Agent-Based Models and Complexity Science in the Age of Geospatial Big Data: Selected Papers from a workshop on Agent-Based Models and Complexity Science (GIScience 2016) PDF
Preview Agent-Based Models and Complexity Science in the Age of Geospatial Big Data: Selected Papers from a workshop on Agent-Based Models and Complexity Science (GIScience 2016)
Advances in Geographic Information Science Liliana Perez Eun-Kyeong Kim Raja Sengupta E ditors Agent-Based Models and Complexity Science in the Age of Geospatial Big Data Selected Papers from a workshop on Agent-Based Models and Complexity Science (GIScience 2016) Advances in Geographic Information Science Serieseditors ShivanandBalram,Burnaby,Canada SuzanaDragicevic,Burnaby,Canada Moreinformationaboutthisseriesathttp://www.springer.com/series/7712 Liliana Perez • Eun-Kyeong Kim (cid:129) Raja Sengupta Editors Agent-Based Models and Complexity Science in the Age of Geospatial Big Data Selected Papers from a workshop on Agent-Based Models and Complexity Science (GIScience 2016) 123 Editors LilianaPerez Eun-KyeongKim LaboratoiredeGéosimulation DepartmentofGeography Environnementale PennsylvaniaStateUniversity DepartmentofGeography UniversityPark,PA,USA UniversitédeMontréal Montreal,QC,Canada RajaSengupta DepartmentofGeography SchoolofEnvironment McGillUniversity Montreal,QC,Canada ISSN1867-2434 ISSN1867-2442 (electronic) AdvancesinGeographicInformationScience ISBN978-3-319-65992-3 ISBN978-3-319-65993-0 (eBook) DOI10.1007/978-3-319-65993-0 LibraryofCongressControlNumber:2017953010 ©SpringerInternationalPublishingAG2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Abroadrangeofconceptsandmethodologiesfromcomplexityscience—including agent-based models (ABMs), cellular automata (CA), and network theory, among others—have contributed to a better understanding of spatiotemporal dynamics of complex geographic patterns and processes. Particularly, ABMs have become ubiquitous in GIScience and a number of related application domains, prompting some ABM researchers to propose the YAAWN syndrome. Along with ABMs, muchmorescalingrelationshavebeenfoundthroughgeospatialbigdataanalytics. However,suchconvergenceisnotunidirectional.Manystatistical(social)physicists have done research on human mobility, urban dynamics, and landscape dynamics, which have traditionally been the domain of geographers and environmental scientists. Recent advances in computational technologies such as big data, cloud computing and CyberGIS platforms, and sensor networks (i.e., the Internet of things) provide new opportunities and raise new challenges for ABM and com- plexity theory research within GIScience. With growing accessibility to rich, big data sources and increased computing power, geographers can simulate dynamic geographic phenomena in a more realistic fashion and test theories and models using empirical data. Despite of the utility of complexity theories, adopting those methodologies properly to the geographic domain is an ongoing research issue. Challenges include parameterizing the complex models with volumes of georef- erenceddatabeinggenerated,scalingthemodelapplicationstorealisticsimulations overbroadergeographicextents,exploringtheproblemsintheirdeploymentacross largenetworkstotakeadvantageofincreasedcomputationalpower,andvalidating theiroutputusingreal-timedata,aswellasmeasuringtheimpactofthesimulation onknowledge,information,anddecision-makingbothlocallyandgloballyviathe WorldWideWeb. InSeptemberof2016,theNinthInternationalConferenceonGeographicInfor- mation Science (GIScience) was held in Montreal, Canada, and brought together approximately300participantsfromaroundtheworldfromacademia,industry,and governmentorganizationstodiscussandadvancethestateoftheartingeographic information science. Withinthecontext of GIScience, weheld aworkshop named v vi Preface “Rethinking the ABCs: Agent-Based Models and Complexity Science in the Age of Big Data, CyberGIS, and Sensor Networks.” The scope of this workshop was toexplorenovelcomplexityscienceapproachestodynamicgeographicphenomena andtheirapplications,addressingchallengesandenrichingresearchmethodologies in geography in a big data era. The 1-day workshop brought together experts on complexityscienceandsocialnetworksinordertodiscussnovelcomplexityscience approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a big data era. Wehadninelightningtalksandninepresentations,correspondingtofourshortand fivefullpeer-reviewedpapers.Wewrappeduptheworkshopwithaveryinteresting discussionaboutthefutureofagent-basedmodels.Asaresultofaveryproductive workshop,itwasdecidedtopublishthemajorfindingsasabookwithintheSpringer GIScience series. Seven selected papers from the workshop, which reflect the advances on ABM development and implementation, as well as the opportunities thatbigdataandnetworktheorycouldbringforabetterunderstandingofcomplex systems,havebeenincludedinthisbook. Theresearchcoveredbythecollectionofpapersinthisvolumeoffersthereader a possibility to encounter diverse applications of ABMs fully implemented and tested,throughthefirstthreechapters,followedbyafourthchapterthatpresentsan ABMtoidentifyhumanmigrationpathways.Finally,thelastthreechaptersexplore the possibilities of using big data and social networks to parameterize ABMs and discoverthecomplexitiesofmovement,migration,andurbanpatterns. Chapter1byCenekandFranklindescribesanABMsystemforthemanagement ofstocksandstakeholdersofAlaska’sSalmonFisheries.Ituses35yearsofsonar datatoparameterizeandcalibratethestockinformation,combinedwithinterviews of fishermen, in order to validate the model. Chapter 2 by Bitterman and Bennett presents and discusses the potential of using ABMs to explore resilience concepts inanagriculturallandusesystem.Theauthorssuggestanovelapproachintermsof exploringtheconceptofresilienceasanadaptivebehaviorwithinacomplexsystem. Chapter 3 by Taylor and Dragicevic presents a very interesting work applying the invariant-variant approach for validating an insect dispersal ABM. As evaluating theperformanceofagent-basedmodelsisnotoriouslydifficult,thepresentedwork makesaninterestingandvaluablecontribution.Chapter4byArnouxetal.offersa novelapproachtoidentifymigrationpathwaysduetoarmedconflictsbyproposing anABMtosimulatehumandecision-makingtomigratefromconflictareas.Chapter 5bySenguptaetal.offersanovelperspectiveabouttheuseofbigdatainorderto extractmovementrulestoparameterizeanABMofanimalmobility.Chapter6by Liuetal.presentsaveryinterestingworkusingspatialnetworkvisualizationsand IRAdatabasetounderstandmigrationsacrosstheUnitedStates,revealingtheurban hierarchy by investigating the directional network structure of US cities created basedontheUSmigrationpatterns.Lastbutnotleast,Chapter7byKoylupresents some innovative ideas related to the leveraging of social media to understand humaninteractionsatalevelthatwasdifficultorimpossibletodousingtraditional interactiondata. Preface vii Finally, we would like to express our appreciation to all contributing authors for their excellent work. Their participation made our workshop a major success andmadethisbookpossible.Wealsothanktheprogramcommitteeandadditional reviewers for reviewing and sharing their experience. We thank the many people who made GIScience 2016 possible: the steering committee for their support and thelocalorganizingcommittee.Lastbutnotleast,wewouldliketowarmlythank ourcolleaguesandfamiliesforsupportingus. ProgramCommittee Clio Andris (Pennsylvania State University), David Bennett (University of Iowa), Christopher Bone (University of Victoria), Suzana Dragicevic (Simon Fraser Uni- versity),BinJiang(UniversityofGävle),AlanM.MacEachren(PennsylvaniaState University), Mir Abolfazl Mostafavi (Universite Laval), Atsushi Nara (San Diego StateUniversity),DavidO’Sullivan(UniversityofCalifornia,Berkeley),andTaha Yasseri(OxfordInternetInstitute,UniversityofOxford). LightingTalks DavidBennett–DependentonWhichPath?ComplexityandAgent-BasedModeling Daniel G. Brown – Combining Spatial-Temporal Data with Behavioral Models HelpsUsBetterUnderstandSpatialProcess SuzanaDragicevic–APerspectiveonVoxel-BasedGeographicAutomata BinJiang–TheThirdDefinitionofFractal Eun-Kyeong Kim – Burstiness Measure as a New Exploratory Spatio-Temporal DataAnalysisStatistic DavidO’Sullivan–SimpleSimulationModelsasaComplexity‘PatternLanguage’ MirAbolfazlMostafavi–CAMUSS:TheStateoftheArtinCellularAutomata RajaSengupta–WhatCanWeLearnfromBigData?BehaviouralRuleExtraction fromAnimalMovementDatabases ClioAndris–SystemResilienceandCollapseasaFunctionoftheInformedAgent Montreal,QC,Canada LilianaPerez UniversityPark,PA,USA Eun-KyeongKim Montreal,QC,Canada RajaSengupta Contents Developing High Fidelity, Data Driven, Verified Agent Based ModelsofCoupledSocio-EcologicalSystemsofAlaskaFisheries.......... 1 MartinCenekandMaxwellFranklin LeveragingCoupledAgent-BasedModelstoExploretheResilience ofTightly-CoupledLandUseSystems......................................... 17 PatrickBittermanandDavidA.Bennett DeconstructingGeospatialAgent-BasedModel:SensitivityAnalysis ofForestInsectInfestationModel.............................................. 31 TaylorAndersonandSuzanaDragic´evic´ AnAgent-BasedModeltoIdentifyMigrationPathwaysofRefugees: TheCaseofSyria................................................................. 45 GuillaumeArnouxHébert,LilianaPerez,andSaeedHarati Automated Extraction of Movement Rationales for Building Agent-BasedModels:ExampleofaRedColobusMonkeyGroup ......... 59 RajaSengupta,ColinC.Chapman,DiptoSarkar,andSarahBortolamiol WealthyHubsandPoorChains:ConstellationsintheU.S.Urban MigrationSystem ................................................................ 73 XiLiu,RansomHollister,andClioAndris Discovering Multi-Scale Community Structures fromtheInterpersonalCommunicationNetworkonTwitter............... 87 CaglarKoylu ix About the Editors Liliana Perez is the director of the Laboratory of Environmental Geosimulation (LEDGE)andanassistantprofessorintheDepartmentofGeographyattheUniver- sity of Montreal. Liliana is interested in advancing GIScience methods applied to ecologybydevelopingmodelingapproachestosimulateecologicalcomplexitiesin ordertounderstandtheirbehavioranddynamicsaswellastousethemasastarting pointtobeginplanningandpreparingmanagementstrategiesinthefaceofclimate change. She has developed and implemented a series of simulation tools focusing onforestry,landscapeecology,biodiversity,andclimatechange. Eun-KyeongKimisaPh.D.candidateintheGeoVISTACenterintheDepartment of Geography at Pennsylvania State University. Eun-Kyeong has been developing spatiotemporaldataanalysismethodologiesbyadoptingapproachesfromstatistical physicsandcomplexityscienceandisinterestedingeospatialbigdatavisualization with advanced technologies. She has served as a graduate researcher for an NSF- sponsoredbigdataeducationproject,andsheisacoauthorofanonlinetextbookon bigdataanalytics. RajaSenguptaisassociateprofessorintheDepartmentofGeographyandSchool of Environment at McGill University. Dr. Sengupta is interested in research on both artificial life and software agents and applying GIScience to environmental management issues and water resources management. He was an editorial board member for the journal Transactions in GIS (2011–2016) and is currently an editorialboardmemberforWaterInternational. xi
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