Table Of ContentAdvances 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
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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.
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