Kohei Arai Editor
Intelligent Computing
Proceedings of the 2022 Computing Conference, Volume 1
LectureNotesinNetworksandSystems
Volume506
SeriesEditor
JanuszKacprzyk,SystemsResearchInstitute,PolishAcademyofSciences, Warsaw,Poland
AdvisoryEditors
FernandoGomide,DepartmentofComputerEngineeringandAutomation DCA, SchoolofElectricalandComputerEngineering FEEC,UniversityofCampinas
UNICAMP,SãoPaulo,Brazil
OkyayKaynak,DepartmentofElectricalandElectronicEngineering, BogaziciUniversity,Istanbul,Turkey
DerongLiu,DepartmentofElectricalandComputerEngineering,University ofIllinoisatChicago,Chicago,USA
InstituteofAutomation,ChineseAcademyofSciences,Beijing,China
WitoldPedrycz,DepartmentofElectricalandComputerEngineering,Universityof Alberta,Alberta,Canada
SystemsResearchInstitute,PolishAcademyofSciences,Warsaw,Poland
MariosM.Polycarpou,DepartmentofElectricalandComputerEngineering, KIOSResearchCenterforIntelligentSystemsandNetworks,UniversityofCyprus, Nicosia,Cyprus
ImreJ.Rudas, ÓbudaUniversity,Budapest,Hungary
JunWang,DepartmentofComputerScience,CityUniversityofHongKong, Kowloon,HongKong
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KoheiArai Editor
Editor KoheiArai
SagaUniversity
Saga,Japan
ISSN2367-3370ISSN2367-3389(electronic) LectureNotesinNetworksandSystems
ISBN978-3-031-10460-2ISBN978-3-031-10461-9(eBook) https://doi.org/10.1007/978-3-031-10461-9
© TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicense toSpringerNatureSwitzerlandAG2022
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Editor ’sPreface
Thiseditionoftheproceedingsseries, “IntelligentComputing:Proceedingsofthe 2022ComputingConference” containspaperspresentedattheComputing Conference2022,heldvirtuallyonthe14thand15thofJuly2022.Weare delightedtoannouncethatthecompleteconferenceproceedingsweresuccessfully executedthroughthewillandco-operationofallitsorganizers,hosts,participants andallothercontributors.
Theconferenceisheldeveryyearsince2013,withanaimtoprovideanideal platformforresearcherstoexchangeideas,discussonresearchresultsandpresent practicalandtheoreticalapplicationsinareas,suchastechnologytrends,computing,artificialintelligence,machinevision,security,communication,ambient intelligenceande-learning.Theproceedingsof2022conferencehasbeendivided intotwovolumeswhichcoverawiderangeofabovementionedconferencetopics. ThisyearComputingConferencereceivedatotalof498papersfromaroundthe globe,outofwhichonly179paperswereselectedtobepublishedintheproceedingsforthisedition.Allthepublishedpaperspassedthedouble-blindreview processbyaninternationalpanelofatleastthreeinternationalexpertreferees,and thedecisionsweretakenbasedontheresearchquality.Weareverypleasedto reportthatthequalityofthesubmissionsthisyearturnedouttobeveryhigh.
Theconferencebringsasingle-tracksessionscoveringresearchpapers,posters, videosfollowedwithkeynotetalksbyexpertstostimulatesigni ficantcontemplationanddiscussions.Moreover,allauthorshadveryprofessionallypresentedtheir researchpaperswhichwereviewedbyalargeinternationalaudienceonline.Weare confidentthatalltheparticipantsandtheinterestedreadersbenefitscientifi cally fromthisbookandwillhavesignificantimpacttotheresearchcommunityinthe longerterm.
Acknowledgmentgoestothekeynotespeakersforsharingtheirknowledgeand expertisewithus.Abigthankstothesessionchairsandthemembersofthe technicalprogramcommitteefortheirdetailedandconstructivecommentswhich
werevaluablefortheauthorstocontinueimprovingtheirpapers.Wearealso indebtedtotheorganizingcommitteefortheirinvaluableassistancetoensurethe conferencecomesoutinsuchagreatsuccess.
WeexpectthattheComputingConference2023willbeasstimulatingasthis mostrecentonewas.
KoheiArai
EstimationofVelocityFieldinNarrowOpenChannelsbyaHybrid MetaheuristicANFISNetwork 1 HosseinBonakdari,HamedAzimi,IsaEbtehaj,BahramGharabaghi, AliJamali,andSeyedHamedAshrafTalesh
DevelopmentofaLanguageExtensionforCon figuration ofIndustrialAssetCapabilitiesinSelf-organized ProductionSystems ........................................
25 EricBrandt,FelixBrandt,andDirkReichelt
Open-SourceMappingMethodAppliedtoThermalImagery ........
43 André Vong,JoãoP.Matos-Carvalho,DárioPedro,SlavisaTomic, MarkoBeko,FábioAzevedo,SérgioD.Correia,andAndré Mora
ScalableComputingThroughReusability:Encapsulation, Speci fication,andVerificationforaNavigableTreePosition 58 NicodemusM.J.Mbwambo,Yu-ShanSun,JoanKrone, andMuraliSitaraman
GeneralizingUnivariatePredictiveMeanMatchingtoImpute MultipleVariablesSimultaneously 75 MingyangCai,StefvanBuuren,andGerkoVink
TimelineBranchingMethodforSocialSystemsMonitoringand Simulation ............................................... 92 AntonIvaschenko,EvgeniyaDodonova,IrinaDubinina,PavelSitnikov, andOlegGolovnin
WebometricNetworkAnalysisofCybersecurityCooperation ........ 103 EmmanouilKoulas,SyedIftikharHussainShah,andVassiliosPeristeras
SafetyInstrumentedSystemDesignPhilosophyParadigmShiftto AchieveSafeOperationsofInterconnectedOperatingSites 123 SolomanM.AlmadiandPedroMujica
BifurcationRevisitedTowardsInterdisciplinaryApplicability 138 BernhardHeiden,BiancaTonino-Heiden,andVolodymyrAlieksieiev
CuriousPropertiesofLatencyDistributions 146 Michał J.Gajda
MulticloudAPIBindingGenerationfromDocumentation ........... 171 GabrielAraujo,VitorVitaliBarrozzi,andMichał J.Gajda
ReducingWeb-LatencyinCloudOperatingSystemstoSimplifyUser TransitionstotheCloud ..................................... 178 LukeGassmannandAbuAlam
Crescoware:AContainer-BasedGatewayforHPCandAI ApplicationsintheENEAGRIDInfrastructure 196 AngeloMariano,GiulioD’Amato,GiovanniFormisano,GuidoGuarnieri, GiuseppeSantomauro,andSilvioMigliori
Signi ficanceinMarloDiagramsVersusThoroughnessofVenn Diagrams 207 MarcosBautistaLópezAznar,GuillermoCímboraAcosta, andWalterFedericoGadea
AHybridReal-TimeSchedulingMechanismBasedonMultiprocessor forReal-TimeTasksinWeaklyHardSpeci fication ................ 228 HabibahIsmail,DayangN.A.Jawawi,andIsmailAhmedy SimulatingtheArnaoutova-KleinmanModelofTubularFormation atAngiogenesisEventsThroughClassicalElectrodynamics .......... 248 HuberNieto-Chaupis
VirtualCriticalCareUnit(VCCU):APowerfulSimulator fore-Learning 255 FredericBanville,Andree-AnneParent,MyleneTrepanier, andDanielMilhomme
SilenceinDialogue:AProposalandPrototypeforPsychotherapy 266 AlfonsoGarcés-BáezandAurelioLópez-L ópez
AStandardContentforUniversityWebsitesUsingHeuristic Evaluation 278 Mohd.HisyamuddinJainari,AslinaBaharum,FarhanaDianaDeris, NoorsidiAizuddinMatNoor,RozitaIsmail,andNurulHidayahMatZain DevelopmentofaMobileApplicationtoProvideEmployment InformationtoPrivate-SectorWorkersinPeru ................... 293 PaulCcunoCarlos,PabelChuraChambi,José LipaOchoa, andJosé Sulla-Torres
AnalysisofTechnicalFactorsinInteractiveMediaArts,withaFocus onPrixArsElectronicaAwardWinners 304 YeeunJoandUranOh
UsabilityEvaluationofMobileApplicationSoftwareMockups 321 FrayL.Becerra-Suarez,DeysiVillanueva-Ruiz, VíctorA.Tuesta-Monteza,andHeberI.Mejia-Cabrera
EasyChat:AChatApplicationforDeaf/DumbPeople toCommunicatewiththeGeneralCommunity ...................
W.W.G.P.A.Wijenayake,M.D.S.S.Gunathilake,P.M.Gurusinghe, W.A.H.K.Samararathne,andDisniSriyaratna
InfluenceofAugmentedRealityonPurchaseIntention ............. 345 AnaZagorcandAndrijaBernik
EncounteringPinchasGutterinVirtualRealityandasa “Hologram”: ImmersiveTechnologiesandOneSurvivor ’sStoryoftheHolocaust 358 CayoGamber
StridedDMAforMultidimensionalArrayCopyandTranspose 375 MarkGlines,PeterPirgov,LenoreMullin,andRishiKhan
TheMachineLearningPrinciplesBasedattheQuantumMechanics Postulates ................................................
HuberNieto-Chaupis
TheThreatofQuantumComputingtoSMEs ....................
PaulinaSchindlerandJohannesRuhland
QuantumComputationbyMeansofJosephsonJunctionsMade ofCoherentDomainsofLiquidWater 414 LuigiMaxmilianCaligiuri
CustomerResponseModelingUsingEnsembleofBalanced Classi fiers:Signi ficanceofWebMetrics 433 SunčicaRogić andLjiljanaKašćelan
WhatAugmentationsareSensitivetoHyper-ParametersandWhy?
ChMuhammadAwais,ImadEddineIbrahimBekkouch, andAdilMehmoodKhan
Draw-n-Replace:ANovelInteractionTechniqueforRapidHumanCorrectionofAISemanticSegmentation ........................
KevinHuang,Ting-JuChen,ShashankShekhar,andJiEunKim WorkingTowardsanAI-BasedClusteringofAirports,intheEffort ofImprovingHumanitarianDisasterPreparedness ................
MariaBrowarskaandKarlaSaldañaOchoa
WindTurbineSurfaceDefectDetectionAnalysisfromUAVs UsingU-NetArchitecture 499 HassnaaHasanShaheedandRiyaAggarwal
AnomalyDetectionUsingDeepLearningandBigDataAnalytics fortheInsiderThreatPlatform 512 AbuAlamandHarryBarron
NearInfraredSpectraDataAnalysisbyUsingMachineLearning Algorithms ............................................... 532 PerryXiaoandDaqingChen
OnRegretBoundsforContinualSingle-IndexLearning ............ 545 T.TienMai
TexttoImageSynthesisUsingStackedConditionalVariational AutoencodersandConditionalGenerativeAdversarialNetworks 560 HaileleolTibebu,AadinMalik,andVarunaDeSilva
AnalyticalDecision-MakingSystemBasedontheAnalysisofAir PollutionintheCityofNur-Sultan 581 ZhibekSarsenova,AldiyarSalkenov,AsselSmaiyl, andMirolimSaidakhmatov
ALocalGeometryofHyperedgesinHypergraphs,andIts ApplicationstoSocialNetworks ............................... 590 DongQuanNgocNguyenandLinXing
ExtractionofConsumerEmotionUsingDiaryDataonPurchasing Behavior ................................................. 608
YuzukiKitajima,ShuntaNakao,KoheiOtake,andTakashiNamatame Signi ficanceinMachineLearningandDataAnalyticsTechniqueson OceanographyData 620
K.Krzak,O.Abuomar,andD.Fribance
ApplyingLatentDirichletAllocationTechniquetoClassifyTopicson SustainabilityUsingArabicText 630 IslamAlQudah,IbrahimHashem,AbdelazizSoufyane,WeisiChen, andTarekMerabtene
MetricsforSoftwareProcessQualityAssessmentintheLatePhases ofSDLC ................................................. 639 GcinizweDlamini,ShokhistaErgasheva,ZamiraKholmatova, ArtemKruglov,AndreySadovykh,GiancarloSucci,AntonTimchenko, XavierVasquez,andEvgenyZouev
OnlineQuantitativeResearchMethodology:Refl ectionsonGood PracticesandFuturePerspectives 656
PierpaoloLimone,GiusiAntoniaToto,PiergiorgioGuarini, andMarcodiFuria
ApplicationofMachineLearninginPredictingtheImpactofAir PollutiononBacterialFlora .................................. 670
DamjanJovanovski,ElenaMitreskaJovanovska,KatjaPopovska, andAndrejaNaumoski
MarkovChainsforHighFrequencyStockTradingStrategies ........ 681 CesarC.Almiñana
ScalableShapeoidRecognitiononMultivariateDataStreams withApacheBeam 695 AthanasiosTsitsipas,GeorgEisenhart,DanielSeybold,andStefanWesner
DetectionofCreditCardFraudswithMachineLearningSolutions: AnExperimentalApproach 715 CourageMabani,NikolaosChristou,andSergeyKatkov
ALBU:AnApproximateLoopyBeliefMessagePassingAlgorithm forLDAforSmallDataSets 723 RebeccaM.C.TaylorandJohanA.duPreez
RetrospectiveAnalysisofGlobalCarbonDioxideEmissions andEnergyConsumption .................................... 747 RajvirThindandLakshmiBabuSaheer
ApplicationofWeightedCo-expressiveAnalysistoProductivityand Coping .................................................. 762 SipovskayaYanaIvanovna
AnImprovedArchitectureofGroupMethodofDataHandlingfor StabilityEvaluationofCross-sectionalBankonAlluvialThreshold Channels 769 HosseinBonakdari,AzadehGholami,IsaEbtehaj, andBahramGharebaghi
IncreasingImportanceofAnalogDataProcessing 797 ShuichiFukuda
NewTrendsinBigDataPro filing ............................. 808 JúliaColleoniCouto,JulianaDamasio,RafaelBordini,andDuncanRuiz
FindingStructurallySimilarObjectsBasedonData SortingMethods ........................................... 826
AlexeyMyachin
ApplicationoftheProposedThresholdingMethodforRicePaddy FieldDetectionwithRadarsat-2SARImageryData 836 KoheiAraiandKentaAzuma
UnderstandingCOVID-19VaccineReactionThroughComparative AnalysisonTwitter 846 YueshengLuoandMayankKejriwal
ADescriptiveLiteratureReviewandClassi ficationofBusiness IntelligenceandBigDataResearch ............................ 865 AmmarRashidandMuhammadMahboobKhurshid
DataMiningSolutionsforFraudDetectioninCredit CardPayments ............................................ 880 AwaisFarooqandStasSelitskiy
NonexistenceofaUniversalAlgorithmforTravelingSalesman ProblemsinConstructiveMathematics 889 LinglongDai
Addition-BasedAlgorithmtoOvercomeCoverProblemDuring AnonymizationofTransactionalData 896 ApoChimèneMonsan,JoëlChristianAdepo,Edié CamilleN’zi, andBiTraGoore
MariaBrowarska1(B) andKarlaSalda˜naOchoa2
1 DelftUniversityofTechnology,Delft,TheNetherlands maria.browarska@gmail.com
2 SchoolofArchitecture,CollegeofDesign,ConstructionandPlanning, UniversityofFlorida,Gainesville,USA ksaldanaochoa@ufl.edu
Abstract. Inrecentyears,naturaldisastershaveincreasedinfrequency, causingsignificantdamagetocommunitiesandinfrastructureworldwide. Whenanaturaldisasterstrikes,airportsintheaffectedregionhaveto adaptquicklyfromservingregularpassengerstobecomingahumanitarianhubhandlingamassiveincreaseinpassengersandcargo.Several countriesareparticularlyvulnerableandpronetosuchadevastating event.Althoughexistinginitiativesaimtoraiseawarenessandimprove airportpreparedness,authoritiesareoftenisolatedintheirresilience effortsastheytendtoactindividually,andtheirresponseisoftenbound bylocalexperience.Consequently,thisresearchaimstobroadenthe fieldofviewfromalocaltoaglobalonebycompilingadatabaseof 971airportsworldwidewithcorrespondingsocio-technicalcharacteristicsinvariousdatamodalities.Inaddition,throughadatascienceapproach,atransformationofthedifferentdatamodalitieswasperformedto extractnumericalfeaturevectorssothatinfuturestudiesacorrelation betweenairportscanbefound,tofindsimilarairportsfromwhichdifferentapproachestodisasterpreparednessandresponsecanbelearned.
Keywords: Airportsdatabase · Disasterpreparedness · AI-based Clustering
1Introduction
Whenanaturaldisasterstrikes,thenearestairportbecomesthecriticallinkfor deliveringandorganizingreliefaidwhiletryingtostayefficientinevacuating citizensandreceivingemergencypersonnel[4].However,theexistinginfrastructureoftencannothandlethesuddenspikeinthevolumeofincominggoods[5]. Whenairportsbecomenonoperational,theonlywaytoreceivevaluableaidisvia road,rail,andwater,whichisoftenmuchlessefficientandtime-consuming[17]. Eventhoughdisastersandhumanitarianaidarenotthenewestchallenges, thereisstillmuchroomforimprovement.Airportsareacomplexsocio-technical
c TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2022 K.Arai(Ed.):SAI2022,LNNS506,pp.483–498,2022. https://doi.org/10.1007/978-3-031-10461-9 33
challenge,astheyaresetinanenvironmentoftechnicalandoperationalchallenges,lawsandregulations,internationalandregionalcooperationofstakeholdersfromvariousfieldsimprovinghumanitarianlogistics.Tocharacterizean airport,weneedtoconsidervariousfeaturesthatdescribetheircomplexity,a) geospatialandairport-specificdata:areasurrounding,reachability,numberof runways,taxiways;b)demographicdata:urbanindexes,andpopulationaround theairport;andc)geographicandurbandata:seaportdataandbuiltenvironmentinformation.Creatingsuchadatabasecanhelpexpertsturnthosedata pointsintovaluableinsights.
Thus,thisresearchexploreshowdatasciencecouldhelpestablishabase forformingcollaborationsbetweenairportsthatmightfacesimilarchallenges indisasterpreparednessefforts.Thegoalistobuildacomprehensivedatabase describingairportsfromtheperspectiveoftheirdisasterpreparednessthatwill helpfutureresearchersfindsimilaritiesbetweenthem,basedontheirintrinsicsocio-technicalfeatures,sothatperhapsanairportinIndonesiacouldbe matchedwithitssiblingairportintheCaribbeans.Theresearchinvolvedseveralprogrammingoperations–startingwithcollectingdatauptodataprocessing. Thedatabasecanbefoundinthefollowingrepository.
https://gitlab.com/maria.browarska/OSM-SOM
Theproposeddatabaseofairportsandtheirnumericalfeaturesarethefirst steptoaprocessthatwillconcludecreatinggroup-specificpolicyadvicefor similarairports.Withthisarticle,wewanttodescribethestepsfromcollection, normalization,andpre-processingofthedatatotransformingthemultimodality ofthegathereddatatoanumericalfeaturevectorthatinfutureresearchcanbe usedforthegroupingofsimilarairportsthroughUnsupervisedMachineLearning algorithmsthatcanclustersimilarairportsbasedonsimilarnumericalfeatures. HavingarelevantscenariotoapplyMLthatbenefitssocietyatlarge.
2KnowledgeGapandResearchGoal
Inordertodefinekeyconcepts,narrowdownthescopeoftheresearchand preciselydefinetheknowledgegap,aliteraturereviewwasconducted,followed by5semi-structuredinterviewswithindustryexperts.
2.1LiteratureReview
Mostofthereviewedarticlesfocusedonacasestudyastheresearchapproach, oftenlookingatindividualairportsandassessinghistoricalevents.Researchers analysedthebehaviourofairportsinspecificdisastrousevents,mainlyfocusing onorganisationalprocessesandstakeholders’cooperation[16, 17, 22].Whileall theconsideredfeatures,withoutadoubt,influencelogisticaloperations,theyare alsouniqueforeachairport.Hence,itischallengingtodrawgeneralconclusions thatcouldapplytootherairportssincetheirorganisationalstructuremaydiffer, duetointernationalandregionalregulations,resourcesandneeds.
Someoftheauthorspointedouttheimportanceofthegeographicallocation ofanairport,structuralfeaturesaswellasreachability[3, 21, 23].Pandeyetal. [14]provedthatutilisinggeo-spacialdataisbeneficialforairporthumanitarian responseplanningandthatairportauthoritiesareinterestedintoolsthatcan helptoplanlogisticalprocedures.ChoiandHanaoka[3]developedamodelthat visualisesalayoutofahumanitarianbasebasedonstructuralfeaturesofan airportandprovesitspotentialapplicabilitywithacasestudy,suggestingthat moreresearchisneededtogeneralisetheirresults.
Whilesomeoftheauthorssuggestedthatcooperationbetweenairportsthat strugglewithsimilarchallengeswouldhaveapositiveoutcome[9, 16],noneof themexploredthepossiblebackboneofsuchcooperation.Thatfinding,combinedwiththeideaofstructuralfeaturesofairportshavinganimpactontheir humanitarianlogisticalprocedures,ledtodefiningtheknowledgegap.
Thespecificmethodsappliedinthisresearchwereusedinthefieldofhumanitarianaid-relatedresearchbefore,butonalocalornationalscale,asshownby Salda˜naOchoa,ComesandChen[2, 12].Theglobalapproachisachallengedue tothelimitedavailabilityofreliabledata,butifsuccessful,itpavestheway formoredetailedresearchonaglobalscale.Thisapproachcouldsignificantly benefitthelessdevelopedcountries,whichoftendonothaveresourcesforlocal advancedresearchandpreparednessstrategies.
Untilnow,thepractitionersinthefield,suchasGetAirportsReadyfor Disaster(GARD),haveusedstraightforwardmethodsforassessingthevulnerabilityofairportsandhadtopreparedifferentstrategiesforeachclient.GARD’s capacityisminimal,andthisresearchcouldleadtonewwaysforauthoritiesto prepare,thankstoestablishingcollaborationsdirectlywithotherairportsfacing similarchallenges.
2.2ResearchGoal
Thegoalofthisresearchisto(1)betterunderstandthechallengesthatairportsfacewhenanaturaldisasterstrikesandtheirpreparednessactivities.This understandingshallthenbe(2)translatedintoalistofsocio-technicalfeatures influencingthelevelofpreparednessandairportcapabilitiesinfacingadisaster.Thefindingofkeyfeaturesisrelevantfor(3)buildingadatabasecontaining valuablehumanitarianaid-relatedinformationaboutseveralairportsworldwide, composedsolelyfrompubliclyavailablesources.Thefocusonpubliclyavailabledataisconditionedbyalargenumberofairportsbeinganalyzed,which makesitimpossibletoconductsurveysandobtaininformationdirectlywithin theresourcesandtimeframeofthisresearch.
3Methodology
Inordertofindspecificqualitiesandfeaturesthatinfluenceairports’preparednessforadisaster,athoroughunderstandingofactivitiesandtheenvironment inwhichtheytakeplaceisneeded.Thisinformationwasderivedfromadesk
studyaccompaniedbysemi-structuredinterviews(Table 3 intheAppendixlists theorganizationcontactedforinterviewing)withexpertsonairports’disaster preparednessandperformance,summarizedinTable 1.Thenextstepwasto translateidentifiedchallengesinfluencingtheperformanceofanairportina post-disasterscenariointosocio-technicalfeaturestoachieveagoodstarting pointforthedataminingprocess.
Table1. Socio-technicalfeatures
Structuralandcapacity features
Runwaysandtheir characteristics
Aircraftparkingandits characteristic
Terminalsandtheir characteristics
Storagefacilitiesboth open-airandcovered warehouses
Accessibilityfeatures
Airportconnection
Geographicalsurroundings
Alternativeairportsandseaports
Organisationalfeatures
Howmuchstaff isavailable
Riskrelatedfeatures
Riskofoccurrenceofa naturaldisaster
Howwellthestaff istrained Regionalcapacityfor handlingdisasters
Whoownstheairport
Whatistheairport’smain Purpose(civil/military)
Whethertheairportwaspart ofanypreparednessprograms
Thedataminingprocesswascomposedoftwomainiterativephases.First, theidentifiedsocio-technicalfeaturesofairportshadtobetranslatedintomeasurabledatapoints–numerical,categorical,ordescriptive.Thesecondphase wasretrievingdatafrompubliclyavailablesources,asdescribedinmoredetail inFig. 2.Whenbuildingadatabasefrompubliclyavailablesources,itiscrucialtohaveastrongunderstandingofwhatwewanttodescribetoallowfor flexibilityandeasyreplacementoradjustmentoforiginallyplannedmeasures. Forfutureresearch,thedataminingprocesscouldbereplacedbyconducting detailedsurveyswithairports.Withsuchsurveys,itwouldbepossibletoobtain theexactmeasurestoaccountforallplannedfeaturesstraightfromthesource, allowingforbetteraccuracyandtrustworthiness.
Tostartbuildingthedatabase,wechoosevulnerablecountriesandairports usingtheINFORMRiskIndexasqualificationcriteriaforchoosing.First,a listofallairportsthatarelocatedwithinthesecountrieswasexported.Next, theairports.csvfilefromOurAirportswasusedtoselectonlyairportscurrently operating,i.e.,havescheduledservices.Anadditionalcriterionwastheairport type-heliports,seaplanebases,andclosedoneswereexcluded,whilesmall, medium,andlargewerechosen.Theseoperationsresultedinformingalistof971 airports,withtheirnames,coordinates,InternationalAirTransportAssociation (IATA)codes,andInternationalCivilAviationOrganization(ICAO)codes.This listwouldformthebaseforallmassqueriesappliedviaAPIstocollectdatafor eachairport.Figure 1 presentsthe971airportsonaWorldmap.
4BuildingtheDatabase
Datausedinthisresearchcamefromamultiplicityofsourcesinvariousdata modalitiesandformats.Inordertotranslatesocio-technicalintocomparable
Fig.1. 971airportschosentobeanalyzed,placedonaworldmap
setsofnumericalfeatures,variouscircumstancesneedtobetakenintoaccount, suchasavailabilityofdata,methodsofmeasuringandquantifyingspecificcharacteristics,theircorrelations,andlevelofimportance.Inordertokeeptrackof changesandmakethedatabaseeasytonavigate,theSQLitedatabasewasbuilt withtheuseofDBBrowsersoftware.TheOSMqueries,theGeoDB-citiesAPI wereconnectedtothedatabasethroughPythonqueries,asseenintheattached GitLabrepository.Toaddrecordsandfeaturestothedatabase,outputsfrom varioussourceswereconvertedintothe.csvformat.ResultsofOpenStreetMap (OSM)andAPIquerieswereautomaticallywrittenintothedatabasedirectly.
4.1DataSources
OSM. InordertoextractdatafromOSM,Overpassturbowasused-awebbaseddataminingtool,designedtorunOSMAPIqueriesandpresentthemon amap.Sincedataneededtobeextractedforover900airports,multiplescripts werewritten,withtheuseoftheOverPyAPI,publishedundertheMITlicense [10].Adetaileddocumentationofthescriptsandqueriescanbefoundinthe attachedGitLabrepository.
OurAirports. OurAirportsisafreeandpublicservicethatmaintainsdata aboutairportsaroundtheworld.SimilarlytoOSM,itisrunbyvolunteersmemberscreaterecordsindividually-butatthesametimemuchoftheinformationcomesfromofficialgovernmentalinstitutionssuchastheU.S.Federal AviationAdministration[13].Inadditionfromexploringanonlineinteractive map-basedtool,userscanalsodownloaddailyupdatedfileswithdatarecordsof allairportsthatarepartoftheservice.Forthisresearch,datasetofallairports andrunwayswasused.
GlobalAirports. Themostcomprehensive,publiclyavailable,datasetaimed atprovidinginformationondisasterlogisticsiscalled Globalairports andwas publishedbytheHumanitarianDataservice[7].Officiallycoordinatedbythe WorldFoodProgramme,basedonopenlyavailabledatafromsourcessuchas OSMandOurAirports,italsocontainsinputsfrompartnersthoughtheLogistics ClusterandLogisticsCapacityAssessments[7].Eventhoughthedatasetis updated,accordingtoaWFPrepresentativeinterviewed,formanyplacesthe datahasnotbeencheckedsincetheoriginaluploadin2013.Furthermore,the datasetcontainsfairlybasicinformationonairports.Datapointspresentedin thetablearenotavailableforeveryairportintheset.
TheLogisticsPerformanceIndex. TheLogisticsPerformanceIndex(LPI) providesinformationonhoweasyordifficultitistotransportgoodsintheanalysedcountries.TheWorldBank,togetherwithvariouslogistics-relatedpartner organisationsconductsthesurveyeverytwoyears[1].Whileaimedatassessingthelogisticalcapacityinthecontextoftradeandmerchandise,someof theindicatorsarerelevantforhumanitarianlogistics,suchastheoneschosen tobeincludedinthisresearch:theassessmentofcustomsproceduresandthe assessmentofgeneralqualityoftradeandtransportrelatedinfrastructure.
TheINFORMRiskIndex. LedbytheEuropeanCommission,INFORM isaglobal,open-sourcedriskindexforhumanitariandisastersandcrises,that describesthreedimensions:hazard&exposure,vulnerabilityandlackofcoping capacities.Inadditiontobeingthequalificationcriteriaforthefinalairport database,partsoftheINFORMRiskindexwerealsousedtocharacterizeairports.
4.2ExtractingData
AirportSurroundings. TwostrategiesinOSMweretestedinordertoasses thesurroundingsofeachairport.First,the“landuse”tagwasexplored-all thenodescontaininginformationonthelandusewithin5kmradiusfromeach airportwereextracted.However,thisledtoinconsistentresults-visualvalidationofmultiplequeryoutputswasconductedanditledtoaconclusionthat buildings-relatednodesarehighlyoverrepresentedascomparedtofieldsorother unusedspaces.Therefore,formanyairports,theresultonlyshowedanumber ofbuildingswithinthatradius,andnoinformationdescribingtheemptyfields thatwerethetruedominantsurrounding.
Thesecondstrategy,whichledtomorerepresentativeresults,wasonebased onpurelythenumberofnodeswiththetag“building”.Theassumptionwasthat ifthebuildingsarewelltaggedinOSM,simplythenumberofthosenodeswithin theradiuswoulddescribehowdenselybuiltthesurroundingoftheairportis. Thelowerthenumberofbuildingsaround-themoreusefulspacefororganising humanitarianaid.Avisualvalidationofmultiplerecordswasconducted,with aspecialfocusontheoutliers-airportswithveryloworveryhighnumberof
buildingsaround.Thesurroundingsofsomeremoteairportswasunderrepresented,resultingin0buildingsreported.Whileitwasnottrue,thenumberof buildingswasverylittleandtheresultwasstilluseful.
AlternativeAirports.
Tofindandalternativeairport,wefocusedonthe surroundingswithina100kmradius.Unlikewithchoosingairportsforthemain database,withalternativeonestherewasnoexclusionofthosethataresmaller ordonothaveanIATAcode.Theassumptionwasthatanykindofairportwithin aclosevicinitytothemainonemightworkasasupportingspace,evenifnotfor landingthesamesizeofairplanes,butperhapsstorageandotherhumanitarian operations.SinceairportsarewelltaggedinOSM,thevalidationofresultswas positive-therewerenooverlookedairportsfound.However,dependingonthe qualityanddensityofroads,anairportwithin100kmradiusmightinfactbe manyhoursaway,whichwouldnotbeauseful alternative.Infutureresearchit isworthconsideringfindingamoreaccuratequalifyingfeaturethantheradius.
AlternativeSeaports.
Similarlytoalternativeairports,alternativeseaports wereinspectedwithinaradiusof100km.Vastmajorityofresultsshowed0 seaportsandthatwasvalidatedthoroughlyandresultedtobetrue.Validation wasalsoconductedforahighnumberofseaportscounted-forsome,thecounted resultswashigherthantheactualnumberofports,becauseofmultipletags withinthesameseaport.Itdidhoweverindicatethesizeoftheseaport-often thenodeswereindicatingmoreseaportterminalsorstoragefacilities.Giventhe smallnumberofrecordsthatindicatedseaportsatall,allresultshigherthan0 werevalidatedandmanuallycorrectedifneeded.
TourismVs.Industry.
Inordertoasseshowwellanairportisequippedto handleasuddeninfluxofcargohandlingandnotonlyagrowthinpassenger turnaround,itwasdecidedthatitcanbeassessedbythesurroundingofan airport.BasedontheinsightsfromtheinterviewwithChrisWeeksofGARD,it wasdeterminedthatairportsthataresituatedinmainlytouristicdestinations arelesslikelytohaveagoodcapacityforhandlingcargo.Therefore,foreach airporttheamountofnodestaggedas“industrial”and“tourismamenities”was calculated.Inordertoaccountforover/underrepresentationofcertainregions, aratiooftourismandindustryrelatedfacilitiesiscalculated-basedonthe assumptionthatiftheregionisunder/overrepresentedinOSM,itwillhappen forbothtypesofamenities.
Runways. Thenumberofrunwayswascalculatedforeachairportbycountingthenumberofnodes/ways/relationswitha“runway”tag.Alloutlierswere manuallyvalidated-thosethatresultedin0runwayswerecorrectedsincea functioningairportcannothave0runways.Thesamewasdoneforallrecords thatshowedmorethantworunwayssinceitisnotverycommonforairportsto havemultiplerunways,especiallyinremoteplaces,whichhappenstobewhere mostoftheairportsfromthedatabaseare.
CitiesandDistances. Inordertoasseshowdistantanairportisfromthe populationitmightbeservingwhenadisasterstrikes,threeclosestcitiesfor eachrecordwerefound,togetherwiththedirectdistance(notbyroad)and populationofeachcity.Forthispurpose,theGeoDB-citiesAPIwasused[11]. Basedonthecoordinatesofeachairportthethreeclosestcitieswithin100km, containingpopulationinformationwerechosen.Validationwasperformedfor anumberofrandomlychosenrecordsandoutliers,andmanuallycorrectedif needed.TheAPIworkswithGeoNamesandWikiData,whichsimilarlytoOSM areconsideredtrustworthysources,thankstotheusercommunityinputand validationscheme.
Population. Datagatheredtodescribesurroundingcitieswasusedtocalculate thegeneralpopulationaroundeachairport-asasummationofpopulationin allthreeclosestcitiesfoundbytheGeoDBcitiesAPI.
AirportArea. Inordertoassessthestoragecapacityaswellastheareaavailableforsettingupahumanitarianhub,theareaofeachairportwascalculated. InOSM,eachairportisnotonlyindicatedbyasinglenode,butbyarelation thatindicatesitsborders.Thisgeodatawasexportedandanalysedwiththe QGISsoftware[18].Thankstobuiltinfeatures,theareaofeachairportwas calculated.Validationwasconductedonarandomsampleofresultsandthe methodprovedtobeeffective.
4.3TheDatabase
Astheplanistocompareairportsbasedonnumericalfeatures,eachdata modalitywasturnedintoan understandable formformathematicalprocessing. Dependingonthemodalityofdata,variouspreprocessingmethodswereapplied, basedonseveralscientificsources[6, 8, 19, 20]andcanbeseeninAppendix2.The finallistofallairportsandcorrespondingfeatureswerebuiltintheDBBrowser andmadeavailablethroughtheGitLabdepository,bothasa.csvfileandan SQLitedatabase.Featuresselectedforeachairport,togetherwiththecorrespondingsource,preprocessingmethods,andadescriptionoftheirrelevancefor assessingdisasterpreparedness,arepresentedinTable 2.
5Limitations
Thequalitydatasourcesusedintheresearchcansometimesbecontested,as thelevelofdetailavailableforvariousairportsandtheirsurroundingswasnot alwaysequal,whichmayleadtoinaccurateresults.Thisisalsoaproblemwith officialsourceswidelyusedbythehumanitariancommunity,suchastheLogisticsCapacityAssessment.Intervieweesmentioned(Appendix1)theimportance ofaccesstodynamicdatathatdescribesthestateofeachairportanditssurroundingsataprecisemomentintime,afteradisasterstrikes,becausethestatic
Table2. Descriptionofthedatabase.
informationgatheredinassessmentsearliercanbeinaccuratethemomentadisasterstrikes.However,intervieweesinvolvedinpreparednessprogramsrather thanimmediateresponseoperationsunderlinedtheimportanceofbuildingcomprehensivedatasetswithstaticinformationtoassessbetterwhatcanbedone aheadofatragicevent.
Anotherchallengingfactoristheaccuracyofassumptionsmade–especially forassessingairportconnectivity.Asprovedbyhistoricaldisasters,theinability todistributehumanitarianrelieffromtheairporttothepopulationinneedcan underminetheairport’soperationsandpreparedness.Amoresophisticatedand accuratewayofquantifyingthelevelofconnectivitycouldbeusedinfuture research.
6DiscussionandConclusion
Thedatabasebuiltinthisresearchisavaluableresourceforfutureclustering analysisorfutureresearchrelatedtoairports’preparednessforhumanitariandisasters.Itcanbefurtheranalyzedinmoredetailedresearch,updatedaccordingly, andusedtoassessairports’venerabilityandpreparedness.Fromthescientific perspective,thisresearchprovesthattherearenowwaysofanalyzingcomplex, specificchallengeswithaglobaloverviewbasedonnumerouspubliclyavailable datasets.Italsoshowsthatscientistsneedtobeverycarefulwhenusingnot preciselyscientificsourcesandthatbuildingaspecific,tailoreddatabaseisa lengthy,challengingprocess.Nevertheless,itcanbeachievednotonlybyIT professionalsbutalsobymultidisciplinaryresearchers.
Thisresearchprovidedavaluableframeworkforapproachingcomplexsociotechnicalenvironmentsofairportsandtheirdisasterpreparedness,through buildingadatabasewithrelevantfeatures,basedoninterviewsandliterature review,usingonlypubliclyavailabledata,followedbyacomprehensivedata selection,collectionandpre-processing.Thechallengesandproblemsencounteredalongtheway,bothsolved,andunsolvedcanformavaluabletoolfor otherprofessionalsandscientistswillingtoconductsimilarresearch,notonly relatedtothedomainofaviationanddisasterpreparedness.
Anadditionalfindingisthatweidentifiedtheneedforacommon,reliable databasewithallrelevantinformationaboutairportsinvulnerablelocations. Theonedesignedduringthisresearchcouldformabaseforaonebuiltwith officialdatasourcesthatareotherwiseunavailabletothepublic.Withthat, however,comesthechallengeofsecurity;sincedetailedinformationaboutairportscanbeviewedassensitivedata,thereforeaccesstosuchadatabaseshould beregulated.
6.1FutureResearch
Theideasforfutureresearchcanbedividedintothreesections-(1)relatedto thedataminingandtheprocessofbuildingthedatabase,(2)datapre-processing andapplyinganunsupervisedclusteringalgorithmand(3)usingtheresultsin variouswaysinordertoimproveairports’disasterpreparedness.
Buildingadatabasesolelyfrompubliclyavailablesourceshassomedrawbacks,asdiscussedinSect. 5,suchaslimitedtrustworthinessandinabilityto retrievetheexacttypesofinformationthatareneededinordertodescribespecificfeatures.Inthefuture,itisworthconsideringbuildingasimilardatabase withdirectinvolvementoftheairportsthatarebeingdescribed–withtheuse ofsurveysandpossibleinvolvementofinternationalhumanitarianandaviation relatedorganisationssuchasACIorOCHA.Thiswouldallowforretrieving morespecificdata,uptodateinformation.Moreover,ifregularlyupdatedand maintained,itcouldbecomeausefulresourceforairportsthatthemselveswould liketoknowmoreaboutcapabilitiesofalternativeportsintheregion–notonly forresearchpurposes,butforoperationsonceadisasterstrikesandhelpfrom neighbouringportsisneeded.Otherscientistscouldalsousesuchadatabasefor variousadditionalanalyses,savingtimeforgatheringthedataandfocusingon whatcanbederivedfromit.
However,thedatabasethatwasbuiltinthisresearchisitselfavaluable resourceforperformingotherresearchrelatedtoairports’preparednessfor humanitariandisasters.Withadditionaliterationsofthedatapre-processing, thereisroomforgatheringinsightfulknowledgeonsimilaritiesbetweenairports,thatwouldformasolidbaseforestablishingcooperations.Inorderto achievethat,futureresearchshouldfocusonidentifyingthedominatingfeatures andadjustingthealgorithmaccordingly.Thiscouldrequiremoresophisticated methodsofdatapre-processingandautomatingtheprocessofanalysingresults, inordertoquicklypickupcombinationsoffeaturesthatcannotoffertrustworthy results.
Buildingpolicyadvicebasedonthedatabasecouldbeachievedbyidentifying airportsthatareespeciallyvulnerable,duetotheirintrinsicfeaturesandcapabilities.Thisprocesswouldhavetobeaccompaniedbyathoroughanalysisof historicaleventsthattookplaceatsimilarairports,andthelessonslearnedcould beusedforimprovingpreparednessofthosethatmightfacesimilarchallenges inthefuture,leadingtoachievingthefullpotentialofthisresearch.
AAppendix1
Fig.2. Processflowofdatamining.
BAppendix2
Table3. Affiliationofinterviewees
Interviewee Organisation
ChrisWeeks GARD
VirginieBohl OCHA,IMPACCTworkinggroup
ThomasRomig ACI
CAppendix3
DDataPre-processing
Inorderforairportstobecomparablefortheunsupervisedmachinelearning algorithms,thefeaturesthataredescribingthemneedtobeturnedintoan understandable formformathematicalprocessing.
Inthissection,thepre-processingoftext,categoricalandnumericalfeatures isdescribed.
D.1Emptyfields
Duetothefactthatvariousdatasourceswereused,therewasanumberof emptyfieldsforsomefeatures.Dependingonthefeature,theseemptyfields werefilledeitherwithzeroesorthemeanvalueofallexistingrecords.Missing fieldsinfeaturesdescribingwhethertherunwayislightedandwhetherthere wasaGARDtrainingconductedbefore,asitwasdecidedthatifthereisno informationavailable,itissafertoassumethenegativeoutcome.Theelevation, lengthoftherunway,widthoftherunwayandmissingINFORMandLPIrisks werereplacedwiththemeanvalues.
D.2Categoricaldata
Anumberoffeaturesinthefinaldatasetdescribeseachairportasamemberofacertaincategory.Forexample,the airporttype featurecategorises airportsinto smallairport,mediumairport,largeairport.Whileitisa clearandunderstandabledistinctionforahumaneye,themathematicalalgorithmsrequireanumericalexpression[15].AsproposedintheoriginalpublicationonSelfOrganisingMaps[20],thecategoricalfeaturewiththreevalueswas transformedintothreebinaryfeatures,withonequalto1,andallothersto0, foreachairport.AnexampleresultcanbeseeninTable 4.Toachievethatfor eachcategoricalfeature,theLabelBinarizerfunctionfromSciKit[15]wasused.
D.3Numericaldata
Itiscommonformanymachinelearningalgorithmstorequirestandardiseddata inputs,inordertoperformwell[15].Thisalsothecasewithunsupervisedlearningalgorithmusedinthisresearch-theSOM.Therearevariousmathematical transformationsthatcanhelptoachieveanormallydistributeddataanditis importanttochooseonethatfitsthetypeofdatathebest.Again,theSciKit documentation,supportedbyvariousscientificsources[6, 8, 19]andexperiments wasusedtochoosetherightapproach.
TheYeo-Johnsontransform[24]wasusedtochangethedistributionof numericaldata,sinceitwasoneofafewtransformationsthatcanbeapplied onnegativeandzerovalues,whichthedatasetcontained.Theeffectofthe transformationcanbeseeninFigs. 3 and 4.Whileitwasnotpossibletosuccessfullytransformallfeatures,especiallytheonesconsistingof0/1values,for mostfeaturestheimprovementisvisible.
Table4. Anexampleofencodingcategoricalfeatures
Fig.3. AnexampleofdatadistributionbeforetheYeo-Johnsontransform.Mostof thedatapointsareconcentratedaroundthelowervalues.ApplyingSOMdirectlyon anon-normallydistributeddatacouldleadtospecificfeaturesbeingoverrepresented, thereforethetransformationisneeded.
Fig.4. ExampleofdatadistributionaftertheYeo-Johnsontransform.Therangeof valueshaschanged,howevertherelationsbetweenspecificvaluesarekeptandthe distributionisnowclosertonormal.
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