Extracting patterns from large movement data setsusingHybridSpatio- temporal Filtering: A case study of geovisual analytics in support of
fisheries enforcement activities
by
©Rene A. Enguehard
A Thes issubmitted tothe Schoo lof GraduateStudies in parti al fulfillme ntoftherequir em ent sfo r the degree of
Master of Science
Department of Geography Memori al UniversityofNewfound land
St. Jo hn's
November 2011 Newfoundland
Abstract
The ubiquitousnature of locationtracking technologies hasresultedinanincrease in movementdatabeing collected. Thesedata are usedin many contexts,suchas understanding animal migration,aidingin fisheries enforcement,or managingfleets of taxicabs.Such large volumesofdata call formore efficient data visualizationandanalysis methods.Thisresearchprovides a generalapproach tothe analysisofmovementdata, namedHybrid Spatio-temporalFiltering(HSF),whichallowsanalysts tofilterdata based oncharacteristicsofmovementwithinageovisuala nalyticse nvironment.F iltering signaturesaredefinedby combining movementpath complexity(fractal dimension) and velocity,to extract behaviouralpatternsfromdata sets.Anevaluation within a fisheries enforcementcase study(using VMSdata),andcomparison to otherapproaches, confirmed the approach isuseful, easytouse, andsuperior to someotherapproaches.
Thisresearch demonstratesthevalue of signature-building filtering approachesfor large movementdata sets.
Acknowledgements
I wouldliketo extend thanks to my co-superv isors,Dr. Rodolphe Devillers and Dr. Orland Hoeber,for having supported me throughoutthisproject.Their insights and comme nts,as wellasmoral support duringthe extentofmymastersprogram were invalu ableto itssuccess .They helped guide mythesistopic,managethescale of what I wantedto accomplish,andenco uraged metolook at problems fromvarious perspectives.
Their responsiven ess allowed metoneverbe stuckonany parti cularprobl em .I amalso trulythankful tothem forhavin gput up with my constantstrugg le in the correctuse of 'that'and'whi ch'.
Acknow ledgeme nts also go out toJerryBlack, Trevo rFradsham,Wanda Arsenault,and their respectivegroupsatFisheriesandOceanCanada(OFO).Without theirinvolvem entin thisproject,in term s of expertise,time,andgoodwi ll,thisthesis would not havebeenpossible. Of equalimportanceis the involvement of all the fisheries enforce me ntofficerswho part icipated inthefieldtrials.Thei renthusiasmandinsightful comme ntswerecrucia l tothe successofthisproject. Theamountoftime that they devotedtothis study,parti cul arly consideringtheirbusyschedu les,was verymuch appreciated.
Thanksarealsoextended toOFOitself,forhavingprovidedthedatathat was usedin thisproject,aswell astheNatural SciencesandEngineer ing Research Councilof Canada (NSERC),the CanadaFoundation forInnovation (CFI),and Memorial University ofNewf oundl and for theirfinancial or in-kindsupport.
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Manypeoplereviewed andcommentedon multipleparts ofthisthesis,making it thedocumentitis today. My sincere thanks areextended toRandal Greene,Cassandra Lee, Garnett Wilson,and KristaJonesfor their valuableinput.I would also liketothank EliGurarieforhelpingmeunderstandtheinner-workings of hisBehavioural Change Point Analysis(BCPA) method,and providingboth codeand feedback viaemail.
My friendsandfamilyalso played a crucial roleinkeeping me somewhatsane throughoutthis endeavour.Fortheir constantencouragement,and puttingupwith my shortfuse towardsthe end,Ithankthem. My colleagues in the MarineGeomatics Research Lab, the UserExperienceLab,and the GeovisualAnalyticsResearch Group, provided a sounding-board forideasandfresh perspectives.Finally, the staffandfaculty oftheDepartment of Geography played a crucialrole inensuring that everything went smoothly throughoutmymastersprogram.
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Tableof Contents
Abstract ii
Ackn owledgements iii
Tableof Contents v
Listof Tables viii
Listof Figures ix
Listof Symbo ls,Nomenclat ureorAbbrevia tions xi
Chapter I Introdu ct ion 1
1.1. Contex tand problem 1
1.2. Questionsand hypoth esis 6
1.3. Goalandobjec t ives 6
1.4. Meth od s 7
1.5. Thes isorga nization 10
1.6. Co-authorshipstateme nt 12
1.7. Referen ces 13
Chapte r2Interacti ve explorat ionof moveme ntdata :A casestudyof geovisualanalytics
forfishingvessel ana lysis 18
Abstract 18
2.1. Introdu ction 19
2.2. Movementdatacomp lexities 21
2.3. Related works 23
2.4. HybridSpatio -Te mpora lFiltering(HSF) 27
2.5. Prototype system 31
2.5.1. Geovisual izationcomponent 32
2.5.2. Interacti ve filtering 36
2.5.3. Signature buildin g 40
2.6. Case studyevaluation 42
2.6.1. Fie ld trialmeth odology .43
2.6.2. Hypoth esis 44
2.6.3. Parti cip ants 45
2.7. Evaluation result s 45
2.7.1. Pre-stud yquest ionn aire .45
2.7.2. Observations 46
2.7.3. Post-stud yquestionnaire 47
2.7.4. Partic ipant scomment s 49
2.8. Conclusion 51
2.9. Acknowl edgem ents 54
2.10. Funding 54
2.11. Reference s 55
Chapter 3 Comparinginteractive and automatedmeth odsforanalyzin glargevessel
movementdata set 61
Abstract 61
3.1. Introduction 62
3.2. Related Works 66
3.3. GeovisuaIAnalyticsSystems 69
3.3.1. VUESystem 70
3.3.2. HybridSpatio-temporalFiltering 74
3.3.3. Behavi oural Change PointAnalysis 77
3.4. Compari son of approache s 79
3.4.1. Participant s 81
3.4.2. Methodology 81
3.5. Result s 83
3.5. 1. Opinions of the Hybrid Spatio-temporalFiltering(HSF)system 83 3.5.2. Opinions of theBehavioural Change PointAnalysis (Be PA)system 85
3.5.3. BCP A segment relevanc e 86
3.5.4. Preferredmethod in term s ofea se-of-use 87
3.5.5. Helpfulness of thetechniqu es 88
3.5.6. Combinati on oftechnique s 88
3.5.7. Overall preferences 90
3.6. Discussion 90
3.7. Conclusion s ... .. 94
3.8. Acknowledgement s 97
3.9. References 97
Summary 103
4.2. Futurework s 110
vi
4.3. References 112
Bibliography .. 116
AppendixA -ICEHR application for ethics review 125
Appendix B -Summaryof ethicalconcerns 129
Appendix C-ICEHRapprovalletter 133
Appendix 0 -Field trial consent form(both field trials) 134 AppendixE - Participantquestionnaire(bothfield trials) 138 AppendixF- Post-studyquestionnaire (first fieldtrial) 139
AppendixG - Procedure fors econd fieldtrial 142
AppendixH-Changepointrecording sheet(second fieldtrial) 143
Appendix I-Generalinterviewquestions 144
vii
Listof Tables
Table 3. 1.Ageneraloverviewof thecharacteristicofeachapproach,basedon participant
responses 94
viii
List of Figures
Figure1.1. Flowof researchandrelated methods 8
Figure2.1.A visualrepresentati on of movementpaths of varyin gcomplexities.Scale and directionof travelare irrelevantwhenestimatingfractal dimension.From left to right, fractaldime nsion=1.161,1.393,1.423,and 1.721 30 Figure2.2.Complexfilteringbasedonvelocity,fracta l,and temporal properties isused to isol atepattern s;multiple coordi natedviewsof the datasupporttheiterat ive visual
exploratio nofthedataby ana lysts 32
Figure2.3.WorldWind-b asedvirtual globe displaying amon th of fishingvessel
movem entpath sinEasternCanada 33
Figure2.4 .Exa mpleof fish ing vessel path :the yellowcurves repr esenttheinterp olated vessel track s and the chevro ns indicatetheposition of collected datapoint sas well
asthedirection of move me nt. 34
Figure2.5.Ellipsesshow ingavesseltrackwith a lowprob abilit y of zonal incur sion, show n by theopacityof theellipses and their amo untofove rlap witha spec ificzone
(shown hereingrey) 35
Figure2.6.Velocit yfilter inghistogram show ing thenumber ofdatapoint s foreach velocityvalue.The filter at thebottom allows forthe selectio nof a subsetofdata,
based onveloc ity threshold s 38
Figure2.7.Fractalfilter selectio n usin g a setof slide rscontrollingthecom plex ity level
(to p)andwindowsize(botto m) 38
Figure2.8. Datapoin tsfilteredusin gfractal filter ing,with1.25<0< 1.5 andwindowsize s
of (a) three datapoint s and(b)25datapoints 39
Figure2.9.Tempora lvisua lizatio nview with indi vidual vesselslistedvertica llyand trip blocksdisplayedhorizon tall y inyellow.Thetimesliderallows for the filter ing ofthe
databasedon tempo ra l thresholds .40
Figure2.10.Signaturebuildi ngand management inte rfacewhichallowsusersto extract
multiplepatterns ofinterest. 41
Figure2.11.Visualizationof threeconcurrentsignaturesbasedondifferen tsettingsfor velocityandfractaldime nsion.Green,red,andyellow areassociatedwith these
different moveme nt pattern s 42
Figure2.12.Mean Percei ved Usefulness(PU) responsesbyparticipant. .48 Figure2. 13.MeanPercei ved Ease of Use (PEU) responses bypart icip ant. .49 Figure2 . 14.MeanOve ra IlUsefulnessvalueofspecificfeatures 50
Figure2.15.Preferred approac h for spec ific tasks 50
Figu ~e3. 1.~~E,HSF,and BCP A approac hes withrespectto auto ma tionand
mteracn vu y 71
Figure3.2. VUE'smapdispl ay show ingafishin gvessel'smovem entpathinEastern
Canada 72
Figure 3.3. The dataanalysi s inte rfacesurrounding VUE'smapp ing syste m 74 Figur e 3.4.Movem entdatarepr esent ationimplement edin theHSF system.Left:
visualizat ion ofall thedata.Right:samedatafilteredto onlyshowarrow heads
wherefishin gactivitiestakeplace 75
ix
Figure3.5.SegmentationofmovementpathdonebySeP Awith thedifferentpatterns
identifieddisplayedusingdifferent colours 79
Chapter 1 Introduction
1.1. Context and problem
Understand ing physical andculturalfeaturesoften involvesthe ability torepresent these features, locatethemwithina givencoordinatesyste m,andsometimes recordtheir changesormovem entsthroughtime (Langran, 1992).The collectionandanalysisof spatio-temporal data, such asGPS trackingdata,populati onmigrationdata, orsocio- economic data,hasbecome an integralpart of manydecision-m akin gprocesses (Andrienkoet aI.,2007b;Tomaszewskict aI.,2007).In parti cular, governments,aswell aspublicand privateorganizations, havebecomeincrea singlyreliant on movementdata, or data about howtrack ed objec tschange through spaceand time.Thesedata are routinelyusedto analyze trafficflows (AndrienkoetaI.,2007a;ChenetaI.,2011;
Willemset aI., 2009), manageinfrastructure (Bomberge retaI.,2006; ManoetaI.,2010), managefleets of vehicles(JeungetaI.,20I 0;Lundblad etal.,2008; pfoser etaI.,2005), or betterunderstand anima lsorecosystems(BertrandetaI.,2007; Focard ietal., 1996;
Marell etaI.,2002;Nams,2005; With,1994). Even individu alsnowhave accesstolarge amountsof personalmovementdata,throughmob ilephonelocalization, orservicessuch asGoogleLatitud e, FourSquare,or image geotagg ing(Eagle&Pentl and, 2009;
Hollenstein&Purves, 2010 ).
These datacan provide arichsourceof informationthatcan helpunderstand comple xprocessesinherent tomovement(Andrienkoet aI., 2008). Of particular interest are behaviour s and pattern s.Behaviour s are defin ed asbeing"the configurationof characteristics correspond ing toagiven reference (sub)set"(Andr ienkoetaI.,2008). In
othe rwords,abehaviouris a setof charac ter istics that, whenusedto filteradata set, consis tentlyisolates rel ated subsetsofdata (patte rns).Andrienko&And rienko(2007) placebehaviour sintothree sub-categories:Individual Movement Beh aviour s (1MB), Mome nta ryCollective Behaviou rs (MCB),and Dynami c Collective Behaviou rs (DC B).
Thesub-ca teg or iescanalso beviewed as ahierar chy, with 1MB focus ingon individual behavi our s, suchastransitbetw eenlocation s,MCBfocusin g on thebehavi our ofapre- determined setof individual s,such as co-locatio noftwo related individu als,and DCB focu sin gongloba l behaviour s, suchasmigration.Thesecategor iescan provide an effective guide in defininga particularbeha viour ,and thereforehowtodetectit.
Incontras t tobeha viour s, Andrienkoetal.(2008) defin epattern s as
"repr esent ati on s ofbehavi ourinsome language,e.g.natural ,mathematica l,graphical". In this sense,apattern can bethought of as a sing le repr esent at ion ofabehaviour,with manypattern spoten tiall yrepr esent ingthe same behaviou r andonepatternbeing potent iall y compose dofmultiple simplerpatterns.Viewe danotherway,Dodge et al.
(2008)defi ne patte rnsas "any recogn izable spatia land tem poral regularityorany interestin grelation ship ina setof movem entdata". Thesearethen dividedintotwo sub- catego ries:generic pattern s,suc hasdispersion orsymme try,andbehav ioura l pattern s, suchasforaging or migration.Again, these sub-ca tego riescanbe viewedas ahierarchy, with behaviou ralpatt ern sbein g compose dof generic pattern s. Conce ptua lly,generic patternscan be relat edbacktotheconc ept of 1MB,whereasbehavioural patterns are compos edof MC Bsor DCB s. Therefore, tounder standand detectbehaviour s,it is cruc ial to startwithgeneric patt ern s.
One oftheissueswhen workin gwith movem entdataisthatthedata sets canbe quitelarge,and many of thetraditi onal ana lys is meth odsdonot scale well tothese sizes (Andrienko&Andrienko,200 7; Jern etaI.,2008).As aresult , ana lysts tryin gtomake senseofthosedata ofte n haveto sift through alarge amo untofdatawithinefficient meth ods,leadin gtopotenti all yvalu abl e informa tion beingmissed ,duetoinform ation overloa d. Thisis com pounded bytherepre sent ation alissues associated with large amount s of data,whereinviewerscanget into "nee dle in a haysta ck"types ofsituations (Keim etal.,2004;Ware,2004).
By focusin g on behavi our sand pattern s, one canbeginto analyze phenomen a such asgroup dynami cs (A ndersso neta l.,2008; Jeungetal. , 20 10), temporal cycles (And rienko etaI., 2008; Eagle&Pentl and ,2009;Wood etaI.,2007), movementpattern s (Demsar&Virrant au s, 20 10;Kwan,2000; Murawsk ietaI.,2005), orattrac tion/repulsion dynam ics (Go tt fried,20 11), whilefilter ing out much of the data whichare not relevant to theanalyst. Studyin gbeha viour s and pattern s canalso provide considerable insightinto the extern alphenom enadrivingbehavi ou r, aswell ashelpin gintheirpredi ction (And rienko&And rienko,2007) .Forinstance,abetterunderstandin g ofwhatexte rna l phenom ena cause "roadrage"behavi our s could leadtothe elabo rationof a predi ct ive model ,helping urban plannersthatwant topreventthese typesofbehavi our s.
Man yapproacheshavebeenproposedtogaina betterunderstandin g ofthese movementprocesses,withsome inspiredfrom biologicalbehaviour s.OptimalForaging Theory (OFT), forinstance,attempt sto model thewayin whichpredatory animals foragin gforfoodmightbehave (Bartume us&Catalan,2009;Charno v, 1976).Itdoesthis bylookin g at the ene rgy balancebetweenenergy spentforagi ngagainstenergy deri ved
fromtheprey.This energy balance often resultsinaform of correlated rand om walk modelwhich exh ibits fractalproperties, suchas a Levyflight (Mare lletaI.,2002) . Deviationsfromthe optimalforaging behaviour can thenbe analyzed toprovideinsight intotheparticularbehavi ourbeing exhibited bythe animal.
Anotherapproach isthat of representin gthemovem entdatatopromote itsvisual analysis.Thisistypicall y addressed by either traditional cartog raphicapproaches, interactivegeovisualizati on appro aches,orautomatedclusterin g appro aches, each having theirbenefits and drawbacks.Cartog raphic approaches such asHagerstand ' sspace-tim e cube (Hager strand,1970),which plotstwo-dimen sions ofspaceagainst time withina three-dimen sionalcube, allow the viewertoquickl yunderstandhowmovem entsand interactiontookplace,boththrough spaceand time.However,usin gtraditi onal approaches the viewe r cannotdirectlymanipulat ethedata,unlikeintera ctive geovisualization techniqu es (AndrienkoetaI.,2007a;Kraak, 2003; TurdukulovetaI., 2007;Wood etaI.,2007;Zhaoet aI., 2008).Theseallowfor more spec ific questionstobe studied through visual repr esentations and interacti vefiltering and highl ighting.However, they also requir emoretime andeffort than automatedclustering techniqu es,suchas Self Organizing Maps (SOMs)(ChoietaI.,2006; Koua&M.-J.Kraak, 2004).Automated clusterin g enables rapid analysisoflargedata sets, but withahighcomputationalcostand limitedcustomizabilit y ortransparenc y.
Movementdata canalsobeanalyzedusingpurelymathem aticalmodelin g(Franke etaI., 2004),statistical(Gurarie etaI.,2009;Underwood&Chapman, 1985), or data- mining app roach es (Lietal.,2006).Theseapproaches analyze thedataand reportresults withoutnecessaril yhavingtovisualizethedata set.Incaseswhereth eresult s are
visua lized, usuall y only the extracted pattern s arerepr esent ed . Forvery largedata sets, thisis anobvio usadvantage,asistheprimaril y algorithm ic natu re ofthese approac hes.
How ever,this canalso leadto ana lysts missing spec ificaspec tsofbehaviou r,or anoma lous pattern s,which theymayhavenot icedhadthedatabeen visua llyreprese nted.
Theseapproachesalsosuffe rfromalack oftransparency,in that ana lysts maynotbe able toidenti fythe effectsoftheautom atedprocesses on theirdata.
Most oftheseprop osed approac hesado pta particular perspect ive,be itbiological, geovisua l,ormath em atical.How ever, few approac hescombine thesemultiple perspecti ves,to achieveamoreintegrated and hol istic approac h.Integrated approac hes mayhelp ana lysts deal with the verylarge amo untofdatathat are oftenassociatedwith movem entdata sets. Parti cul arl y, combiningforag ing theory (Bartume us&Cata lan, 2009)andcomplex filter inginan interacti ve geovisua lana lyticsenv ironme nt(Ho&Jern, 2008; Johan sson&Jern , 2007;Lundbladet aI.,2008;Tomaszewsk ietaI.,2007)can provide a genera lize d hybrid approac h to analyzingthese largemovementdata sets.
Further,man y of theprop osed approac hesinthis dom ainhavenotbeen evaluated in terms ofusab ility or usefuln ess,norhave they been comparedto oneanot herinany meanin gful sense.Doin g so could provide conside rableinsig ht intothe situat ionswhere one approachma ybe supe rior to others.Itmay also identi fypotenti al s forimprove ment or integrati on of variousapproac hes .
1.2. Questions and hypothesis
The primaryresear chquestion sto addresse d in thisthesis are:
• Whichmovem ent characte risticscan beusedtobuildsignatures thatidentify speci fic behaviou rs?
• Howcan a geov isualanalytics environme nt bedesignedto effective lyallow for visual explorationof largemovementdata sets?
• Howcan a geov isua l analyticsenvironm entbedesignedtomaximiz eusabilit y amo ng analysts?
• Doestheapproa ch developedimprove analysts' abilityto extrac t movem entpattern s from theirdata sets?
• Howdoesthe proposedapproachcomp are in terms of usabilit y and effecti ven esswith existin g approa ches?
Theresear chhypoth esisisthat ageovi sual ana lyt icssyste mallow ing filtering on multiplecharacteri stics of movementwill improveanalysts' abilitiestobothdeal with largeamountsof movementdataand find interestin gpattern swithin them.
1.3. Goal andobject ives
Thegoa lof thisresearchisto elabora te, implement, and test anovelhybrid approachtothe analysisofmovementdata, combining the charac terist icsof movementin ageo visualanalyt ics environment,aswellas studying the situationswhere thismeth od, andothers,maybe most suitableforuse.
Toattainthis goal, thespecificresearch obj ectivesofthisthesis are:
1. Studycharac te risticsofmovem ent thatcan beusedtofilterthedata, and theirrelation tomovem ent patte rnsandexhibited behaviour s.
2. Design anefficientgeovisua lana lyticsenviro nme ntforthe visua lexploratio noflarge movem entdata sets.
3. Designagene ric approachto complex filterin g of movem entdata such thatspec ific movem entpatt ern signaturescan be elaborated.
4. Implement theapproach usin g aprototype softwaresystem.
5.Validate the usabilit yand usefulne ss of theapproac h, usin gfisherie s enforce me ntas a case study.
6. Com pare the approac h to otherapproacheswithin this same fisher iesenforcem ent casestudysett ing.
1.4. Methods
The researchmeth odology followe d bythisthesis is summarized inFigure 1. 1, and hasproceed ed ina generally linear fashion,from ident ificat ion ofresearchquestions, todesign,implement ation ,valid ation,andfinallycomparison.Lite rature review and communication withexperts, theresearch community,andvariousother interestedparties was ongo ing throu ghouttheproj ect. The informati on gainedfrom these inte ractions,and fromreview ing existin gwork s,inform ed every aspect oftheprojectand help edin identi fyingdirect ion s.Theapplicationof theconcept sinto aprototype system and the validationproces sbothgeneratedissue sthatrequired communication withexperts.Asa result ,therewas co nt inua l interactionbetweenthe practic al and theoretical aspectsof this work.
The initial literature review helped identify the currentstate knowledgein fields related tomovementdata visuali zationand analysis.The fields of behaviouralecology, pattern detection , and geovisualanalytics,providedinformationastohowtosupportthe
analysis of large movementdata sets.From these,thecombinationof fract aldimension, tocharacterizemovementpath complexity,and velocit ywashypothe sizedto lend itsel f to
the detection of specific behavi ours.This,combined with theconceptsof interactive filtering,signat urebuilding, and geovisualization,compo sedthe coreof the design.
Literature review
Rapid Applieation Developm enl (RAD ) Design
•HYbrj~fi~~;~~1dimension oVe/oeity aTemporal
•Behaviour signatures elnteractive geovisualization
I : NA~~£~~:~~~~~~~rk
I
Itera tiveapproach
Approach validation
•Partieipanlreeruitmenl,byDFO
•Meetings
~~:~b:eu:e
oQuestionnaire oComments
I :
Compar ison study
'I
Partieipanlreeruilment.byDFO Meet ings Use of mullip/esystems Feedbaekt hrou ghlnterviews
Communications
•Presentations:departmental,conrerences
•Meetings:domalnex perts,studyparticipants,other researchers ePublications:journalarticles,conferenceproceedings,thesis
Figure 1.1.Flowof researchand rel a tedmeth od s.
The design wasthenimplem ented on top of anexistingvirtua lglobe geovisua lizatio nsystem,i.e.WorldWind.Aniterativeapproac hsim ilarto thatofRapid Applicatio n Developm en t (RA D)(McConnell,1996) wasfollowed,with design and implem ent ationproceed ing featurebyfeature.Featureswereimpleme ntedfrom auser- centeredviewpo int,ens uring that eacheleme ntof the implem entati on wasusable and understand abl ebythetargetuser group, in this casedata ana lysts.
The finalphase oftheRADprocess wasthevalid ation ofthe approac h implem ent edbytheprototype syste m.Thiswas achieved bytheuse ofafieldtrial meth odappliedto afisheries enforceme ntcasestudy .Fisher iesandOceansCanada (DFO),apartn erin thelar gerprojectthis thes isfalls under, were asked to selectanumber of fisheriesenforce me ntoffice rswhichwerefam iliarwith the ana lys isofvessel movem entdata.The dataused for thisproj ect werepro vided byDFO,andextractedfrom theirVessel Monitor ingSystem(VMS)database fortheyear 2009.Itcoveredallof AtlanticCanada,fromtheGulfofSaintLawrencetothe edge of the Grand Banks of Newfoundland,andfrom the Southerncoast of Nova Scotia to Northe rnLabrador.
Thefield trial s were cond ucted inbothSt. John's,NL (April,2011),and Dartm outh,NS (June,20II),onan individu al basis.Intotal,nine enforce me ntofficers particip ated intheapproac hvalidation.Officerswereaskedsomebackgro und inform ationto assess expe rience levels,usedtheprototype syste m,and then answe reda questionn aire. This questionnair e,based on the Techno logyAccepta nce Model (TA M) (Davis,1989 ), yielde d quantit ativ eand qualit ativ efeedb ack astotheu sefulnessandease of use of the system.
In addit ion,a second fieldtrial scheduled in paralleltotheDartm outhfieldtrial, allow edcompariso nofthe proposed approac h to othe rapproac hes .The purpose ofthis field trialwasto gathe r qualit ativ einformati on as totheusefulness oftheprop osed approa chrelative to otherapproac hes.Spec ifica lly, the compariso n between an existin g trad itionalweb-mapping syste m,anautomatedapproac h,and the propose dapproac hwere inves tigated. This was doneinorde r to assess howtheprop osed approachcompares to otheralternatives in thecontex tofourcase study.
The communicati onof the researchoccurred throughouttheproject, sim ilarly to theliterature review.Multiple preliminarymeetingswere heldwithoro,to discuss projectideas,present someearly researchprototyp es,and to set up the field trials.The initialconcept for thisresear ch was presentedin theform ofa researchprop osaltothe Departm ent ofGeograph y (April,20 10) .The main approac hand researchprototype were presentedbythe candidateat theGeoVi z 20 11Work shop in Hambur g, Germa ny(March, 2011),andsubse q uentlyata researchseminarat theDepartm ent of Geography(April, 2011).Earlyfind ingsabo ut the compariso nof the approac hwithothersystems were presented at theMarit ime Anoma ly Detection (MA D) Workshop inTilburg,Netherlands (June,20 11). Duringthesepresentati ons,manyinterestin gdiscussion swere generated withother researcher s,highli ghtin gusefulliteratur eandsugges t ing potent ial improvem ent sthatcouldbemadetotheoverall approach .
1.5. Thesis organization
This thesisusesa manu script forma t, withchapte rs two and threebeingjourn al paper sthathavebeen subm itted tointern ationalpeer- revi ewjourn als. Chapter twodetails
10
the elaborationoftheproposed approac h, includin g aliterature review, adescrip tion of theapproach,aswell asitsfield trialvalidation,and hasbeen submittedtothe journal Inf ormat ionVisualizat ion.Itdescribes anovel approac h for visua lizingand analyzing movementdata, called Hybrid Spatio-tempora lFiltering(HSF),thatexploits the physical, fracta l,and temp oral characteristicsof movementto isolatespecific typesofbehaviours.
These filtering settingscomposeabehavioural signature,which can be combinedwith othersignatures,orre-usedon differentdata.Thefield trialvalidation ofthe approac h showed boththeusefuln ess andease ofuse ofthis approach.
Chapterthreefocu ses on the comparisonof theHSF approach totwo other existingapproac hes,and hasbeen submitted totheJournalofOceanandCoastal Management.Spec ifically,a current ly used analysissystem(DFO's VUE system),an automa tedsystemimplementing Behavioural Change Point Analysis(BCPA),and the interactive approac hof HSF, werecompared bymeans of a field trial.DFO's expertswere asked touse eachsystemand provid e feedback regardingtheirindividu albenefit s and limitations, aswell asanypossibiliti esforcombinin g approac hes inspecificcontexts.
This real- world comparisonof theusefulness of theHSF approac h,incomparison to other methods, showe d thatthis approac his superior to existingandrecently developed approac hes,andcould be applied to anumber of different dom ains.Italsoide ntifieda balance ofparam eters (ease -of-use, transparency,functiona lity, andspeedof analysis) that are critica l in theusefuln ess andacceptanceof approac hesinspecific domain s.
Chapter foursummari zeshow eachof theresearchquestionshavebeen addressed and whetherthehypoth eseswerevalidated.Inadd ition, the chapter highli ghtsthemain contributio nsofthisthesis, andexploresfutureopportu nitiesfor research.
II
1.6. Co-authorshipstatement
This projectispart of a broader Natura lSciencesandEng inee ring Research Counc ilof Canada (NSER C) Strateg ic Project s Grant(ST PG P365189-08), headedbyDr.
Orland Hoeb er,inComputerScience,and invol vin gDr. Rodolph eDevill ers,in Geography,amo ngother researchers.Fisheriesand OceansCanada(DFO)areindustry pa rtnersonthegrant.Early meetingswith DFO identifiedanumber ofpotent ialresearch projectsthatwouldbe ofinteresttothem,oneofthesebeingrelatedtothe geovisua lizationofcomplex fisheri esmovem entdata.Whilethe gene ra l topi cwas constra ined todealin gwith fisheriesdata,thecandidateindep end entl ydecid edtoresearch thecombinationof fractaldimen sion,velocity,temp oral filtering,andgeovisua lization,to designa flexiblegeovisua lana lyticsapproach to ana lyzing move mentdata.Thesegeneral conce ptswereagreed uponbyboth co-superviso rs,andtheywereforma lizedthro ugha thesisprop osal.
The practicalaspectsof theresearch,includingliteratur erevi ew ,design ofthe spec ific approach,attendin gmeetin gswith DFOoffic ials, developm ent oftheprototype system,cha iringofthefieldtrials in both St. John 'sand Dartmouth,were undertakenby the cand idate.Anapplicatio nfor jointethica lreviewof thefieldtrialsassociatedwith this work,alongwithanother proj ectundertheumbrell a ofthelarger grantwas submitted by apost-doctoralresearch erworkin g on the other proj ect. The cand idatedesignedthefield trialmethodologyusedin this thesis,in collaborati onwith myco-supervi sors,conduct ed thefieldtrials, and alsoexecutedthedata compilati on and analysis ofthe fieldtrial
12
results.Thisincludedboththequantitativeresults ofthefirst roundoffie ld trials, and the interviewtranscriptions andanalysisofthe secondroundoffield trials.
Thetwojournal articles includedin thisthesis, chapters two and three,were initiallydeveloped as outlines bythe candidate.Thesewere latermodifieduponreceiving recommendationsfrom both co-supervisors, tomakecertain that thedivisionbetween bothpaperswas acceptable and that theircontentwouldfitwith thetargetedjournals. The firstdrafts of eachpaperwerewrittenbythe candidate, after which point the revisions proceeded inan iterativereviewprocess,wherein the candidate revisedthe manuscripts based on thecomments ofthe co-supervisors.The candidate istheprimaryauthor on both papers,with both co-supervisors being co-authors.Thecandidate isalsotheauthor of this manuscript,integratingthepapersinto a coherent thesis.
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Chapter 2 Interactive exploration of movement data: A case study of geovisual analytics for fishing vessel analysis
Abstract
Theanalysisoflargemovementdata sets is a challengingtask,duetotheir size andcomplexity.This paperpresents aninteractivegeovisualanalyticsapproach named Hybrid Spatio-temporalFiltering(HSF) that integrates filteringofmultiplemovement characteristics,geovisualization, and multiple coordinated viewsto enable analyststo focuson movementpatternsthat areofinterest.This study proposes anoveltechnique that combines the fractal dimension andvelocityofmovementpathsto effectivelyfilter out uninteresting records, through an iterative signature-building process.Inaddition,the fractal dimension estimationisperformedusing amoving-window technique,which allows investigations at multipletemporal scales.These tools are used inconjunctionwith aprobability-based zonal incursiontooltovisuallyrepresentwhenthemovementnears areasof interest, and helpidentify specific types ofbehaviors.These featureswerebuilt todeal with data sets havinglow and uneven sample rates. Field trials withfishingvessel movementdataillustratetheutility ofthe interactivefeaturesand visualr epresentations ofthemovementpatterns.Within a geovisualanalyticsframework, the approachallows analysts to explore largemovementdata setseasilyandefficiently.Thecombinationof velocity,fractal dimension, and temporalfilteringhelpedanalysts effectively identify subsetsofdata that conformed toparticularbehavioralpatterns of interest.
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2.1. Introduction
Theana lys isofla rge mo vem ent datasetsis a cha llenging task,duetothe sizeand com plexi tyofthesedata sets (And rienkoetal.,2007a;Dykes&Mountai n,2003;Kraak
&van deVlag, 200 7). Howe ver, suc hana lysescan provideuse fulinsight s into the
beh avior of movin gtargets,helpin g ana lysts toidentifytrend s,patt ern s, oroutliers within thedata.Whilemovem entdata can be visua lized inanon- sp atialmann er (Eagle&
Pentl and , 2009; Unde rwoo d&Chap ma n,1985;Zhaoetal.,2008),geovisua lizat ion systems can provideeffec tive waysfordealin gwith vari ou s aspe ctsofthe complexityof mov em entdata.Man y syste ms havebeenprop osedin thepast,withvary ing degrees of success(And rienkoetal.,2007a;Eagle&Pentl and , 2009;Ho ferl in et al.,2011;Jern&
Franze n,2007;Kwan,2000) .None, however,have exp lo ited themov em en t' s comp lexity and physic albound storedu cevisual complex ity and provid e insi ght intothedata.
Tothisend,thisresear chfocu ses on thedes ign andevalua tionofa method that hel psredu cethe amo untofdata anana lyst need s to investigate.TheHybrid Spat io- temp or al Filte ring(HSF)system presen tedpro vides ana lystswithan inter facethat em ploysa nov elfilterin gmech ani sm and uses visua l repr esent ati on sto add ress the inhe re ntcom plexi tyofthedata.Theapproac h taken exp lo itsphys icalcharac teristicsof thedata (i.e.veloc ity, head ing),temp oral cons trai nts,and the comp lex ityofthe movem entpath ,quantifi edus ing fracta ldime nsio n,toincreasethe effectivenessof an interactivefilterin g syste m.Aninterac tivegeov isua lizationinte rface isprovid edto gra ph ica llysho w the aspec tsofthedatathatmatchthefilter,as wellastoillustr atethe possible extentoftravel betwee n the logged location s.
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Altho ug hothe rs have exp lored meth od s forfilte ring movem entdatausin g veloci tyand time, anovel aspectofHS F istheuse of fractal dimen sionto filter the data.
Frac ta l dimen sion gives aquan titat ivemeasu re ofhow muc han object,oramovem ent path,fills itstheoreti cal space(Mande lbro t,1967;The iler, 1990).Itis essentiallya numeri calmeasu re of path comp lexi ty,some timesreferredto asto rtuosit y. Frac ta l dimen s ionis sca le invari ant,wh ichaffords it robu stn esstomovem ent s ofdiffe rentscales andgaps in datasets. This allows thecomp ari son of movem ent sthatdiffernot only in scale but alsoingeome try.
A hybridfilterin gfeatur eusin gphysic al cha rac te risticsand fracta l d ime ns ion, togeth erwith temp oral andobjec tof interestinform ati on,pro v ides ana lys tswithahigh degree ofcontr ol on howthedataare filtered.Oneofthereason sfor com b in ing the physical charac te risticsof mo vem ent with fractaldimen sion is that physical cha rac ter isticsof moveme ntareverysensi tive to erro rsoromissionsinthe data,whilethe fracta l dimen sion estimatio nisnot.However,physical cha rac ter isticsareeasier to understand astheyhav e adirect ana log ue in thereal world,whe reasthe conceptof fracta l dimen s ion is moreabstract.
The iterat ivemodi ficati on of filtersett ingsto extrac tspecific types ofpatt erns allowsana lys ts tobuildsignatures tomatch speci fic movem entbeh avior sthat arebe ing soug ht.For instan ce,tran sit and migrati onpattern s, whe reobjec tsare movin g fromone placetoanoth erina stra ight line,wouldtend tohave ahigh veloc ityand a lowfractal dimen sion.Incontras t, fora gin g andsim ilarsearc hing pattern swouldmanifest them sel ves as fa irlycom plex lines,tendin gtoward s alowervelocit y and high erfracta l
20
dimension.Multiple different signatures may also beusedtogether ,inorder toidenti fy difference s orsim ilarities inspatialortemp oraldistribu tions,forexa mple.
Due tothelarge sizeofmovem entdata sets,impleme nting this approach ina visualfashion requir esan interacti ve geov isualana lyticssyste mwithsupportfor large spatio-tempo ra l data sets. HSFuses avirtual globe repr esent ati on,withindividual data pointsdispla yedaschevron glyphsand lines connectingsubseq ue nt record s.This glyph- basedrepresentati onlendsitselfwelltofiltering,and is straightforward tounderstand.
The virtual globe allowsfor spatio-temporaldata from anywher ein theworldtobe represented, without having to dealwith projectionzones or distorti onwhenzoo ming in tolocalviewsorout toglobalviews.
Totestthis approac h,aprototypeimplementation ofHSF wasevalu atedthrough a case study usin gVesselMonit orin g System(VMS ) mo vementdatacollect edin2009 from vesselsfishin gin the Northwes tAtlantic region.These datawereprovidedby Fisheries and Oceans Ca nada(DFO).Theevaluat ionwas conducte das afieldtrialusing ex perts at DFOwhose prim arydutiesincludefisher ies enfo rce me nt.The purpose ofthis evaluation wasto establish wheth erthe experts foundthis geovisual analyti cs approac h to dataanalysisuseful andeasytouse,incompariso n totheir current pract ices.
2.2. Movement data complexities
There exist anumberofinherent complexit iesassociated with movementdata, which donotnecessarily ex ist with other typesof data(Kraak&van de Vlag,2007;
Rodi ghi ero,20 I0).Generall y speaking, movementdataare composedof a latitude, longitude ,and timestamp ,with ancill arydata suchas ve loc ity,headin g,andaltitude
21
commo nly included .Thetemp oralresolution ofthesedata, or the amo untoftime between collecting datapoint s,plays an importantrolein howthedata can beused.For instanc e,data collected onan hourl ybasiswill notlendthems elve stothe same type of ana lysesasdata collected every10seconds. The spatialaccurac iesofthedata also play an importantrol e astheyrelatetothe spatia land temp oral scales atwhic h data can be analyzed.Sim ilarly, temp oral accuracycan play an important rolein the accuracyof a data set.For instance,some posit ionin g syste ms used tolocate birds deri vethe geographic coordin ate sfrom the apparent elevation ofthe sun,whichacts as aproxy for time (Schaefe r&Fulle r,2006). Significa nt inaccur acie sin thesetimeestim ateswould leadtolar geinaccur aciesin reportedpositi ons.
Added tothese facto rsis thevolumeof moveme nt data collected.Tem pora l resolut ion directl y affects the amountof data thatare recorded,sin cefor equa lspansof time, doublin gthetemp oral resolutio nofthedata collectio nsystem dou blesthe amo unt ofdatacollected.Moreo ver ,whileincreasin gtherate ofdata acquisit ion maybeusefulin somecases,itcan have d rawbac ks when trying toanalyze data atcerta inscales.
However,these are nottheonlyfactors to cons ide r.For instan ce,the amo untofancillary datarecord ed , as wellasthenumber of objec tsofinterestbein gtracked,increasesthe volumeofdatatomanage andvisualize.
Otherimpo rtant issuesto considerare the syste mat icandrando merro rswithin the data.Certain positi onin g syste ms,suchas GPS,maynot always be able to acquire a positi on (e.g.not enoughsate llites in view).Insuchcircumsta nces, theymay systema tica llysendoutade faultpositi on, suchas where the syste mwasinit iall y calibrate d,oran impossibl eposit ion (e.g.95°N).Addedto theseare rando merrors,such
22
aswhen the systemisunabl etorecord adatapoin tdue to computer mal functions or transm ission erro rs.Theseerrors may appea rasrand om gapswithinadata setandcan complicateana lyses thatdepend onregularsampling inter vals.
2.3. Related works
Visuali zat ion ofmovementdatahasbeen anactive topi c ofresearchand innovationforhundreds ofyears, withfamousexamplessuchas Charles Minard's1869 flowmapofNapole on' sRussiancamp aign of 1812 (Tufte,200 I).Likemostflowmaps, Minard'smaprelied oncartog raphicgeneralization and data summa riza tion tohelp the viewe r makesenseof the data.Whilethiswork s well for static mappin g ofsmall data sets, it requires either the cartog rapher tounderstand theentire data setbeforeit can be genera lized,or theuse of auto matedgenera lizatio n tools.Duetothe sizeoftoday's mo vem entdata sets, that canoften rangeintomulti-gigabyte orterabyte sizes,full understandin g ofdata setsoften requir estoomuchtime and manu al effortto bepract ical;
automa ted toolshavebecom e anecessity (And rienkoetal.,2008a).Moreove r,the thres ho ldofwhatis cons ide reda "large" data set islikelytoincrease overtime,givena comme nsura te increase incomputer processin gpower anddatastorage den sit y. As a result,there is a continuing need for progressivelybettertechn iques todealwithlarge data sets .
Some of themorerecentkeywork s exploring thevisualizati on ofmovem entdata include Hagerstrand' s space-time cube,which represent stwo-dimen sion almovement s throughtime in a three-dimensional cube(Hagerstrand,1970).This meth odhasbeen expandedand enhancedinanumber ofw ays,such as stand ardi zin gthepath sto a
23
commo norigin(Kwa n,2000),addingactivityorpattern classes(Re n&Kwan, 2007), and integratingthe conceptof the space -time prism (Kraa k,2003). These increm ent al improvem ent s,whilebeneficial ,havenot overco meoneofthelimitation s of this approac h: it relies on directdepi ction ofdata,rath erthan summa rizationor pattern extract ion(Andrie nkoetaI.,2008a).As aresult,ana lystsare oftenoverwhe lmedbythe large volumeof comp lex data they needtodeal with. Directdepiction , whilefaster,only offersanoverviewofthe entire data set,whilecartogra phicsummar izat ionofdata setsof this size canbedifficult.Forthisreason,someformofcomputer-a idedsynthes is, through catego rizationor pattern extraction, is an attractiveoptio n.
Approac hes for conde ns ing movem ent data toitsmost impo rtantcomponen ts has been exploredinanumber of differentways (Andrienkoet aI.,2007a;Kwan,2000;
Rinz ivillo etaI.,2008;Willem s etaI.,2009).Itcan be achieve d usin g,forinsta nce,data classifica tionorcluste ring techn iques.Identifying cluste rsorpatte rnswithinthedata and mergin g sim ilarones can grea t ly redu cetheamount ofdatatodispl ay (And rienkoetaI., 2008a).This identifi cati on of movementclusters canfollowvario usapproac hes, including theidenti fication ofbehavioralpattern s (AnderssonetaI.,2008;Dodge et aI., 2008),movem ent charac te ristics(Gottfried,2011;Pelot&Wu, 2007), orlevel s of traffic density (Lax hamma retaI.,2009;WillemsetaI.,2009).
Thevisua liza tionof these cluste rsofmovementdata, and themer gin g of simi lar clusters,can thenhapp eneitherautomatica lly(And rienkoetal.,2007a;Gura rieeta l., 2009;Rinzivill o etaI.,200 8),or manu all y (AndrienkoetaI.,20 11; Pelot&Wu,2007).
Both approac hes haverelat ive strengthsandweaknesses.Forinstan ce, automa tedpattern detection reduces the amo untof work requ ired forananalystto acquire knowledge,but
24
also rem oves a certa inamo untof contro l,which isretained withinte ractive summarizatio n techniqu es(Andrienk o etaI.,2008a).Intera ctiv etechniqu es allow fora greate r explorati on of the data , since theydonotrely on default settings, butalso requir e more timeand workto acq uire useful know ledge.
Much researchhasbeendo ne within the biologycomm unity to analyzeand understand moveme nt data.Trac kinganima lsusingdevicessuchas collarsorarchiva l tagsis a com mo n procedur etohelpunderstand or mod elmigrat ionpattern s and populati ondistributi on s (Foca rdietaI., 1996 ; FrankeetaI.,2004) .Optima l foragin g theo ry (Ba rtume us&Catalan , 2009;Charno v, 1976),corre lated rand omwalk s (Mare llet al.,2002),orother tec hniques(Cho ie tal.,2006;Franke eta l.,2004;Marell etal.,2002), are meth odsthat were developedto extract useful know ledgefrom thepattern s withsuch data.However, mo vem entdata canalso be ana lyzed inotherways,suchasusing fractal dimen siontoinvest igate whetheranima lsof diffe rentsizes perceivelandscapesat different sca les (Nam s, 2005;With,1994).Ithas also been show n thatthese same techniqu escanbeapplied to some humanmovem ent s suchasfishin g vessels,asthey behave sim ilarly tonatur alpredators (Bertra ndet aI.,2007).
Theconcep tof fracta l dimension was origina llyprese ntedby Mande lbrotin 1967 (Mande lbrot,1967),as'fract iona ldime nsio n',whic hessentially desc ribes theself- sim ilarity,orcomplex ity,within featur esunder study(e.g.lines,polygo ns,polyhed ra).In the contex tofmovem entdata,it allowsforthedifferenti ationbetw een simplestraight- linemovem ent s,suc haswalking fromahouseto abus sto p, to complex jagged movem ent , suchassearching for someo neina crowd.Itcan bethought of as the rat io
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betwee n the actual distance covered by amovementpath and the straight- line distance betweenits startandend poin ts.
Calculating theexact fractaldimensionvalue foragivenfeature requ iresthat itbe defined math ematically. When dealing withdata that werecollectedthro ughsomeform ofmeasurement or sampling, this approac h isnot feasible. However,it is possibleto estimate the frac taldime nsio nof features using a varietyof methods, suchas usingthe correlation dimension orbox-coun ting dimensio n(Theiler,1990). Manyofthese methods,however, suffer fromper form anceissuesrelatedtotheirreliance on mathematicaldifferent iation. Asa result , anumber ofmethodshavebeenrecently proposed to address thisissue (Ftichslinetal.,2001;Sevcik,1998). Theseadvances, coupledwithincreases in processing power,make theestimationof fractaldimension fromlarge movement data setspossible in nearreal-time.
Anotherapproac hfordealin g with large volumesofmovement datais thatof anomaly detection . Anumber ofdifferenttechn iques can beused todetect anomalies in movem entpattern s,suchasrule-based systems(LietaI.,2006),statistica l methods (LaxhammaretaI.,2009),or pre-determ ined criteriasets(Sage, 2005). Thecommon featureamong these approac hesisthe specification of whatareconside red'normal' behaviors, thus allow ingtheextractionof insta ncesof behaviorsthatdonot fitthese profiles.
Suchanapproac hofbuildingupofprofiles ormodels for movem entpattern sis notlimitedtoanom alydete ction.Itcan beusedtopredict whereandwhen trackedtargets willbe or havebeen (Bombergeret aI., 2006). Theaccuratemodelingof the movements of vehicles, animals,orpeople,using data fromposition recording system,can be quite
26
usefulduetotheinsi ghtprovided intowhat mighthapp enbetweentwo consec utive data point s (Cha ngetaI.,20 10) .Thisadditiona l inform ation can helpmaketasks suchas estimationof fishin geffort(Den g etaI.,2005; Mills etaI.,2006; Muraw ski etaI.,2005), species mod elin g (M ullow ney&Dawe, 2009),or the visua lizat ionof thedatamore accurate(Lund bladeta l.,2008; Rodi ghiero,2010).
This paperpresentstheI-ISF approac hand the associatedcomplex itiesfor usin g a visua lapproac h toana lyz ingthe data,suchashybridfilterin g and interactivity. TheI-ISF prototype syste mcan becon sider ed anexamp leof a geovisua lana lyticssyste m.
Geov isua l analyt ics is thesub-fieldof visuala na lytics that dea ls w ith thec ha llengeso f exploring andana lyz ingspat io-tempora l data (AndrienkoetaI.,2007 b;Thomas&Cook, 2005).The main goalof visua lana lyticsistoprom ote thevisua lana lysisofdatato acq uiresomeform ofknowledge whic hcouldotherw isebemissed by visua linspectionor entirelyautoma tedapproac hes.lt relieson interactive represe ntat ionsof the data,aswell as com prehens ive dataanalysistools,toprovide arich enviro nme nt forusersto explore and manipulate complex data sets,and to support decision-m akin g.
2.4. Hybrid Spatio-Temporal Filtering (HSF)
The meth odprop osedinthispaper takes a geovisua lana lyticsapproac hto solving theprobl em s ofvisualizin g andana lyzing large moveme nt data sets thatmayhave low and un-even sample rates.Interactive filtering,multiplecoordinated views,and details on demand givecons ide rableana lytica l pow ertothis approach.Itallow sfor the identification ofspec ific type s of movem entbehaviors, as wellas other types of features
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ofinterest, such as group dynami cs, congestedareas,and temp oralcycles (e.g. day-ni ght cycles orseaso na l cycles).
Formally,HSF is the approachof integratingvelocity,fractal dimension ,and temp oralfiltering,into an interactiv e geovisua lizationenvironment,supported by multiplecoordinatedview s,to aid in the analys isof largemo vem entdata sets . Particul arl y,theintegration of velocit y and fractaldimensionfilterin g allo ws forthe elaboration ofspeci fic movem entpattern'signatur es'based on filter sett ings, whichcan beusedtohighlight differentpattern swithin the data.
Movement datatypically donotincorp orate ancillary parameters, suchasvelocit y.
Asa result ,theHSFapproa chdoesnotrely on thesedatabeing present ,and proceeds to anauto matedestima tionof velocit y values, based on thestra ight-linedistancebetwe en two subsequent datapoints .Byusin gthe ratioofthedistancebetweentwo subsequent datapoint s and theamount oftime elapsed betw eenthese,an averagevelocit yis estimated(EquationI).These veloc ities are thenrepresentedvisuallyas ahistogram . This both helpsana lysts understand the distributi on of'veloc ity valueswithinadata set,and select the range ofveloc ities thatrepresentsom ebehavi orin whichtheana lyst is interested .
(I)
Fractaldimen sionestim ationcanbeacco mplished usin ga meth odpresentedby Sevcik (Sevcik,1998).Fractal dimension , as a measure of path complexity,has been shown tobe usefulin identifyin gthetypes ofmovem entbehaviors an individualis undertakin g (Bart ume us&Cata lan,2009; Bertr and etal.,2007; Marell etal.,2002).The
28
ident ificat ion of such beha vioralpattern sisimportant forana lysts in that itallows themto focus onspec ific types of activiti es, orsetsof relatedactiviti es,and provid esthemwith an idea of their spatial distributi on.
The fractaldimensionmeasur e is a real numberbased on the inherent dimen sion al ity of the data,suchasonedimens iona lfora stra ight line, or twodimen sional fora complexvessel path onaplane.As such, the fractaldime nsio n(D)fortwo- dimen sion almovementdatacan range from 1.0to 2.0,with 1.0bein g aperfectl y straight line and2.0 being apathso complex thatitcovers theent ire two-dim ensionalplane (Figur e2.1).Itcan be estimat edusin g aratio of theunit-squ aretotalpath lengthtothe number of datapoints (Equation2).
DSevcik=1+
I~:g(;~)
(2)An interestin gpropert y of fractal dimensionisthatit is scale invariant. This mean sthat anytwopathswithgeometricalsim ilarity haveasim ilar fractaldimension valu e,regardle ss ofthespatia l dimensionthey occupy.For instance,twomovin gtargets that areexhibitingsim ilar path s,butwithonegoing twiceas fastor tw ice asfarasthe other,and thus covering twicethedistance,wouldstill have thesamefracta l dimension value.This is obv ious lyadesirabl eproperty whe nattempt ingtodefine genera lizedfilters to identifyspec ific types ofbehaviors. Anotheradva ntageoffractaldimen sionfilter ingis that it isinsen siti veto gaps in thedata, asthe geome tryofthe paths is what is be ing used to estimate thefractaldimension,ratherthanindividualdatapointlocati ons.Thisis an import ant featurewhen used with datathat includ es a significa nt number of dropped data points orerrors.
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Figur e2.1. A visualrepresentation of movement paths of varyin gcomplexiti es.Scaleand direction of travel are irrelevant when estimatingfractal dimension.From left to right. fractaldimension=1.161,
1.393,1.423, and 1.721.
Est ima t ionof frac ta l dimen sion istyp icallydon e over full data sets,inorderto increase accuracy.Whilethis workswellfor identi fyin gprevailin gbeha vio rs over la rge data sets,thismakesit unsu itabl e for estima tingcha nges inbehaviorover time.Inorde r to support filter ing ofthedata, amodifi cation of theorigina l meth od (Sevcik,1998 ) was don e,by allow ing multi plesub-sets ofthe datatobe evaluatedandcompare d,usinga movin g window.This moving-wind ow fracta ldime nsion,witha variablewindowsize, allows the est ima tionof fracta ldime ns ion tobe tailoredto specific types of behaviors . Forinsta nce,ifanana lyst knowsthat a specificbehavio roccursover a spanoftwoto four ho urs,a windowsize of three hour s can beused.Thealte rna tiveofusin gthe fu ll path wouldauto ma tica llysmoo thoutthesesma ll-sca le beha vior s,makin gthemdifficu lt todiscove r.Using themo vin g windowtechniq ue,itis also possibletolocate sub- beh avior s within lar ge- scalebehavio rs,filterin g outthosethat donotmatchthispatt ern.
Fina lly, temp oralfilterin g is achieve dthro ug h the plott ingofdatablock s (trips) alonga time- line.Ana lys tscan then visua llyinter pre tseq uencesofdata,makingtemp o ral patt ern s suc hasperi odici ty,seq uence,or sync hro niza tion,easie rto de tec t.Thisalso
30
allo ws ana lysts toquicklyident ifythose subsetsofthedatain whic h theymaybe interested ,filterin g out therest of thedata.
Thesethreefilteringmode s,velocity, fractaldimension ,and temp oral ,can function separate lyorinconj unctionwithoneanotherin theHSF approac h.Ana lystscan select filter sett ingsto isolatespecificbehavio rs,based ontheveloc ityandcomplexity charac te risticsinhere nt tothepatte rns that make upthosebehaviors. Mult iplebehaviora l signat urescan beused simultaneo usly,to explorepheno me nasuchastransitions from one behaviortothe othe r, behavioral anisotropy,or relatedbeha vior s.
2.5. Prototypesystem
Theabove Hyb rid Spat io-te rnporalFilteri ng(HSF)approach has been implem ent ed withinaprototype geovisualanalyticssystem, usin g fishe riesenforcement as a case studyinorde r totest itsfeasibilityandeffective ness .As aresul t, the prototype focusedon VesselMonit oring System(VMS) dataprodu cedbyfishin g vessels.The prototypeincludestwomajorcomp onent s:thegeo visualizatio ncomponent,and the interactiv ehybridfilterin g component, integrat edthroughmultiple coordinatedviews (Figure2.2) .Thegeovisualizatio ncomponent represent sthe target movem entpath s and includ es a featurewhere inellipsescanbedispl ayed for eachsetofdatapo intstoindicate aprob abilit y of incur sioninto zonesofintere st.HSFintegratesthreetypes of filtering (velocity,fractal,and temp oral), andallowsfor the creationof reloadablesignatures based on thevelocit yand fract aldimensionfilter settings.
Multiple coord inatedviews (Wang Bald onado etaI.,2000) areusedto allow changesinoneofthese compo nents tobe auto matica lly reflectedinallothe rs.This
31
effectivel yenabl esuser sto invest igate multipleaspec tsoftheirmo vementdata setat the same time,whichisben efici alnot onlyfor the ana lys isofthedata,butalso duringthe construct ionof filterin g signaturesfor pattern s ofinterest.
-r;mporal
Filtering
MUltiple coordinated views Spaual
(WoMdWin d)
Figure 2.2. Complex IiItering based on velocity,fractal,andtemporal propertiesisused to isolate patterns;multiple coordinated viewsof the data support theiterative visualexploration of the data
byanalysts.
2.5.1. Geovisualization component
Thegeovisua lizat ioncompone nt used as thefoundat ionoftheHS F syste m is based on theJava vers ionofNASAWorldWind, anopen-so urce3D mappin g system sim ila r toGoogle Earth(Figure2.3). Thissyste mallowsforthesta nda rdview manipulati on operationsava ilablein mostthree-dim en sionalmappin g softwa re,suc has pan ,tilt,andzoom. Addition all y,HSF extends this syste m to supportanumberofothe r modesof interaction , suc h asprovidinghover-b aseddetail s on demandregardinga particulartar get , and thehighli ghtin g of completetargetpaths via waypoint select ion.
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Figu re 2.3. WorldWind- basedvirt ua lglobedisplayingamon tb of fishingvessel movement pathsin EasternCa nada.
The datapointsthemselves are displayedusing chevron-shapedglyphs for each record (Figure2.4).Chevrons were chosensince theyimplythedirectionality of movement, which helpsusersunderstandthe temporalflow withinadata set.Thesetof chevrons representingthepath of onevesselare linked usinglines,toprovide analystsa generalideaofthe areas potentiallytravelledbythe vessel.
Duetothetemporalresolution of the VMSdataused inour case study(one hour), simplestraight linesproduceasignificanterrorcomponent(Bertrand etal.,2007;
Tremblayet al.,2006).As aresult, acubic Hermite spline interpolationmethod wasused, whichgeneratessmooth curves of higheraccuracy, andareassured to gothroughevery actual data point (Tremblay et al., 2006). Anaccuracy assessmentwasrunon bothlinear andcubic Hermite spline interpolation forourspecific data,toverifythattheresults were consistentwith thosepresented by Tremblayetal. (2006). in thatcubic Hermitespline interpolation significantlydecreasesthe errorofthepath estimation.Thesecondary
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benefit of usingthis splineinterpolation techniqueisthat curved paths areeasyforthe humaneyetofollow.leadingtoless cognitivestrainon the users(Ware,2004).
Boththe chevronsand thelines are filled witha solidyellow color.Yellowwas chosen primarilyduetoitshighcontrastwith the blue background colorof thewater.
This use ofyellowonabluebackgroundcanreadilybepre-attentivelyprocessedby viewers.allowingthepathstobeidentified withoutconscious attention (Hering,1964;
Ware, 2004). Whencompletepaths areselectedforhighlighting,these arecolored in cyan,whichprovidesachromatic contrastwiththeyellowpaths,aswellas aluminance contrastwith theblue background.
Figure 2.4.Exa mpleof fishin gvesselpath:theyellow curvesrepresent theinterpolatedvessel trac ks and the chevronsindicat etheposition ofcollected datapoint saswellasthedirectionof movement.
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Thegeovisualizationcomponentimplementedincludesanoptionofdisplaying semi-transparentellipses(Figure2.5) estimatingthepotential area travelledby a vessel between two consecutivedata points.Dueto theknown temporal resolutionofthe data and themaximum velocityof each vessel, it is possibleto estimate themaximum spatial areacovered by a vessel.Thisvisual tool canthen beusedto inform the analystasto whetherit is possiblefor a vessel to have enteredintoa particularzone.In the case of this study,weused zones that areclosed to fishing,and therefore where most fishingvessels should not venture.Theellipsesarecalculated by finding themaximumrecorded vessel velocity foreach individual vesselwithin thedata set.The maximumrecorded vessel velocity isthenincreasedby10%,to accountfor abroadrange of factors, suchastail winds,a calmsea, ormaximum throttle,that could provide ahighermaximum velocity than thedatawouldsuggest.Thisprovides a conservative estimateofthe true maximum velocityofavessel, sincethe exact figureis generally unavailable.
Figure 2.5. Ellipsesshowingavessel track withalowprobabilit y of zonalincursion. shown bythe opacityoftheellipses andth eir am ount of overl apwitha specific zone (sh owllhereillgre y).
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Thisvelocityisthenusedto calculate the maximum distancethat could be travelled within the spanof timebetween each pairofdatapoint s. The ratio oft he distancebetween eac h pair of datapoint s and themaximumdistancethat could be travelleddetermin ethe sizesofthe semi-majorandsemi-minoraxes ofthe ellipses, using themethoddescr ibedby Formula 1-3 andFigure2 inMillset al.(2006).In otherwords, thefartherapart twodatapo ints,the closerthe vesselisto itsmaximum velocity,and thereforethe sma lleritssemi-minoraxis.
Oncethe ellipsesarecalculated, the areaof spatialoverla pof eachellipseonto each zone iscalcul ated .Theratio ofthis area tothetotal areaofeachellipsegivesa probabil ity ofincur sioninto each part icu lar zone.For the spec ialcase whereeitherofthe datapoints is actuallywithina zone,theprobabilityof incu rsionis set to100%.These incursion probabilities arerepresentedin thegeovisualizationview using a slightly darker color thanthepathline. Thisallows the visualassociationof eachellipseto each path, whileallowingfor increased contrast betweenthe chevrons, paths, andellipses.Finally, the opacityofeachellipse is set totheirpercentprobabil ity ofincursion,with multiple overlappingellipsesadd ing their opac ities(Figure2.5).
2.5.2. Interactive filtering
Velocit yfilter ing wasimplemented asaseparate componentin ourprototype system.Itsupports the visua lizationofthedistribu tion of vesselvelocitieswithinadata set, through theuse of ahistogram , as wellasfiltering of databased ona velocityrange.
Such inform ation can be interestingfor the ana lysts,asfishing vesselswill display specificrangesof speedfor different activities(e.g.stea ming to fishinggrounds,
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