InformationVisualizationinHCI

SWEN-444

Definitions

•  Visualize:–  Toformamentalmodelormentalimageofsomething

–  Tomakesomethingvisibletothemindorimagination

•  Visualization:–  Humanactivity,notpersewithcomputers–  Visual,Auditoryorothersensorymodalities–  Creationofvisualimagesinaidofunderstandingofcomplex,datarich,representationsofdata

InformationVisualization

•  Pre-attentiveprocessing– Unconsciousaccumulationofinformationfromtheenvironment

–  Informationthat“standsout”isselectedforattentive(conscious)processing

– Whydoessomeinformation“standout”?•  Notexactlysure!•  Butithassomethingtodowiththestimulusitself,andtheperson'scurrentintentionsorgoals

Weber'slaw

•  “justnoticeabledifference”

•  I–originalintensityofthestimulus•  ChangeinIistheminimumdifferencerequiredforittobeperceived(jnd)

•  Kconstant

ΔII= k

WhatisInformationVisualization?

•  Informationvisualization:“theuseofinteractivevisualrepresentationsofabstractdatatoamplifycognition”(Ware,2008)

•  Abstractdataincludebothnumericalandnon-numericaldata–  Stockprices,socialrelationships,patientrecords

•  Typicalconcerns:discoveryofpatterns,trends,clusters,outliersandgapsindata

•  Designgoal:bemorethanaestheticallypleasing,showmeasurableusabilitybenefitsacrossdifferentplatformsandusers

InformationVisualization

•  Data,dimensionalityofthedata•  Presentationofthedata•  Processingofthedata•  Interactionwiththedata•  Dynamicalviewupdating

InformationVisualizationFlow

HCI:disasterstory

•  1988:•  IranAirFlight655shotdownbyUSSVincennes•  F-14??-290casualties•  Conclusion:‘Aegishadprovidedaccuratedata.Thecrew

hadmisinterpretedit.’•  Differentradarscreensdisplayeddifferentaspectsof

airplane•  Correlatinginformationwasdifficult•  Vitaldataclutteredbytrivialdata

Data Type by Task Taxonomy

Data Type by Task Taxonomy: 1D Linear Data

•  Itemswhichcanbeorganizedsequentiallye.g.textdocument,listofnames

•  Designissues:–  Colors,sizes,layout–  Scrolling,selectionmethods

•  Exampleusertasks:checkwhichitemshavesomerequiredattribute

Data Type by Task Taxonomy: 2D Map Data

•  Itemsmakeupsomepartofthe2Darea–  Notnecessarilyrectangular,e.g.LakeonGoogleMap–  e.g.Geographicmap,floorplans

•  Exampleusertasks:findingitems,findingpathsbetweenitems

Data Type by Task Taxonomy: 3D World Data

•  Itemswithcomplexrelationshipswithotheritems–  e.g.Volume,temperature,density

–  e.g.Medicalimaging,architecturaldrawing,scientificsimulations

•  Designissues:position,orientationandnavigationforviewing3Dapplication

•  Exampleusertasks:temperature,density

Data Type by Task Taxonomy: Multidimensional Data

•  Itemswithnattributesinn-dimensionalspace

•  Relationaldatabasecontentscanbetreatedthisway

•  Interfacemayallowusertoview2dimensionsatatime

Data Type by Task Taxonomy: Temporal Data

•  Verycloseideato1Dsequentialdata,butwarrantadistinctdatatypeinthetaxonomyastemporaldataissocommon–  e.g.Stockmarketdata,

weather•  Itemshaveabeginningand

endtime,mayoverlapintime

•  Exampleusertasks:findingeventsduringatimeperiod,searchingforperiodicalbehavior

Data Type by Task Taxonomy: Temporal Data (cont.)

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Data Type by Task Taxonomy: Tree Data

•  Non-rootitemshavealinktoaparentitemItems,linkscanhavemultipleattributese.g.Windowsfileexplorer

•  Exampleusertasks:howmanyitemsarechildrenofanode,howdeeporshallowisthegraph

Data Type by Task Taxonomy: Tree Data (cont.)

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Data Type by Task Taxonomy: Network Data

•  Itemslinkedtoarbitrarynumberofotheritems

•  Exampleusertask:shortestpath,leastcostlypath

•  Howtovisualize,layoutthenetwork?

The seven basic tasks 1.   Overview:userscangainanoverviewoftheentire

collection2.   Zoom:userscanzoominonitemsofinterest3.   Filter:userscanfilteroutuninterestingitems4.   Details-on-demand:userscanselectanitemor

grouptogetdetails5.   Relate:userscanrelateitemsorgroupswithinthe

collection6.   History:userscankeepahistoryofactionsto

supportundo,replay,andprogressiverefinement7.   Extract:allowuserto“save”,publish,examine

extracteditems

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Challenges for Information Visualization

•  Importingandcleaningdata•  Combiningvisualrepresentationswithtextuallabels:

Howtoputontextlabels(e.g.onamap)withoutcoveringwhatyouwishtodisplay?

•  Findingrelatedinformation:Properjudgmentoftenrequireslookingatdataderivedfrommultiplesources

•  Viewinglargevolumesofdata•  Integratingdatamining•  Integratingwithanalyticalreasoningtechniques:Use

datatosupportordisclaimhypotheses•  Collaboratingwithothers•  Achievinguniversalusability:Text,tactileorsonic

representations?•  Evaluation

Challenges for Information Visualization

•  Goalistoseparatethe“signal(information)fromthenoise(data)”

•  Toomuchversustoolittleinformation•  Visualizationspasstheeyeballtest•  Minimalism–emphasizethedataratherthanthescaffolding–  Avoidunnecessaryandbusygraphics–  Readablesize,legible–  Appropriateuseofcolor–  Appropriatescaling,alignment,symmetry

Exercise:ARecordYearforAutoRecalls

Indiscussiongroupspleaseanswerthefollowingquestions:•  Whatisthedatashowninthisvisualization?•  Whatquestionsdoesthisvisualizationanswer?•  Whatdoyouthinkabouttheuseofanimation?•  Isthevisualizationeasytounderstand?•  Canyoureadthedatafromthevisualization?•  Whatisthevisualizationdatatype?Whattaskscanbe

performed?•  Whydoyoulike/dislikethisvisualization?•  Canyousuggestanyimprovements?Howwouldyou

redesignit?

NY Times: http://bit.ly/auto-recall

References

•  Folk,C.L.,&Remington,R.Top-downmodulationofpreattentiveprocessing:Testingtherecoveryaccountofcontingentcapture.VisualCognition,14,445-465.

•  Ware,Clin,VisualThinkingforDesign,MorganKaufmann,SanFrancisco,CA(2008).

•  http://www.cs.umd.edu/hcil/trs/96-13/96-13.html

•  Cuffe,Kirkham,Dent,andWilson,DataVisualization:Thesignalandthenoise,IEEEPotentialsJuly/August2018