visual Variables - Position
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The Eight Visual Variables – Shape/Mark
Severalexamplesofdifferentmarksorglyphsthatcanbeused.
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The Eight Visual Variables – Shape/Mark
Thisvisualizationusesshapestodistinguish betweendifferent cartypesinaplot comparing highwayMPG
andhorsepower.Clustersareclearlyvisible, aswellassomeoutliers.
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The Eight Visual Variables – Size (Length, Area and Volume)
Examplesizestoencodedata.
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The Eight Visual Variables – Size (Length, Area and Volume)
Thisisavisualizationofthe1993carmodelsdataset,showingenginesizeversusfueltankcapacity.
Sizeismappedtomaximumpricecharged.
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The Eight Visual Variables – Brightness
Brightnessscaleformappingvaluestothedisplay.
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The Eight Visual Variables – Brightness
Anothervisualizationofthe1993carmodelsdataset,thistimeillustratingtheuseofbrightnessto
conveycarwidth(thedarkerthepoints,thewiderthevehicle).
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The Eight Visual Variables – Color
standardlineargrayscale
rainbow
heated
bluetocyan
bluetoyellow
Avisualizationofthe1993carmodels,showingtheuseofcolortodisplaythecar’slength.Here
lengthisalsoassociatedwiththey-axisandisplottedagainstwheelbase.Inthisfigure,blue
indicatesashorterlength,whileyellowindicatesalongerlength.
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saturation
hue
Microsofthue/saturationcolor
selector.
The Eight Visual Variables – Orientation
Exampleorientationsofarepresentationgraphic,wherethelowestvaluemapstothemarkpointingupward
andincreasingvaluesrotatethemarkinaclockwiserotation.
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The Eight Visual Variables – Orientation
Samplevisualizationofthe1993carmodelsdatasetdepictingusinghighwaymilesper-gallon
versusfueltankcapacity(position)withtheadditionaldatavariable,midrangeprice,usedtoadjust
markorientation.
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The Eight Visual Variables – Texture
Sixpossible exampletexturesthatcouldbeusedtoidentifydifferent datavalues.
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The Eight Visual Variables – Texture
Examplevisualizationusingtexturetoprovideadditionalinformationaboutthe1993carmodels
dataset,showingtherelationshipbetweenwheelbaseversushorsepower(position)asrelatedto
cartypes,depictedbydifferenttextures.
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The Eight Visual Variables – Motion
• Canbeassociatedwithanyoftheothervisualvariables
Example:Gapminder
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Designing Visualizations
Characteristics of Visual Variables
• Selective:IsXdifferentfromtheothers?
• Associative:IsXliketheothers?
• Order:IsXmore/greater/bigger/…thanY?
• Quantitative:HowmuchisthedifferencebetweenXandY?
• Length:Howmanydifferentcategoriescanwerepresentwiththis
variableforatask?
Reference:Summaryof:
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Characteristics of Visual Variables
Reference:Summaryof:
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Using Visual Variables - Practically Speaking
• Don'tuseorderedvariablesfornominaldata
• Usepositionforthemostimportantinformation
• Manylengthsare~5— alimitingfactor
• Forquantity,use(vertical)position,length,size,brightness,
saturation
• Formanydistinctions:usesize,shapeorbrightness
• Redundantencodingmay makefeatureseasiertointerpret
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Color– Some Notes
Colorispowerful,buttherearespecialconsiderations
• Hueisnominal,butcanbegiven(cultural)orderings(e.g.
temperature,elevation)
• Affectiveconnotationsofcolorvaryacrosscultures
• Dependingonwhoyourusersare,youmayneedtodesignfor
colorblindnessorlow-vision
Allowinguserstosetcolors/colorschemesisthemostflexible.
Resource:
SimDaltonism:colorblindness simulator forOSX (Thereothertools, andforother platforms)
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Color - Some Guidelines
• Uselighter,lesssaturatedcolorsforlargerareas
• Usedarker,moresaturatedcolorsforsmallerareasandlines
• Forordinaldata,usedarkerandmoresaturatedforhighervalues(e.g.
heatmap)
• Color(hue)lengthis~6-10,sowithmorecategories,colormaynotbe
anappropriatevisualvariable.
• Saturationhasalengthof~3
• Ensurealuminancecontrastof3:1fortext.
Resource:
ColorBrewer
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Heatmap Example
Exampledatashowsconcurrent usersessions overtime, takenfrom adevelopment environment.
http://bl.ocks.org/tjdecke/5558084
ColorBrewer
ColorBrewer
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http://colorbrewer2.org/
More Advanced - ColorCat
Colormaps forCombined AnalysisTasks.
https://github.com/SebastianMittelstaedt/ColorCAT
S.Mittelstädt,D.Jäckle,F.StoffelandD.A.Keim.ColorCAT: GuidedDesignofColormaps forCombined Analysis Tasks.Eurographics Conferenceon
Visualization(EuroVis) - ShortPapers,TheEurographics Association, DOI:10.2312/eurovisshort.20151135, 2015.
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• Visualizationshelp:
•
•
•
•
Todescribesomestructure,patternsoranomalyinthedata.
Toexploreandanalyzelargedatasets.
Tomakeeffectiveuseoftheinformationoverflow.
Tocommunicateinformationtopeople.
• Visualizationcandistortthe“truth”!
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• Expressiveness:Visualization presents allthe information and only the
information.
• Effectiveness:Visualization is effective when it can be interpreted
accurately and quickly and when it can be rendered inacost-effective
manner.
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How to Visualize Badly
Pooruseofabarchart.
Betteruseofascatterplot.
http://www.idvbook.com
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Interaction
Interactive Analysis Process with Visualizations
• Shneiderman's "InformationSeekingMantra"
“Overviewfirst.ZoomandFilter.DetailsonDemand”
• Keim's VisualAnalyticsMantra
“Analyzefirst,showtheimportant,zoom,filter
andanalyzefurther,detailsondemand”
References:
B.Shneiderman. 1996.Theeyeshaveit:Ataskbydatatypetaxonomyforinformation visualizations.Proceedings ofthe1996IEEESympoium onVisual
Languages,VL'96.
D.A.Keim,F.Mansmann, J.Schneidewind, andH.Ziegler.2006.Challenges invisualdataanalysis.Proceedings oftheconferenceonInformation
Visualization, IV'06.
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Interaction Types
• Select:marksomethingasinteresting
• Explore:showmesomethingelse
• Reconfigure:showmeadifferentarrangement[sametype]
• Encode:showmeadifferentrepresentation[differenttype]
• Abstract/Elaborate:showmelessormoredetail
• Filter:showmesomethingconditionally
• Connect:Showmerelateditems
• Someotherthings:history,annotate,extract
References:
J.S.Yi,Y.A.Kange,J.T.Sasko,J.A.Jacko.2007.Towardadeeperunderstandingoftheroleofinteractionininformationvisualization.IEEETransactionsonVisualizationandComputer
Graphics.13:6
B.Shneiderman.1996.Theeyeshaveit:Ataskby datatypetaxonomyforinformationvisualizations.Proceedingsofthe1996IEEESympoium on VisualLanguages,VL'96.
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Interaction – Select
Example
https://www.google.de/maps/
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Interaction - Explore
Example
https://www.google.de/maps/
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Interaction - Reconfigure
https://bost.ocks.org/mike/miserables/
http://mbostock.github.io/d3/talk/20111116/bundle.html
Example
Example
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Interaction - Encode
http://www.infocaptor.com/
Example
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Interaction – Abstract/Elaborate
http://mbostock.github.io/d3/talk/20111018/tree.html
Example
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Interaction – Filter
http://shiny.rstudio.com/gallery/movie-explorer.html
Example
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Interaction – Connect
http://mbostock.github.io/d3/talk/20111116/iris-splom.html
Example
Example
http://mbostock.github.io/d3/talk/20111116/bundle.html
69
Many Design Alternatives! – Checkout Existing
Visualizations
• http://textvis.lnu.se/
• https://github.com/mbostock/d
3/wiki/Gallery
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THANK YOU!
Questions?
Computer
Formoreinformation
aboutour work
pleasecontact
DominikSacha
Tel.+49753188-4046
[email protected]
http://vis.uni-konstanz.de/
Human
Visualization
Action
Hypothesis
Data
Knowledge
Model
Finding
Exploration
Loop
Insight
Verification
Loop
Knowledge
Generation
Loop

Visualization Background and Theory

Transcription

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