ConferenceonModellingComplexUrbanEnvironmentsJune21-22,2018

UniversityofWaterloo,Canada

ScheduleataGlanceThursdayJune21,2018-Day1

8:30-9:00AM CoffeeandOpeningRemarks

9:00-10:00AM Keynote1

10:00-10:20AM CoffeeBreak

10:20-12:20PM Session8 Session2A Session4

12:20-1:30PM Lunch

1:30-2:30PM Keynote2

2:30-3:00PM CoffeeBreak

3:00-5:00PM Session7 Session9 Session3

5:00-6:00PM PosterContest/Reception

FridayJune22,2018-Day2

9:00-10:00AM Keynote3

10:00-10:20AM CoffeeBreak

10:20-12:20PM Session5A Session2B

12:20-1:30PM Lunch

1:30-2:30PM Keynote4

2:30-3:00PM CoffeeBreak

3:00-5:00PM Session1 Session5B

5:00-6:00PM Session10

AuthorIndex

Akbari Aidin Session3

Allen-Muncey Karl Session8

Anderson T.M. Session2A

Axhausen KayW. Session7

Babin Robert Session8

Bin Okmyung Session5A

Birkin Mark Session1

Bottai Carlo Session7

Briceno-Ariasa L.M. Session4

Campbell Lindsay Session2B

Carro A. Session5A

Colley Kathryn Session5A

Cools Mario Session3

Craig Tony Session5B(1),Session5B(2)

Crooks Andrew Session1

Currie PaulK. Session2B

deKoning Koen Session5A

Deadman Peter Session3

Detotto Claudio Session5B

Dragicevic S. Session2A

Driscoll John Session7

El-Shayeb Hadi Session3

Enns Russel Session8

Farmer D. Session5A

Filatova Tatiana Session5A

Fitzgibbons Jo Session2B

Fox Session4

Frank Anna Session3

Friesen Milton Session2A,Session3

Fu Liping Session1,PosterContest

Garcia JorgeA. Session4

Ge Jiaqui Session5B(1),Session5B(2)

Grant-Muller Susan Session1

Gret-Regame Adrienne Session7

Guo Cheng Session5B

Herrmann DustinL. Session2A

Hoffman Falk Session4

Huang Yu Session5A

Hyman Tracy-AnnNicole Session3,PosterContest

Idda Corinne Session5B

Innocenti Eric Session5B

Inostroza Luis Session1

Jahanmiri Fatemeh Session7

Jasny Lorien Session2B

Johnson Michelle Session2A

Khiali Amin Session7

Khirfan Luna Session3

Kim J. Session5B

Knapp Sonja Session4

Knight M.A. Session3

Lemoine-Rodríguez Richard Session1

Liu Anjie Session1,PosterContest Liu Nan Session5B

Maciejewski Kristi Session2B

Magliocca Nicholas Session5A

Malleson Nick Session1

Martínez FranciscoJ Session4,Session7

Meerow Sara Session2A

Minnaker Leia Session2B

MirnaslBonab SeyedehNayyer Session3

Mitchell Carrie Session2B

Mohammadifardi H. Session3

Mudigonda Srikanth Session3

Mullin Megan Session2B

Muresan Mathew Session1,PosterContest

Musango Musango Session2A

Mustafa Ahmed Session3

Nava ConsueloR. Session7

Nurse Leonard Session3,PosterContest

Odiari Eusebio Session1

Parker Dawn Session5A,Session7

Parkin J.Cameron Session2A

Pham Martin PosterContest

Polhill Garry Session5B(1),Session5B(2)

Ponnambalam K. Session4

Prunetti Dominique Session5B

Robinson Derek Session5B

Saddi Ismaïl Session3

Salt Doug Session5B

Schwarz Kirsten Session2A

Schwarz Nina Session4,Session5B

Smith David Session3,PosterContest

Straunch Michael Session4

Svendsen Erika Session2B

Teller Jacques Session3

Unger J.A. Session3

vanStrien MaartenJ. Session7

Vaughan James Session5A

Volz A. Session5B

Walls Margaret Session5A

Weber R. Session5A

Wilkin Tristan Session8

Woodruff SierraC Session2B

Wray Alexander Session2B

Zellner M. Session5B

Zhang Ziye Session5A

SessionDetailsSession1:Integrating“BigData”and“SmartCities”DataWithUrbanModellingOrganizer:AlisonHeppenstallJune22,2018;3:00-5:00PMLocation:Fromspectralvaluestothreedimensionalestimationofurbanform.AssessingquantitativerelationshipsbetweenTechnomassandradiometricindexesAuthors:RichardLemoineRodriguez(RuhrUniversitätBochum);LuisInostroza(RuhrUniversitätBochum;UniversidadAutónomadeChile)Abstract:Therepresentationofcomplexecologicalsystemsasaspatialnetworks,i.e.foodwebs,pest-parasitoidwebs,andmutualisticwebs,providesinsightintotheirorganizationandfunction.Recently,spatialnetworksareleveragedintheformoflandscapeconnectivitynetworks,composedofgeolocatedhabitatpatchesthatlinktogetherasafunctionofthemaximumdispersaldistanceofthespeciesofinterest.Landscapeconnectivitynetworksareusedtounderstanddispersalpatternsandhowthesepatternsrespondtochangesinlandscapestructureandconnectivity.However,landscapeconnectivitynetworksaremostlylimitedtostaticrepresentationsatthelargerlandscape-scalesanddonotaccountforcomplexityindispersalprocesseswheretheemergentstructureandevolutionofnetworkstructureisafunctionofdispersaldynamicsatsmallerscales.Themainobjectiveofthisresearchstudyistodevelopamodellingapproachthatintegratesnetworktheoryandagent-basedmodelling(ABM)fortherepresentationofadynamicdispersalnetworkoftheemeraldashborer(EAB)forestinsectinfestationinanurbansetting.TheproposedNetwork-ABM(N-ABM)generatesdynamicspatio-temporalpatternsofEABspreadtogetherwiththespatio-temporalEABdispersalnetworkusinggeospatialdatasetsoftheurbanforestenvironmentfromtheTownofOakville,Canadaasacasestudy.Graphtheorymeasures

areusedtocharacterizetheN-ABMsimulationresultstoprovideinsightintothedynamicsoftheEABdispersalandspatialnetworkstructureintheurbanlandscape.TheproposedmodelcanbeusefulforurbanforestmanagementasitiscapabletoidentifyimportantEABdispersalpathwaysandhabitatfeatures.Keywords:urbanmorphology,remotesensing,urbanindicators,landsurfacetemperatureEstimatingvehicleemissionsusingbigtrafficdataAuthors:AnjiLiu(UniversityofWaterloo);LipingFu(UniversityofWaterloo);MathewMuresan(UniversityofWaterloo)Abstract:Asclimatechangeandurbanairqualitybecomegrowingconcernsaroundtheworld,thereisgreaterneedtoquantifyvehicleemissions.Vehicleemissionsgenerateasignificantportionofgreenhousegases.Sincetheseemissionsareinfluencedbytrafficflowconditions,itisbeneficialfortrafficmanagementprofessionalstohavemethodstoestimateemissionsinrealtime.Unfortunately,directmeasurementofemissionsfromallroadvehiclesisbothcostlyandimpractical.However,emissionscanbeestimatedthroughtherelationshipsbetweenemissiongenerationandtrafficflow.Thisposterwillpresentamethodtoestimateemissionsinrealtimeusingbigtrafficdata.Trafficdatafromvideocameras,Wifidetectors,andloopdetectorsatintersectionsareusedtomodelthetrafficflowalongaroadcorridor.Anemissionmodelisdevelopedtousethetrafficflowasinputtoestimatetheemissionsgenerated.Newsourcesoftrafficdata,suchasvehicletrackingthroughvideocamerasusingimageprocessing,provideopportunitiesforimprovedtrafficflowmodellingand,subsequently,emissionestimation.Tocalibrateandvalidatetheemissionestimationmethods,apre-validatedsimulationapproachistaken.ThetrafficnetworkissimulatedinVissim,amicroscopictrafficflowsimulator,andcalibratedaccordingtothetrafficdata.OutputsofthesimulationareinputintoMOVES,anemissionsimulatordevelopedbytheEnvironmentalProtectionAgencyUS,togenerateemissionestimates.Theseresultsserveasthegroundtruthusedtoevaluatethereal-timeemissionestimationmethod.Keywords:emissionmodelling;trafficdata;trafficsimulation;trafficflowmodellingTheuseofbigdatainsimulatingattributesandmobilitiesofpassengersontheUKRailnetworkAuthors:EusebioOdiari(UniversityofLeeds);MarkBirkin(UniversityofLeeds);SusanGrant-Muller(UniversityofLeeds);NickMalleson(UniversityofLeeds)Abstract:Thenowwide-spreaduseofelectronicdeviceshasmeantthatnovelsourcesoflargeconsumerdatasets(called‘BigData’)areincreasinglyreadilyavailable.Thesedatasetsaretypicallycreatedforaparticularpurpose,andassuchareskewedanddonothavethebroadspectrumofattributesrequiredfortheirwiderapplication.Forexample,intherailsector,ticketingdataconsistsof~60%commuters,whilstthewiderCensuspopulationconsistsof~10%commutersmakingticketingdataaskewed(orbiased)subsetofthewiderCensuspopulation.Ticketingdataalsooftenhavelittleornosupplementaryinformationaboutthepassengerswhopurchasethem(suchasjourneypurpose,socio-demographicattributes,etc.).Furthermore,theticketingdatadoesnothaveinformationaboutthecontextinwhichtheticketwasused(suchasthelevelofcrowdinginthetrainwhentheticketwasused,orthewaitingtimesthepassengerendured).Thislackofcontextpresentschallengesintheplanningandmanagementofrailways,andincausalinferenceonthedriversofmobilitychoice.Thisresearchquestionswhetherbiasedlargeconsumerdataavailablewithintherailwaysindustrycanbecombinedwithsupplementaryrepresentativedatatoaugmentcurrentestimatesofpassengerattributesandactivityontherailways.Inthefirststageoftheresearch,spatialmicro-simulationisusedtoadjusttheresolutionandgeographicscaleofthreedisparatedatasets:‘bigdata’consistingofeveryrailwayticketsoldintheWestYorkshireUKstudyarea;the2011Censusinteractiondata;andthe

NationalRailTravelSurvey.Themethodologyextendsforthefirsttimetheconceptofsimultaneouslyconstrainingfor3-levelsofvariables,enablingtheinclusionofmobilityinteractioninthespatialmicrosimulationprocess.Theoutcomeisasyntheticpopulationofattribute-richindividualswhoarerepresentativeofallrailwaypassengersinthestudyarea.Inthesecondstage,thesyntheticpopulationisusedasinputintoarailGISnetworkthathasbeenlogisticallyconstrainedbyrealtransitschedules.Thisproducesarichdatasetofrailwaypassengersincludingbothexogenousandendogenousattributes,enablingtheidentificationofcontextrichspace-timevolumesofindividualpassengersontraincarriages,platformsandstations.Thisalsoenablestheassignmentofpassengerstotrafficontherailnetwork,precludingtheneedforthecomplexitiesofautilityoptimizingtransitassignment.Thehypothesisisthatsuchmicro-levelinformationonpassengerdemandwillhaveasignificantimpactonthequalityofinputstostrategic,tacticalandoperationalrail-sectoranalysisplanningmodels.Furthermore,anaccurateGISrailnetworkmodelwouldbebeneficialtonetworkoperatorsforthemanagement,maintenanceandinterventionsassessmentontherailways.Keywords:consumerdata,bigdata,passengermobility,networksimulation,spatialmicrosimulationLeveragingOpenDataforUrbanAgent-basedModels:ExamplesandChallengesAuthors:AndrewCrooks(GeorgeMasonUniversity);SarahWise(UniversityCollegeLondon)Abstract:Todayweareawashwithnewsourcesofopendata,fromCCTVtocrowdsourceddata.Newformsofdatarangeintheircharacteristicsandpurpose.OneexampleisVolunteeredGeographicalInformation(VGI),wereuserspurposelycontributeGeographicInformation(GI)asinthecaseofOpenStreetMap;anotherisAmbientGeographicInformation(AGI),wheretheintentionofcontributorsisnotnecessarilytoprovideGI,butGIcanbederived,asfromTwitter.WhilemuchprogresshasbeenmadeinutilizingthesenewsourcesofdatainGIScience,theyhaveonlyrecentlybeguntobeintegratedintoagent-basedmodels(ABMs).ThistalkwilldiscusstheopportunitiesthatopendataprovidesforABMs,specificallyfocusingonhowsuchinformationgivesusanewlenstostudythemicrointeractionsofindividualsortovalidateemergentpatternsfromABMs.Throughaseriesofexamples,wewilldemonstratehowsuchdatacanbeintegratedintogeographicallyexplicitABMs.Bybuildingontheseexampleswewillshowcasehowthespatialenvironmentandagentpopulationscanbebuiltusingopendataandhighlighthowagentbehaviorscanbeinformedalongwithhowaggregatedoutcomescanbevalidatedbysuchinformation.Wewillfurtherdiscussthechallengesassociatedwiththisprogramofresearch:usingsuchdataisnotwithoutitsdifficulties,includinggatheringoraccessingthedata,storingthedata,analyzingthecollecteddata,andassessingitsvalidity.Together,thisworkprovidesabriefoverviewofthecurrentstateofopendata-informedABM.Keywords:agent-basedmodeling,opendata,volunteeredgeographicalinformation,socialmedia,crowdsourcing,urbansystems

Session2A:Social-EcologicalNetworksintheCityOrganizer:JeremyPittmanandCarrieMitchellJune21,2018;10:20AM-12:20PMLocation:Integratingspatialnetworksandagent-basedmodellingforrepresentationofforestinsectinfestationinurbanlandscapes Authors:T.M.Anderson(SimonFraserUniveristy);S.Dragicevic(SimonFraserUniversity)Abstract:Therepresentationofcomplexecologicalsystemsasaspatialnetworks,i.e.foodwebs,pest-parasitoidwebs,andmutualisticwebs,providesinsightintotheirorganizationandfunction.Recently,

spatialnetworksareleveragedintheformoflandscapeconnectivitynetworks,composedofgeolocatedhabitatpatchesthatlinktogetherasafunctionofthemaximumdispersaldistanceofthespeciesofinterest.Landscapeconnectivitynetworksareusedtounderstanddispersalpatternsandhowthesepatternsrespondtochangesinlandscapestructureandconnectivity.However,landscapeconnectivitynetworksaremostlylimitedtostaticrepresentationsatthelargerlandscape-scalesanddonotaccountforcomplexityindispersalprocesseswheretheemergentstructureandevolutionofnetworkstructureisafunctionofdispersaldynamicsatsmallerscales.Themainobjectiveofthisresearchstudyistodevelopamodellingapproachthatintegratesnetworktheoryandagent-basedmodelling(ABM)fortherepresentationofadynamicdispersalnetworkoftheemeraldashborer(EAB)forestinsectinfestationinanurbansetting.TheproposedNetwork-ABM(N-ABM)generatesdynamicspatio-temporalpatternsofEABspreadtogetherwiththespatio-temporalEABdispersalnetworkusinggeospatialdatasetsoftheurbanforestenvironmentfromtheTownofOakville,Canadaasacasestudy.GraphtheorymeasuresareusedtocharacterizetheN-ABMsimulationresultstoprovideinsightintothedynamicsoftheEABdispersalandspatialnetworkstructureintheurbanlandscape.TheproposedmodelcanbeusefulforurbanforestmanagementasitiscapabletoidentifyimportantEABdispersalpathwaysandhabitatfeatures.Keywords:Networks;Agent-basedmodeling;Forestinsectinfestation;Urbanforestmanagement,Geographicinformationsystems

ComparingSocialCapitalandSpatialUsePatternsinUrbanEnvironments Author:MiltonFriesen(UniversityofWaterloo)Abstract:Thispaperutilizestheresultsofanewsocialcapitalmeasurementinstrument(SocialCapitalGeneralSocialSurvey–SCGSS)developedforneighbourhoodscalemeasurementofcitizenssocialnetworkandtrustlevels.Understandingandreliablyquantifyingsocialinfrastructureatthisscaleisbecomingincreasinglyurgentandthisexploratorypaperextendsourknowledgeofthosedynamics.ThesurveyinstrumentispairedwithGlobalPositioningSystem(GPS)datatomeasurespatialbehaviourofarandomsampleofparticipants(n=97)acrossthreeEastHamiltonCensusTractsdifferentiatedbyincome–median(Rosedale)andastandarddeviationabove(Delta)andbelowthemedian(McQueston)foradultsaged18-64years.ThespatialstatisticsoftheGPSdataareanalyzedandsummarizedinArcGISforcomparisonwiththeSCGSSdatacollectedfromthesameparticipantstodeterminethevalidityofthecentralhypothesisthatgreatermovementintheurbanenvironmentcorrelateswithhigherlevelsofsocialcapital.Thecomparativedataareanalyzedusinglinearregression(LM),randomforest(RFM)andprincipalcomponentanalysis(PCA)inanRenvironmentandtheresultsaresummarized.Keywords:socialcapital,socialnetworks,spatialstatistics,socialinfrastructure,GPSModelingAvianPatchNetworksinDenseUrbanSettingsAuthor:J.CameronParkin(UniversityofWaterloo)Abstract:Thisresearchfocusesonthemovementandaccommodationofavianandothervagilespeciesthroughpatchnetworksindenseurbanfabric.Thegoalofthisresearchistoanalyzeandvisualizeexistingurbanpatchnetworksinordertoinformthelocation,strategy,andcompositionofinterventionsintheurbanfabric.Theseinterventionsaredesignedtopromoteurbanite-faunainteraction,whichhaspotentialtoincreasecitydwellers'environmentalstewardship,andbenefittheirpsychologicalwell-being.Inordertomanagetheinherentcomplexityofnegotiatingurbanandecologicalsystemsinthedensedowntowncore,digitalmethodsofmodellingnetworksareborrowedfromlandscapeecology,graphtheory,andparametricarchitecture.Computationalworkflowsarecreatedtosimulatetwodimensionalandthreedimensionalagentmovementwithinpatchnetworks,

populatethesenetworkswithbirdsightingdata,andusethisinformationtolocateandinformavarietyofinterventiontypologies.Keywords:Ecology;UrbanEcology;EcologicalData;EcologicalNetworks;ComputationalDesignAGreenInfrastructureSpatialPlanningmodelforevaluatingecosystemservicetradeoffsandsynergiesinthreecoastalmegacitiesAuthor:SaraMeerow(ArizonaStateUniversity)Abstract: Agrowingnumberofcitiesareexpandinggreeninfrastructuretofosterresilience.Whilethesenature-basedsolutionsareoftenpromotedonthebasisoftheirmultifunctionality,inpractice,moststudiesandplansfocusonasinglebenefit,suchasstormwatermanagement.Thisrepresentsamissedopportunitytostrategicallysitegreeninfrastructuretoleveragesocialandecologicalco-benefits.Toaddressthisgap,thispaperpresentstheGreenInfrastructureSpatialPlanning(GISP)modelasatoolforidentifyingandcomparingspatialtradeoffsandsynergistic‘hotspots’formultiplebenefitsinthreediversecoastalmegacities:NewYorkCity,LosAngeles(UnitedStates),andManila(Philippines).Spatialmulti-criteriaevaluationisusedtoexaminehowstrategicareasforgreeninfrastructuredevelopmentacrossthecitieschangedependingonwhichbenefitisprioritized.PreliminaryGISlayerscorrespondingtosixplanningpriorities(managingstormwater,reducingsocialvulnerability,increasingaccesstogreenspace,improvingairquality,reducingtheurbanheatislandeffect,andincreasinglandscapeconnectivity)aremappedusingexistingdatasetsandspatialtradeoffsassessed.Criteriaareweightedtoreflectlocalstakeholders’prioritiesasdeterminedthroughsurveysandstakeholdermeetings,andthecombinedresultsvisualized.Whileadditionalmodelimprovementsareclearlyneeded,preliminaryresultsempiricallyillustratethecomplexitiesofplanninggreeninfrastructureandurbanresiliencemoregenerally.CommunityengagementwithgreeninfrastructureasamechanismtopromoteurbanresilienceAuthors:KirstenSchwarz(NorthernKentuckyUniversity),DustinL.Herrmann(UniversityofCincinnati)Abstract:Greeninfrastructureisamechanismforpromotingurbanresiliencethroughitssocialandecologicalbenefits,butitspotentialtoconferresiliencemaydependonthelevelofcommunityengagementinthedesign,implementation,andmaintenanceofsuchprojects.Weuseacasestudyofacommunity-engagedgreeninfrastructureprojectinnorthernKentuckyaswellasanemergingframework,EcologyfortheShrinkingCity,toexaminethepotentialroleofcommunityengagementinbuildingurbanresilience.Acentraltenetofresilienceistheabilityto“returntodesiredfunctionsinthefaceofdisturbance,toadapttochange,andtoquicklytransformsystemsthatlimitcurrentorfutureadaptivecapacity”(Meerowetal.2016).Resilience,therefore,isagoalonlyattainablethroughsustainedeffortandinvestment.Whengreeninfrastructureprojectsareimplementedwithlittleinputfromthecommunityinwhichtheyareembedded,theynotonlymissanopportunitytodevelopenvironmentalstewardship,butmayimpactthelong-termabilityofacommunitytorespondtodisturbanceinawaythatpromotesurbanresilience.Understandingtheroleofcommunityengagementinbuildingurbanresilienceisespeciallyimportantinshrinkingcitiesandareasofconcentrateddisinvestment,asvacantlandsrepresentplacesofpotentialsocial-ecologicaltransformation.

Session2B:Social-EcologicalNetworksintheCityOrganizer:JeremyPittmanandCarrieMitchellJune22,2018;10:20AM-12:20PMLocation:

ExploringPerceptionsoftheWesternCapeWaterCrisisthroughSocialNetworkAnalysis Author:KristiMaciejewski(StellenboschUniversity);PaulKCurrie(StellenboschUniversity);JosephineKavitiMusango(StellenboschUniversity)Abstract:TheWesternCapeProvinceofSouthAfricaiscurrentlyexperiencingtheworstdroughtinitsrecordedhistory.Likemanycitiesinrecentyears,CapeTownisfacinganexistentialthreattoitseconomyandthewellbeingofitsresidentsduetherapidreductioninwateravailability.Numerousdemand-side-managementandoff-gridwatersupplyinterventionshaveresultedinanunprecedentedreductionindailywaterconsumption,from1.1billionlitrestonear450litres,overjust24months.Thefundamentaldirectcausesofthecrisis,andofchangesinwaterconsumptionlevels,areasyetunclear.However,itisapparentthatresidentsholdvariedperceptionsof:(i)thecausesofthecrisis;(ii)thecontributionsofdifferenteconomicsectorstowardswatersavingorover-consumption;(iii)howthecrisishasbeenmanagedbytheCityandotherstakeholders;(iv)thekeyinstitutionsinvolvedincommunicationsorinterventions;and(v)theefficacyofresidents’owncontributionstowatersavingathouseholdandcommunitylevel.Thus,theobjectiveofthispaperwastodrawlinksbetweenresidents’perceptionsofthecrisisandtheirassociatedbehaviouralchanges.ThiswasachievedusingSocialNetworkAnalysis,whichwasdevelopedthroughawide,convenientsurveyofkeystakeholders,andwhichidentifiedkeyevents,campaigns,institutionsandcommunitymembersthatinitiatedchangeandinfluencedhumanbehaviour.Thepaperprovidesusefulinformationforunderstandinghowresidents’perceptionscouldbeinfluencedshouldfuturecrisesemerge.Keywords:social-ecologicalsystem,drought,institutions,humanbehaviourchangeUnderstandingenvironmentalstewardshipoutcomesthroughthelensofsocialnetworkanalysis:towardsasocial-ecologicalnetworkanalysisAuthors: MichelleJohnson(USDAForestService,NorthernResearchStation),ErikaSvendsen(USDAForestService,NorthernResearchStation),LindsayCampbell(USDAForestService,NorthernResearchStation),LorienJasny(UniversityofExeter) Abstract:Social-ecologicalnetworkshaveemergedasafieldofresearchtounderstandhowsocialconnectivityandecologicalconnectivitymayinterfaceandinteract,butfindingalignmentofsocial-ecologicalconnectivityforsuchanalysiscanbechallengingincomplexurbansystems.However,governancesocialnetworksstillhavethepotentialtoaffectecologicalconditionsatmultiplescales,regardlessofecologicalconnectivity.Thelargenumberofindividualsandorganizationsincitiesresultincomplexnetworks;howtheseorganizationstogethercanaffectecologicalconditionshasbeentheorized,buthavebeenempiricallymeasuredinfrequently.In2017,theNewYorkCityStewardshipMappingandAssessmentProject(STEW-MAP)collectedsurvey-baseddataonenvironmentalstewardshiporganizationalnetworks,individualorganizations’perceivedoutcomes,andotherorganizational-levelcharacteristicsofenvironmentalstewardshipgroups.Here,weapplythissocialnetworkdatasetofurbanenvironmentalstewardshiporganizationstoinvestigatehowasocialnetworkcaninfluencecumulativeenvironmentaloutcomesinNewYorkCity.Beginningwithperceivedoutcomes,wealsoconsideravailableplot-leveldataonforestandwetlandconditionsandremotely-sensedlanduse/landcoverdata,togainanunderstandingofoverallsystemchanges.Fromthisempiricalanalysis,weidentifyhowperceivedenvironmentaloutcomesrelatetonetworkstructureand

position,aswellasoverallecologicalcondition.Wealsoidentifythepotentialforfuturesocial-ecologicalnetworkanalysisincitieslikeNewYork,byidentifyingaspectsofthelargerurbansocial-ecologicalsystemwhereconnectivitycaninfluencebothstewardshipoutcomesandecologicalconditioninasocial-ecologicalmanner.Isclimatechangeadaptationapublicgood?Authors:SierraCWoodruff(TexasA&MUniversity),MeganMullin(DukeUniversity) Abstract:Astheimpactsofclimatechangebegintobefeltacrosstheglobe–includingmoreintensehurricanesandextremewildfires-itisclearthatadaptationisessential.Thequestionremains,however,whoisresponsible?Insufficientattentionhasbeendedicatedtotheidentificationoftheprovidersandbeneficiariesofclimateadaptation.Specifically,weexaminewhenadaptationisapublicgood.Thisinformationiscriticaltoidentifyactors,designaccuratemodelsofadaptation,andcreateeffectivepolicyinstruments.Climatechangemitigation–reducinggreenhousegasemissions–isaclassicpublicgoodsproblemwithimplicationsforwhoengagesinmitigationeffortsandhow.Manyaspectsofadaptationalsohavepublicgoodcharacteristics.Thewidespread,trans-boundaryconsequencesofclimatechangepushesadaptationtowardsthepublicgoodsendoftheconceptualcontinuum.However,therearesignificant“impurities”inadaptationsinceclimatechangeimpactsarenotequallydistributed.Thefactthatsomecommunitiesandsomeindividualswillbemoreheavilyaffectednarrowsthe“public”natureofclimateimpacts.Theunequaldistributionofimpacts,meansthatthebenefitsofadaptationarealsounequalwithpotentialimplicationsforwhotakesaction,policyandfinancingdecisions.Inthispaperweconsiderwheresealevelrisestrategies,suchaselevationofbuildingsandconstructionofseawalls,fallonthecontinuumfromprivatetopublicgoods.Wedrawonthesethoughtexperimentstoconsiderthefinancialinstrumentsavailabletomobilizepublicandprivateresourcestoadvanceadaptation.Keywords:Climateadaptation;publicgoods;sealevelrisePrinciplesofjusticeinurbanresilienceAuthors:JoFitzgibbons(UniversityofWaterloo),CarrieMitchell(UniversityofWaterloo)Abstract:Ascitiesgloballydealwithincreasinglycomplexandinterrelatedproblemssuchasclimatechange,theideaofbuildinggeneral“resilience”inurbansystemsisoutpacingotherpolicyframeworksinpopularity.Yet,manyscholarshavecautionedthatresilienceisaninherentlyconservative(ratherthantransformative)conceptwhichobscuresissuesofpowerandjustice(Meerow,Newell&Stults,2016;Joseph,2013;Ziervogeletal.,2017).Todate,therehasbeenlittlebywayofin-situorempiricalfindingstotesttheseclaims.TheRockefellerFoundation’s100ResilientCities(100RC)isonemajorinitiativethatattemptstomainstreamresiliencethinkingintocityplanning.Wehavetakenthisongoingprogramasanopportunitygenerateempiricalfindingsonhowplannersconfrontissuesofjusticeandinequityinresilienceplanning,focusingontheglobalSouth.Tosupportouranalysis,wehavedrawnonAnguelovskietal.’s(2016)conceptsof“actsofcommission”and“actsofomission”toconsiderbothproceduralanddistributivejustice.Proceduraljusticeprocessessuchasnegotiationandredistributionofpowerarenecessarytogeneratethetransformativesystemschangesthatresiliencenarrativespromise(Harris,Chu&Ziervogel,2017).Theseproceduralconsiderationsalsoinfluencedistributivejusticebyaffectingspatialandinfrastructureplanningwhich,inturn,affecteconomicandresourceinequities.Usingkeyinformantinterviewsanddirectedcontentanalysisof13ResilienceStrategies,weexaminewhetherandhowthe100RCChiefResilienceOfficershaveunderstoodandimplementedprinciplesofjusticeinthedevelopmentoftheircity’sResilienceStrategy.Ourfindingsconfirmthatresilienceplanninghasnotsoughttoradicallytransformpowerrelationswithincities,norfocused

explicitlyonjustice.Thesefindingswillhelpguidefutureplannersandpolicymakerstoreflectonjusticenotonlyasanoutcomeforresilience,butalsoasapartoftheplanningprocess.Keywords:resilience,environmentaljustice,criticalplanning,climatechange,proceduraljusticeThesocial-ecologicalnetworksofcancerpreventioninurbanEnvironmentsAuthor:AlexanderWray(UniversityofWaterloo);LeiaMinnaker(UniversityofWaterloo)Abstract:Thispaperexploresthenetworkofdependenciesbetweensocialstructures,ecologicalsystems,andcanceroutcomes.Theglobalburdenofcancerissubstantialandgrowing,ofwhichhalfarepreventablethroughchangestolifestyleand/orenvironmentalfactors.Primaryformsofcancerpreventionoftenrevolvearoundalteringbehaviouralorenvironmentalconditionstoreduceexposurerisk,whilesecondaryformsofpreventionaimtounderstandexistingcontributionsofsocioecologicalconditionstocancerepidemiology.Therefore,giventhemajorityofthehumanpopulationnowresidesinurbanareas,thecitybecomesthedominantplacetoexplorethecomplexsystem-levelrelationshipsbetweenhealthandenvironment.Wepresentresultsofascopingreviewof308peer-reviewedjournalarticlespublishedbetweenJanuary1990andApril2017,across14subjectdatabases.Thereviewoftheevidencepublishedtodaterevealsatrendtowardsaccountingforcomplexsocialandenvironmentalcontributionstocancerrisk.Researchershaveexploredtheroleofsocioeconomicfactors,aswellasanthropogenicenvironmentalexposuresinalleviatingoraggravatingtheriskofalltypesofcancer.Thespatialpatterningandaccesstohousing,transportation,greenspace,andpublicservicesallcontributetoarealorperceivedriskofcancerforpeople.Furthermore,theevidencesuggeststhaturbanandhealthrelateddisciplinesoperateinrelativeisolation,oftennotsharingresearchacrosseachother’sprimaryliteraturenetworks. Session3:ModellingandtheplanningprocessOrganizer:JeremyPittmanJune21,2018;3:00-5:00PMLocation:

ASystems-OrientedVulnerabilityApproachtoFloodinginCave,WestmorelandAuthor:Tracy-AnnNicoleHyman(UniversityoftheWestIndies);DavidSmith(UniversityoftheWestIndies);LeonardNurse(UniversityoftheWestIndies)Abstract:TheCommunityofCaveisapproximately4.8km²andliesjustoffthesoutheastcoastofWestmoreland,Jamaica.Itisapproximately12kmfromtheparishcapital,Savanna-la-Mar,withmajoreconomicactivitiesnamelyfarmingandfishing.Likemanyislandeco-systems,Caveispronetotropicalcyclonesthatgiverisetoflooding.Ofspecialnoteisthe1979floodevent,whichcausedsignificantdamagetoterrain,disruptiontotransportationsystemsanddevastationtolivesandpropertyinWesternJamaica.TheparishofWestmorelandexperienced31ofthe41deaths,andmanypersonsweretrappedintheircommunities,unabletoevacuate.Stilltoday,therearelimitedrainfallgaugesandnofloodgauges,earlywarningsystemsnoranofficialevacuationshelter.Witha50-yearreturnperiod,anyrecurrenceofthistypeoffloodin2016,couldbeworsethanin1979.Theoverallaimofthisresearchwasthustoconstructabasicsimulationofa1979flood-likeeventassessingthevulnerabilityofstakeholderstofloodingin2016.Thisbasedongeographicalconsiderations,floodcharacteristicsandthemovementpatternsofresidents–analyzedthroughtheuseofAgent-BasedModels,GeographicInformationSystems,andimplementedwithNetlogosoftware.Preliminaryresultsrevealthatpossiblelossoflifetakesplaceoneachofthethreedayssampled,[1]withmostfatalitiesonaFriday.The

expectedresultsfromthisresearchshouldguidelocalgovernmentintheappropriatecitingforevacuationshelters,safezones,alongwithimplicationsforearlywarningandlanduseplanninginSouth-EastWestmoreland.Keywords:Cave,flooding,agent-basedmodel,evacuationInstitutionalEmergenceandthePersistenceofInequalityinHamilton,ON1851-1861Author:MiltonFriesen(UniversityofWaterloo);SrikanthMudigonda(St.LouisUniversity)Abstract:Economicinequalityinurbansettingsisareadilyobservablephenomenaincontemporarycitiesbuthistoricalresearchreflectsthattheproblemisnotnew.Inthispaperwearguethattherearecitizenlevelinteractionsandarrangementsthatcontributetothestabilityofasmallgroupofwealthycitizensalongsideahighdegreeoftransienceinthepoorandpopulouspartofthecity.WedevelopanRcodedagentbasedmodelthatdrawsoncitizenagencyandeconomicdynamicsrevealedinastudyofHamilton,Ontario(1851-1861)byMichaelKatz(1975).TheKatzstudydrewontaxassessment,censusandrelatedformaldocumentationtoestablisheconomic,institutionalandmobilitycitizenpatternsacrossatenyearspan.Ourcentralhypothesisisthatthewealthycitizensdevelopedandhadaccesstoinstitutionalresourcesthatbufferednegativeexternalitieswhereasthepoordidnot.InourAxtellandEspteinbasedmodel,aprobabilityfunctionleadstoproto-institutionsthatemergefromagentinteractionsandthereafterproducegreaterstability,higherlevelsofwealth(sugar)andlongerlifespansfortheirparticipatingagents.Theinverseistruefortheagentswhodonothaveinstitutionalaccess.Keywords:agent-basedmodeling,insitutionalemergence,citizenagency,socialevolution,inequality,urbanpolicy,citizentransienceApplicationofSystemDynamicmodelingtoolinsustainabilityassessmentofwaterandwastewaterassetmanagementplansAuthor:H.Mohammadifardi(UniversityofWaterloo);M.A.Knight(UniversityofWaterloo);A.J.A.Unger(UniversityofWaterloo)Abstract:Thepublicinfrastructures,specificallyundergroundwaterandwastewaterpipenetworksystems,arereachingtheirendofservicelife,andtheirreplacementwillneedsignificantcapitalwork.ThegovernmentofCanadaannouncedthe$53-billionNewBuildingPlanforrejuvenationofCanadianpublicinfrastructure,thelargestinfrastructureinvestmentincountry’shistory.However,theacceleratedrehabilitationandreconstructionofinfrastructureswillnothappenwithoutconsequencesonsocial,ecological,andeconomicsystemsthroughthechoicestakenintheirassetmanagementplans.Thecomplexityofplanningdecisionsismorecompoundedwhenplannersrealizethatdifferenteconomic,social,andenvironmentaldimensionsofthechallengeareinherentlyinterrelated.Infact,technologicalsystemsarenotedtohavethehighestlevelofcomplexities,inwhichtheyinteractwithallsocial,natural,andeconomicsystems(Halliday&Glaser,2011).Complyingwithexistingandchangingregulatorypoliciesandrequirements,anddevelopmentofsociallyacceptable,environmentallyfriendlyandfinanciallyviableassetmanagementplanscallsforacomprehensivesustainabilityassessment.Thispaper,demonstratetheapplicationofSystemDynamic(SD)modelingandLife-CycleAssessment(LCA)toolforsustainabilityassessmentofmunicipalwaterandwastewaterassetmanagementplanscenarios.Keywords:Systemdynamicmodeling,Sustainabilityassessment,Lifecycleassessment,Waterandwastewater,assetmanagementplanningEquitableModesofPlanningPractiseforUrbanClimateResilience:ACaseStudyinCharlottetown,PEIAuthor:LunaKhirfan(UniversityofWaterloo);HadiEl-Shayeb(UniversityofToronto)Abstract:Theunpredictabilityofclimatechangeimpactsonurbanareascannotbeignoredinordertoavoidsignificantlossesinthebuiltenvironment,economy,andhumanhealth.Climatevulnerablecities

requirecontext-specificresilienceplans,wherenaturalandconstructedsystemsareabletoreturntoanoriginalstate(persist),respond(adapt),andreorganize(transform)inthewakeofdisturbance.Yet,thediscussiononurbanresiliencecannotbegivendueprocesswithoutthetetheringoftheecologicaldimension,whereblueandgreennaturalelementsarepreservedandvaluedfortheirservices.Furthermore,thesingularemphasisontheexpertattheexpenseofcommunityknowledgeofbioticandabioticfunctionscanrenderthedesignprocessvoidofcontext-specificandcommunityvalueessentialforcreatingclimateresilience.Inthispaper,weunderscorethesocio-ecologicalelementsofurbanclimateresiliencebydevelopingatheoreticalframeworkthatadaptsthecharrette,asaparticipatorydesigntool,tocapitalizeonlocalecologicalknowledge(LEK)andattributealocalvaluesystemforblue-greendesigninterventions.Thus,theframeworkisgroundedinaninterdependenttriadofvaluesincludingthedesignofthebuiltform,blue-greeninterventions,andlocalknowledgetransfertoaction,tosucceedtransformativeresilience.WeapplythistheoreticalframeworktoCharlottetown,PrinceEdwardIsland(P.E.I.),acitythatisincreasinglysubjectedtointensestormsurgesandsealevelriseinAtlanticCanada,whereaseriesofdesigncharrettesintegratedtheLEKintourbanclimateresilienceproposals.Keywords:UrbanClimateResilience;LocalEcologicalKnowledge;Blue-GreenInfrastructure;TransformationIntegratedWatershedModelinginUrban-RuralGradient:ReviewoftheSWATModelingFramework Author:SeyedehNayyerMirnaslBonab(UniversityofWaterloo);AidinAkbari(UniversityofManitoba);AnnaFrank(UniversityofWaterloo);PeterDeadman(UniversityofWaterloo)Abstract:Theconstantgrowthofhumanimpactonnaturalsettingsandthereciprocalinfluencesofphysicalenvironmentonsocialsettingshaveleftnoroomformutuallyexclusivethinkingframeworkswithrespecttonaturalsystemsandhumansocieties.Theintenseuseoflandbyhumansocietiesforagriculturalproductionandtheadverseimpactsoftherangeofactivitiesinurbanenvironmentscontributingtothisintensityonbothnaturalandsocialsettingsareclearindicatorsofthecomplexityofflowsandinteractionsthatliewithinandamongbothcontexts.ItisplausibletoinferthatIntegratedWatershedModeling(IWM)shouldtranscendtheoldboundariesofthoughtsandmustbedefined,implemented,andmeasuredwithinanewthinkingframeworkembracingbothurbanandruralsettings.Inthisregard,thispaperaimstodrawageneralconclusionbasedoninductivereasoningonthenecessityofdefininganewframeworkforIWMinurban-ruralgradient.WefocusedontheSWATmodelasanexemplaryIWMtool,andconductedaliteraturereviewoftheframeworksusedandsearchedforexamplesofcoupledurban-ruralmodels.WemadeastructuredreviewoftechnicalchallengesandbenefitsofusingSWATmodelasadecisionmakingsupporttoolforsustainableurbanplanning.Keywords:SWAT,urbanandruralplanning,integratedwatershedmodeling,coupledhumanandnaturalsystemsAnintegratedCellularAutomataAgentBasedmodeltosimulateurbanizationAuthor:AhmedMustafa(UniversityofLiège);IsmaïlSaadi(UniversityofLiège);MarioCools(UniversityofLiège),JacquesTeller(UniversityofLiège)Abstract:Severalstatisticalandgeospatialapproacheshavebeenproposedanddevelopedtomodelurbanexpansion,includinglogisticregression,cellularautomata,andagent-basedmodels.Thisstudyproposesanintegratedcellularautomataandagent-basedmodel(CA-AB).Themodelsimulatestwodevelopmentprocesses:expansionanddensification.ThemodelisappliedtoWallonia(Belgium),asacasestudy.Theurbanlandusemapsof1990,2000,and2010andseveralurbanizationexplanatoryfactorsareusedtocalibratethemodel.Theagentsarecategorizedintothreegroupswithdifferent

characteristicsandgoals:developer(DevAG),existingresident(ExtAG)andplanningpermissionauthority(PPA).Ateachtimestep,correspondingtooneyear,anumberofDevAGsselectlocationstodevelop.Theselectionoftargetlocationisaspatialknowledge-baseddecision.Eachdeveloperagenttendstobuildwithintheexistingurbanarea(densification)orclosetoitinordertobenefitfromexistingservices.Inthisstudy,a3×3neighborhoodwindowisusedtorepresenttheDevAGspreferencesregardinglocalattractiveness.Aftertherespectivedeveloperhasselectedacelltodevelopordensifyandatwhichdensitylevel,ithastoaskforadevelopmentpermitfromthePPA.ThePPAdeterminestoallowthedevelopmentaccordingtotwofactors:(i)landuseregulations,and(ii)theresistanceofexistingresidentsagainstproposednewdevelopment.ThevalidationofthemodelshowscomparableresultstothosereportedintheliteraturewithKappaindicesbetween0.88and0.92.

Session4:ArtificialIntelligence&OptimizationModelsinUrbanEnvironmentsOrganizer:JorgeGarciaJune21,2018;10:20AM-12:20PMLocation:LanduseplanningandoptimalsubsidiesAuthor:L.M.Briceno-Ariasa(UniversidadTecnicaFedericoSantaMaria);F.Martinez(UniversityofChile)Abstract:Theurbanplanificationisacomplexprobleminvolvingthechoiceofasocialobjectiveforabuiltcity,fromwhichanoptimalallocationofagentsshouldbefoundasamarketequilibriumwiththehelpofsomepolicylikesubsidies.Inordertofindtheoptimalallocationandsubsidies,weproveafundamentalresultwhichassertsthatanyfeasibleallocationcanbeachievedasamarketequilibriumbyapplyingsuitablesubsidies,whichcanbecomputedeveninthecasewithlocationexternalities.Thisallowustosplittheproblemintwoindependentsteps.First,wefindtheoptimalallocationforasocialobjectiveand,second,weusethefundamentalresulttoderivesubsidiesthatreproducetheoptimalallocationasamarketequilibrium.Thecomputationoftheoptimalallocationisobtainedfromaconvexoptimizationurbanplanningproblemapplicabletoawideclassofobjectivefunctions.Optimalsubsidiescanbeobtainedevenwhenthepolicymakerseekstoreducetheimpactofthepolicy,orfacesimplementationconstraints,e.g.,someagentsorzonescannotbesubsidizedortaxed,orinsomespecificzonesthebudgetforsubsidiesislimited.Asanexample,wesimulateasmallcitywhichaimsatimprovingsocialinclusion.Keywords:convexoptimization;landuseplanningproblem;locationsubsidies;urbansegregation.Sharingorsparing?OptimisingavirtualurbanregiontofosterbiodiversityandclimateregulationAuthor:NinaSchwarz(UniversityofTwente);FalkHoffmann(HelmholtzCentreforEnvironmentalResearchUFZ);MichaelStrauch(HelmholtzCentreforEnvironmentalResearchUFZ);SonjaKnapp(HelmholtzCentreforEnvironmentalResearchUFZ)Abstract:Similartothelandsparingversuslandsharingdebateforagriculturallandscapes,urbanscientistsdiscusstheprosandconsofdifferenturbanforms,i.e.thespatiallayoutofcitiesanduseintensities,underthetermsof“compactcity”or“smartgrowth”andurbansprawl.Whileacompacturbandevelopmentisoftenassumedasthegoaltostrivefor,morescatteredbuilt-upandgreenareasincitiescouldbebeneficialforsomeecosystemservicesprovidedbyurbangreenspaces,especiallyifcloseproximitybetweenresidentsandurbangreenspacesisrequired.Thus,inthispaperweoptimisethespatiallayoutofavirtualcityregardingtwoobjectives,namelybiodiversity,operationalisedas

speciesrichnessofvascularplants,andoneecosystemservice,localclimateregulation.Wederivedbothunderlyingmodelsfromempiricalfindingsandemployedamulti-objectivegeneticalgorithm.Inthispaper,wepresentpreliminaryresultsregardingthespatialarrangementofurbangreenspaces,low-densityresidentialandhigh-densityresidentialareas.Also,wediscusschallengeswefacedwhenchoosinganappropriatespatialextentandresolutionandwhensettingupsimplemodelstobeusedduringoptimisationbasedoncomplexresultsfromempiricalstudies.Keywords:optimisation;biodiversity;climateregulation;virtuallandscapeArtificialintelligenceapproachestotrackingtheOpioidCrisisAuthor:Fox(IndependentConsultant)Abstract:PendingKeywords:BayesianAlgorithmtoEstimateTravelTimesinRoadNetworksAuthors:JorgeA.Garcia(UniversityofWaterloo);K.Ponnambalam(UniversityofWaterloo)Abstract:TheuseofAdvanceTravellerInformationSystems(ATIS)aimatimprovingtheperformanceofaroadnetworkaswellasimprovingdrivers’sdecision-makinginregardtorouteselection.Havingaccurateinformationaboutcurrenttraveltimesisessentialfordriverstodecidetheroutetofollowaccordingtotheirpreferencesandriskaversion.Inthisstudywedevelopedanalgorithmforupdatingstatesofroadsegmentsusingtheinformationofcurrentvehiclestravelingontheroadnetwork,suchalgorithmincludesadiffusiontermtoaccountforthedeteriorationofinformationovertime.DriversaresuppliedwithestimatesfromtheBayesianalgorithmandtheychoosetheroutetofollowbyoptimizingautilityfunctionthatincludesindividualandsocialgainandariskaversioncomponent.Wecomparedtheresultsfromthesimulationagainstsolutionsobtainedfromanoptimizationmodelofthesamesystem.WealsomeasuredtheefficiencyachievedintheroadnetworkbyusingtheBayesianalgorithm.Finallywecreatedandtestedanonlinelearning

Session5A:Agent-BasedModelsofHousingOrganizer:YuHuangJune22,2018;10:20AM-12:20PMLocation:

Theimpactoftransportinfrastructureonhousingmarkets:Anagent-basedmodellingapproachAuthors:A.Carro(UniversityofOxford);D.Farmer(UniversityofOxford)Abstract:Inthiscontribution,westudysomeofthemainchannelsandmechanismsbywhichinfrastructurehasanimpactontheeconomy.Inparticular,wefocusonitseffectonhousingmarkets,housingbeingthelargestassetclassintheeconomyaswellasoneofitsmaindrivers.Tothisend,wedevelopaspatialagent-basedmodelofthehousingmarket,basedonadiscretesetofdistrictsor

locations,eachofthemcontaininganon-spatialhousingmarketmodel.Thisnon-spatialmodelsimulatesalargepoolofhouseholdswithrealisticdemographiccharacteristics,life-cycles,andhousingdecisions.Amongotheraspects,thedynamicsofboththeownershipandtherentalmarketaremodelledindetail,includingthebuy-to-letsector.Furthermore,householdsareallowedtomovebetweenthesedifferentlocalmarketsinordertoaccommodatetheirdifferentbudgetconstraintswhiletryingtostayclosetotheirjoblocations,thusembeddingthenon-spatialmodelsintoaspatiallydistributedcommutingnetwork.Asacasestudy,themodeliscalibratedagainstalargesetofUKmicrodatafromhouseholdsurveysandhousingmarketdatasourcessuchastheFinancialConductAuthority's(FCA)loan-levelProductSalesData(PSD)andtheWhenFresh/Zoopladataonrentallistings,aswellasconfidentialdatafromtheBankofEngland.Oncethesemicrodatasourceshavebeenusedtofine-tunehousehold'sindividualcharacteristicsandbehaviours,amacro-calibrationisperformedtoensureconsistencywithdifferenteconomicaggregatesandhousingmarketcoreindicators.Keywords:agent-basedmodelling,spatialeconomics,housingmarkets,transportinfrastructure,commutingAnOverviewofILUTEin2018Author:JamesVaughan(UniversityofToronto)Abstract:ILUTE(IntegratedLandUse,TransportationEnvironment)hasbeenindevelopmentbytheUniversityofTorontooverthepasttwentyyears.ThegoalofILUTEistoprovideurbanplannerswithintheGreaterTorontoandHamiltonAreawithatoolthatiscapableoftestingdifferentlong-termpolicypositionsrelatedtolanduseandtransportationinfrastructure.ILUTEhistoricallyconsistedofseveralprimarymodels:Birth/Death,Marriage/Divorce,Migration,Dwellingconstruction,andDwellingownership.For2018,wearefocusingonthedevelopmentoftwocomponents,animproveddwellingconstructionmodel,andtheintegrationoftheoperationalAgent-BasedUrbanTravelDemandModel,GTAModelversion4.Thisworkwillhelpprovidebetterexplanatoryvariablesandallowforbetterspatialbehaviourfortheagentsandprovideinformationaboutgreenhousegasemissions.Agent-basedhousingmarketsinfaceofnaturalhazardsAuthors:TatianaFilatova(UniversityofTwente;UniversityofTechnologySydney);KoendeKoning(UniversityofTwente),OkmyungBin(EastCarolinaUniversity)Abstract:About80%ofpopulationistoliveincitiesby2050accordingtotheUNprojections.Manyofthesecitiesarewithincoastalzoneorwithinriverfloodplains,drivenbyagglomerationforcesclosetohistoriclocations.Yet,withclimatechangeprojectionsthesedevelopmentsbecomemorepronetonaturalhazards,whicharegettingmoresevereandfrequent.Combinedwithrapidurbanization,thisleadstoincreasingrisksforsuchalong-termassetashousing.Notably,floodzonesareusuallyspatiallycorrelatedwithrichenvironmentalandurbanamenities.Individualpreferencesforlocationsplayacrucialroleintheformationofspatialpatternsandpricesinurbanlandmarkets,andconsequentlypotentialdamages.Weapplyanempiricalagent-basedhousingmarketmodel,RHEA,tostudyhowfloodriskscapitalizeinhousingprices.Inparticular,wemodelabilateraltradebetweenindividualheterogeneousbuyersandsellersofresidentialproperties.Buildinguponpreviousresearchonagent-basedmodelingofurbanlandmarkets,wemakeastepforwardtowardsempiricalmodelingbyusingactualhedonicanalysisandspatialdataforflood-proneareasinNorthCarolina,USA.Ouragentsdifferinincomesandbehavioralassumptionsregardingriskperceptions.Weexplicitlymodeladaptivepriceexpectationswithinaspatialagent-basedmodelusingactualhousingtransactionsdata.Inthistalkdiscussanumberofmethodologicalstepsrequiredtomovefromstylizedtoempiricalagentmodelofahousingmarket.

Keywords:hedonicanalysis,agent-basedmarket,riskperceptionExploringdistributionalinfluencesonandeffectsofdynamicadaptivepolicypathwaysforrepeatedcoastalhazardsAuthors:NicholasMagliocca(UniversityofAlabama);MargaretWalls(ResourcesfortheFuture)Abstract:Theeconomiccostsofcoastalhazardshavebeenincreasingfordecades,andfutureprojectionsareonlyexacerbatedbycontinuedcoastaleconomicgrowthandfrequencyand/orseverityofcoastalhazards.PolicyinterventionsintheUnitedStatestodiscouragecoastaldevelopment,preserveorreclaimcoastalhabitat,and/ordecreaseregionalvulnerabilitytosubsequenthazardshavebeenlargelyineffective.Theurgentneedformoreeffectiveadaptationpolicyhasshiftedthequestionfrom“whatisthebestpolicy”to“whatisthebestcombinationandsequenceofpolicies”–ashiftcapturedbytheincreasinglypopularconceptofadaptivepolicypathways.However,accountingforthedynamicvaluesandbehaviorsofheterogeneouscoastalresidentsthataffect,andareinturnaffectedby,coastaladaptationpoliciesremainsachallengeforthedesignandanalysisofadaptivepolicypathways.Wepresentanagent-basedmodel(ABM)ofastylizedcoastalregionexperiencinguncertainincreasesinstormfrequencyandseverity,exogenouspopulationgrowth,andendogenoussocio-economicchange.TheABMisusedtoinvestigatehowthesocio-economiccharacteristicsandriskperceptionsofacoastalpopulationchangeovertimeinresponsetocoastalhazardsandalternativeadaptationpolicies.Therelativecostsandbenefits,effectiveness,andunintendedconsequencesofasetofplausibleadaptivepolicypathwaysthatfocusonlanduseincoastalareasareevaluatedandcompared.Whilethismodelimplementationisstrictlyexploratory,weillustrativeitsusefulnessforisolatingandidentifyingkeytrade-offsandspecificpathwaysamongpolicyoptionsbyaccountingfortheinherentlydynamicandheterogeneousbehaviorsofcoastalresidents.Keywords:Land-usechange,housingmarkets,landmarkets,computationaleconomics,computationalsocialscienceModelingBuy-SellerinHousingMarket—ABiddingNetworkPerspectivetoUnderstandMarketMechanismAuthor:ZiyeZhang(CornellUniversity)Abstract:Thispaperproposesanagent-basedmodel(ABM)forhousingmarketsincludinganewtypeofhouseholdswhoarebothbuyerandselleratthesametime.Traditionally,economicmodelstreatbuyersandsellersinadichotomicmanner.However,housingmarketswitnessmanyhouseholdsbuyandsellatthesametime(called“buy-seller”).AccordingtomyhousingbuyersurveyinBeijing2016,45%are“exchangebuyers”,whoaresellingahousingunitwhilepurchasinganewone.Theessentialfeatureofthebuy-sellersistheinterdependencebetweenbuyingandsellingdecisions.Asabuyer,anewpurchasedependsonasuccessfulsaleduetobudgetandpolicyrestrictions;asaseller,asalemayrelyonasuccessinbiddingtoavoidrentingforliving.Thisinterdependencegeneratesabiddingandtransactionnetwork,throughwhichonehousehold’ssuccessorfailureinbiddingorsellingwilltriggerarippleeffectandinfluenceallotherconnectedhouseholds.Thispaper,forthefirsttime,proposesanABMtomodelbuy-selleragentsandthebiddingnetworkmechanismofurbanhousingmarkets.Onechallengeistoidentifythelooppattern—oneofthetwopotentialtransactionpatternsinthenetwork—inABM.ThispaperprovidesanewalgorithmforthisidentificationandmakeitpossibletorunABMwithbuy-sellers.Purchaserestrictionpoliciesareinterpretedasashockconvertingsomepurebuyersintobuy-sellers,anddirectandindirectinfluencesareexaminedintheABM.Keywords:Buy-Seller;MarketMechanism;BiddingNetwork;HousingPurchaseRestriction;Agent-basedModel

Unfoldinghousingmarketcomplexity:empiricalhousingmarketanalysisandspatialagent-basedmodelingAuthors:YuHuang(UniversityofWaterloo);DawnParker(UniversityofWaterloo)Abstract:ThehousingmarketinKitchener-Waterloo(KW)hastakenasurprisingturn,witha20.7%averagesalespriceincreasefrom2016to2017.Whathasledtothehousingpricevolatilityandpre-LRTspeculationintheregion?Howhavechangingdemographicsandlocationchoicepreferencesinfluencedthemarketinthisregion?Thisstudyaimstointerprettherecentmarketdynamics,unpack,ifpossible,thedominantfactors,andbuildanempiricalgeo-computationalhousingmarketmodel.Incontrasttoconventionalaggregatedmarketanalysis,thisstudytakesindividualpreferencesforhousingandresidentialchoicebehaviourintoaccount,soastoexplainthemarketfrombottomup.Accordingly,twoapproachesareproposedinourresearch,1)acomprehensivehousingsurveyandanalysis;and2)agent-basedhousingmarketmodelling.ThehomebuyerandsellersurveyhasbeendistributedtohomemoversinKWandreceived506responsesfrom06/2015to04/2017.Takingadvantageofourdetailedmicro-datafromthesurvey,weproposetoestimatetheheterogeneouspreferencesamongdifferenthouseholdsandtheirassociatedwillingnesstopay(WTP)forthehousesbyademandanalysis,adaptedfromBajariandKahn(2005).Wethenpresentsimulationsthathighlighttheadded-valueofanagent-basedmodelingapproach,intermsofstrongtheoreticalandempiricalmicrofoundationsforthebehavioralrulesofactions,interactionsandlearningofheterogeneousagents.Growinghousingmarketsfromthebottomupenhancesourunderstandingoftheircomplexdynamicsandofferstoolsforthedesignofsustainableurbandevelopmentstrategies.Keywords:housingmarket;agent-basedmodelling;computationaleconomics;complexity

Session5B:Agent-BasedModelsofRetail,OfficeandIndustrialMarkets,andIntegratedModelsOrganizer:YuHuangJune22,2018;3:00-5:00PMLocation:

Fromoilwealthtogreengrowth-Anempiricalagent-basedmodelofrecession,migrationandsustainableurbantransitionAuthors:JiaquiGe(TheJamesHuttonInstitute);GaryPolhill(TheJamesHuttonInstitute);TonyCraig(TheJamesHuttonInstitute);NanLiu(TheJamesHuttonInstitute)Abstract:Thispaperdevelopsanempirical,multi-layeredandspatially-explicitagent-basedmodelthatexploressustainablepathwaysforAberdeenCityandsurroundingareatotransitionfromanoil-basedeconomytogreengrowth.Themodeltakesanintegrated,complexsystemapproachtourbansystemsandincorporatestheinterconnectednessbetweenindividuals,households,businesses,industriesandneighbourhoods.Wefindthattheoilpricecollapsecouldpotentiallyleadtoenduringregionaldeclineandrecession.Withgreengrowth,however,thecrisiscouldbeusedasanopportunitytorestructuretheregionaleconomy,reshapeitsneighbourhoods,andredefineitsidentityintheglobaleconomy.Wefindthatthetypeofthegreengrowthandthelocationofthenewbusinesseswillhaveprofoundramificationsfordevelopmentoutcomes,notonlybydirectlycreatingbusinessesandemploymentopportunitiesinstrategicareas,butalsobyredirectinghouseholdsandservicebusinessestotheseareas.Newresidentialandbusinesscentresemergeasaresultofthisprocess.Finally,wearguethat

industries,businessesandthelabourmarketareessentialcomponentsofadeeplyintegratedurbansystem.Tounderstandurbantransition,modelsshouldconsiderbothhouseholdandindustrialaspects.Keywords:spatialagent-basedmodel;recession,migration;urbantransition;oilcrises;sustainabilityModellingfuelpovertyandmarketfailuresAuthors:GaryPolhill(TheJamesHuttonInstitute);JiaqiGe(TheJamesHuttonInstitute);TonyCraig(TheJamesHuttonInstitute);DougSalt(TheJamesHuttonInstitute);KathrynColley(TheJamesHuttonInstitute)Abstract:FuelpovertyhasbeendefinedinScotlandasasituationinwhichmorethan10%ofhouseholdincomeneedstobespentonallfuelusetokeepthehouse‘acceptably’warm(basedonBoardman1991).Buildingfabricisamajorcontributingfactortomaintainingacomfortabletemperatureinthehome.Thehousingandhomeimprovementmarketsthusplayanimportantroleinamelioratingfuelpoverty.HousesinScotlandhaveanEnergyPerformanceCertificate,givingthemaratingusingsevenbandsfromA,themostenergyefficient,toG,theleast.Inworkcurrentlyunderreview,Liuetal.haveshownthatinAberdeenshire,moreefficienthomesdoattractapremiumontherentalprice.Grantsandinterest-freeloansareavailableforhomeimprovementsthroughgovernment-fundedschemes.Thoughaimedprimarilyathomeowners(someofwhomarefuelpoor),someprivatelandlordsareeligible;therearealsoschemesthattenantscanapplyfor.Further,energycompanieshaveobligationstoprovidefreeinsulationandheatingimprovementstoqualifyinghouseholds.Manysuchschemesarenottakenupbyhouseholds,however,andthisisacauseforconcern.Anothermajormarketisdomesticenergysupply.Here,low-incomefamiliesareatadisadvantageasmanyareonprepaymentmeters.Tariffsforthesearetypicallyhigherthanoncreditmeters,solow-incomehouseholdsgenerallypaymoreperunitofenergyconsumedthanbetter-offhouseholds.Understandingfuelpovertyrequiresmodellingthatdoesjusticetothecomplexitiesofinteractingmarketsandmultidimensionalprioritiesoftheactorsinthem.Keywords:Fuelpoverty,complexity,agent-basedmodellingDrawingonanalogiesfromecologytomodelretailcompetitionusingABMAuthor:DerekRobinson(UniversityofWaterloo)Abstract:Agent-basedmodels(ABM)havebeenusedtorepresentandformalizeourknowledgeofthedriversofland-useandland-coverchangeoverthepasttwodecades.Mostofthesemodelsrepresentresidentialorfarmhouseholds,whichislikelyduetotheavailabilityofsurveydataorthepotentialtosurveyhouseholdsabouthowtheymakesettlementorcropchoiceandlandmanagementdecisions.Fewermodelsexistthatrepresentactorsthatdevelopland.Amongthosethatexist,thefocushasbeenondevelopingresidentiallandsandinvolvedstatisticalmodels(e.g.,survivalanalysis),withacouplecaseswheresurveysofdeveloperactorshavebeenusedtoinformagentbehaviours.AnotheruniquelydevelopedABMhasbeenusedtoinvestigatepetrolpricingcompetitionamongpetrolservicestations.Despitetheselatterexamples,thecreationofABMstorepresentcommercialandretaildevelopmenthavebeenunderrepresentedgiventheirinfluenceonurban-to-exurbanmorphologiesasemploymentlandsandadestinationforcommerce.WepresenttheresultsofempiricalresearchonconsumerspendingandretaillocationsiteselectionasthefoundationforthedevelopmentofanABMofretailcompetition.Weusetheseinsightsincombinationwithecologytheoryaboutcompetitiontocreateaconceptualmodeloftheprocessesandbehavioursofretailagentscompetingforstoresales.Theoverarchinggoalofthepresentedresearchistoevaluateifreal-worldpatternsofstorelocationscanbegeneratedusinganABMapproachsothatwecanenrichfutureland-useandland-coverchangemodelsbybetterrepresentingcommercialdevelopment.

TowardsanAgentBasedModelofDowntownOfficeMarketSupply-DemandDynamicsAuthors:A.Volz(UniversityofIllinoisatChicago);M.Zellner(UniversityofIllinoisatChicago),R.Weber(UniversityofIllinoisatChicago);J.Kim(UniversityofIllinoisatChicago)Abstract:Agent-basedmodels(ABM)ofrealestatepredominantlyanalyzetheresidentialmarket,withthe2008recessionstimulatingsignificantinterestinthearea.Lessattentionhasbeenpaidtothecommercialofficemarket,asthebustsofthelate80’spredateABMasananalytictool.However,thechangingstructureofoffice-usingfirms,theresurgenceofcentralcities,andthecontinuinggrowthoftheserviceindustrypresentfutureuncertaintyinthemarketanddemandnewattention.Inthispaper,wepresentanABMofametropolitanarea’sofficemarket:OMITD(OfficeMarketInternalTransitionDynamics).Themodelpositsmechanismsunderlyingsearchbehaviorsofcommercialtenantsfornewspace(adaptingtoavailabilityandrent)andtheresponsesfrombuildingmanagerstotheirobservationsofthemarket(adjustingrenttoindividualandmarketvacancy,developingordemolishingbuildings).Themarketisdrivenbyanexogenouslygeneratedlaborforce.Theinteractionbetweentenantsandbuildingmanagersoverrelativelyshortdurations(64-80quarters)resultsinoutcomesresemblingametropolitanarea’sofficemarketrentcycles,distributionsofestablishmentsbysize,anddistributionsofbuildingsbyclass.Withconfidencethatourmodelreproducesthemarket’smacro-levelstructurefromthesemicro-levelinteractions,weusethemodeltoexaminephenomenathathavereceivedlittleattention,includingfiltering,wheresmallerfirmsmoveuptheofficequalityladder.WefindthatOMITDprovidesasuitabletestbedforexploringthemarketandpolicyimplicationsofbehavioralassumptionsaboutagents,andgeneratesreasonablesimulateddataoftheinternaldynamicsofanofficemarket.Keywords:officemarkets,agentbasedmodels,filtering,priceadjustmentprocesses,firmlifecyclesExploringtheeffectsofpolicyinstrumentsonurbansprawlandincomesegregationAuthors:ChengGuo(ChengGuo,HelmholtzCentreforEnvironmentalResearch,UFZ);NinaSchwarz(UniversityofTwente)Abstract:Differentpolicieshavebeenimplementedindifferentcitiesforcontrollingurbansprawl.Thesepolicieshavebeenclassifiedintothreecategories:1.regulation,2.economicintervention,3.institutionalchange,managementandadvocacy.Whileithasbeenshownthattheycancontrolurbansprawl,fewstudiessofarhaveinvestigatedtheireffectsontoincomesegregation.Forthispaper,weimplementedseveralpolicyinstrumentspercategoryinastylizedurbanagent-basedmodel(ABM)withindividualbehaviorsbuildingonAlonso’sbidrenttheory.Foreachpolicyinstrument,weransimulationswithdifferentincomedistributions.Wemeasuredtheoverallemergentpatternswithandwithoutpoliciesusingindicatorsforurbansprawlandincomesegregationandcomparedthesepatternsqualitativelyandquantitatively.Simulationresultsconfirmedpositiveeffectsofthesepoliciesontourbansprawl,butalsorevealedtheir–insomecasescounterproductive-effectsontoincomesegregation.Ofalltestedpolicies,homogenousdensitycontrolistheonethatmosteffectivelycontrolsbothurbansprawlandincomesegregation.Whilegivingpracticalsuggestionstopractitionersabouturbanpolicies,thisstudyalsodemonstratesthepotentialofABMsforpolicydesignKeywords:agent-basedmodel;urbansprawl;segregation;policy,residentialmobilityUrban,agriculturalandtouristicallandusepatterns:combiningspatialeconometricsandABM/LUCCAuthors:ClaudioDetotto(UniversitédeCors);CorinneIdda(UniversitédeCors);EricInnocenti(UniversitédeCors);DominiquePrunetti(UniversitédeCors)Abstract:ThisworkdealswithcomputersimulationofcomplexspatiotemporalsystemsbasedonAgent-BasedModelsofLandUseCoverChange(ABM/LUCC).Weproposeamodelingframeworkanditsmodularandscalableimplementationinordertosimulateandevaluateterritorialplanningpolicies.The

settingofthisstudyisacollectionoftouristareasthathavefaced-andstillface-anintenseresidentialdevelopmentleadingtoahugepressureonlandpricesaswellastoland-useconflicts(localresidentialmarket,touristrentalinvestment,agriculturalproduction).Theaimistoidentifynewmanagementpractices,abletocombineeconomicdevelopmentandlandcompetitioninastronglyconstrainedenvironment.Bymeansofcomputermodels,weintendtorepresentthebehaviorofheterogeneouseconomicagentsinbothtimeandspace.Existingspatialterritorymanagementmodelsaremostlyanalyticallyintractable.Discrete-eventsimulationmodelingofferspracticablesolvingtechniques.Inparticular,itallowstoprocessABM/LUCCmodelsandtodealwithheterogeneousbehaviourshapedbymanyinteractingcomponents.InthisworkweshowhowtocombinespatialeconometricswithABM/LUCCcomputersimulation.Furthermoreweexplainhowtoobtainparametersestimationabout:theirdistributionoverspaceandaccordingspatialeconomicactivities;pricedeterminantsoflandandrealestate;Thepredictiveparametersarecomputedfromrealdataaswellasourdatasimulations.Keywords:ABM/LUCC,spatialeconometrics,cellularmodels,complexspatialsystems

Session7:Measuring,modellingandinterpretingscaling/powerlawsinurbansystemsOrganizer:FatemehJahanmiriJune21,2018;3:00-5:00PMLocation:Combiningpolycentricityandurbanscalingtoexplainsocio-economicvariablesAuthors:AminKhiali-Miab(PLUSZurich);MaartenJ.vanStrien(PLUSZurich);KayW.Axhausen(PLUSZurich)andAdrienneGret-Regamey(PLUSZurich)Abstract:Recentstudiespredictthat75percentofpeoplewillbelivingincitiesby2030(TriantakonstantisandMountrakis2012).Thisfasturbanizationprocessandanincreasingpercapitautilizationofresources,raisesseriousconcernsabouttheresilienceandfutureperformanceofsocio-economicsystems.Inordertoensuresustainablegrowthofsettlements,requiresagoodunderstandingofthewayinwhichsettlementpatternsaffectsocio-economicprocesses.Thisknowledgewillallowustopredictthefutureperformanceofsettlementareas(Czamanski2008,Ernstson,vanderLeeuwetal.2010,Grêt-Regamey,Celioetal.2013).Meijers(2006)andBertaudandMalpezzi(2003)showhowstudyingthespatialstructureofsettlementnetworkscanrevealtheeconomicperformanceandtransitionsofthesenetworks.Ingeneral,itisbelievedthatsettlementnetworksareformedbythecombinationoftwoprocesses.Thefirstprocessisthebottom-upself-organisationofsettlementsystemswhichresultsintheuniversalscaling-lawinsettlementsystems.Thesecondprocessisthetop-downregulationswhicharesetbyregulations,policiesorenvironmentalconditionsofaregion(StranoandSood2016).Alargeamountofresearchhasbeendonetounderstandbottom-upself-organisationofsettlementsystems(Lämmer,Gehlsenetal.2006,Deville,Songetal.2016).Basedonthesestudiesaunifiedtheoryofurbanscalingcanbesuggested(BettencourtandWest2010).However,deviationsfromtheuniversalscaling-lawinsettlementareashavebeenreported(Bettencourt,Loboetal.2010,StranoandSood2016).Thereasonforthesedeviationsisstillanopenquestioninthisresearcharea.Theoreticalandempiricalstudiesinthefieldoflandscapeplanningshowedthatpolycentricty,whichreferstotheexistenceofmultiplecentersintheorganisationofsettlementareas,isassociatedwiththehigherefficiency(MeijersandBurger2010),increasedterritorialcohesion(González-GonzálezandNogués2016)andconsequentlythehigherperformanceofsettlementareas.Polycentricityhasbecome

anormativeplanninggoalinmanytop-downspatialdevelopmentplansatglobal,Europeanandnationalscales.Recentadvancesincomplexnetworksciencecanprovideindicatorstoassesspolycentricnetworkstructures.Forexample,inpreviousresearchwehaveshownthatthestructuralhierarchyindicators(e.g.GRC:Globalreachingcentrality)isagoodapproximatemeasureofpolycentricityinsettlementnetworks(Khiali-Miab,vanStrienetal.2017)HereweshowthatGRCcanalsobeusedtoexplaindeviationsfromthescalinglaw.Makinguseofalinearregressionmodel(R2=0.77),weshowthatthecombinationofscalinglawandhierarchicalstructureofsettlementnetworkscanbeusedtopredictthemedianincomeinsettlementareasinSwitzerland.Thesefindingsprovidespatialplannerswithquantitativemethodsthatcanaidthemtodeterminewhethersettlementdevelopmentmovestowardsasocio-economicallyefficientstate.Urbanscaling:ABayesianpaneldataapproach Authors:CarloBottai(UniversityofTurin)andConsueloR.Nava(UniversityofAostaValley)Abstract:Theroleofcitiesforsocio-economicdevelopmenthaveattractedanincreasingpopularityinrecentyears,sothaturbanscalingisbecomeaurgenttopicofanalysis.OurcontributionfocusesontheEuropeanOECDFUAsdevelopment[BettencourtandLobo,2016].Weestimatetheirnonlinearagglomerationandscalingeffectsbycomparingtheirpopulationsizewithkeyaggregatevariables,likeurbanizedareasize,numberofgrantedpatents,totalemploymentlevelandGDP.Indeed,severalcontributionshavecharacterizedcitiesastheemergentpropertyofaComplexAdaptiveSystem.Accordingly,therelationshipbetweentheirsizeandmanymacroscopicvariablesturnsouttobedescribablebypower-lawdistributions[BettencourtandWest,2010].Thissuggeststhatgeneralscale-freelawsmaygoverncitiesagglomerationdynamics,whilegeographical,historicalandculturalfeaturesplayonlyasecondaryrole[Bettencourtetal.,2010].Comparedtothepreviousliterature,inthepapertemporaldynamicsandBayesianmethodsare,toourknowledge,jointlyexploitedforthefirsttimeinthisframework,inordertoobtainmorerobustestimatesandpolicyimplications.Weassessscalingparametersconsistencyacrossnationsandovertime,bytakingadvantageofapaneldataapproach.Inthatway,weovercomesmallsamplesizedrawbacks,alsoobtainingmoreaccurateestimates.Wesuitablyspecifytheeconometricmodeltodealwithvariableendogeneityandweaddtime-specificrandomeffects,modeledwithaBayesianmethod.Bothcity-specificfixedandrandomeffectsareconsideredasawaytoestimatetheunder-orover-performancesofeachmetropolitanarea,relativetoexpectationsfortheirsize.Keywords:Urbanscaling;Paneldata;Bayesianinference;EuropeanmetropolitanareasFractaldimensionasobjectivefunctioningeneticalgorithms,towardsanewarchitecture Author:JohnDriscoll(PortlandStateUniversity)Abstract:In2011,theUnitedNationsreportedthat3.6billionpeoplearelivinginurbanareas,andthisisexpectedtogrowto6.3billionpeopleby2050(WorldUrbanizationProspects,2012).Limitstogrowthareinevitableasthecarryingcapacityoftheplanetistestedbeyondanythingwehaveseenbefore.Manyindicatorssuchaspeakoil,drought,climatechange,topsoildepletion,biodiversityloss,oceanacidification,andsoon,arehavinganeffectonsocietiesworldwideandespeciallyonthethirdworld.Citiesinasmuchastheyrepresenttheproblemarealsoapotentialsolutioninmanyways.Researchintothescienceofcitieshasshownthatdenselypopulatedurbanenvironmentsaregenerallymoreefficientthelargertheybecome(West,Bettencourt,2010).Atheoryastowhythisistruerelatestofractalgeometryandspacefillingcharacteristics.Akeyquestionis,canarchitectsandplannersleveragethisimportantaspectofurbandynamics?Thisquestionisnotsimplyhowcanweapplysciencetoarchitecture,forinstanceusingthepowerlawsweobserveincitiestoimprovethewaywedesign

buildingsbutrather–howcanwedothisinafieldincreasinglydominatedbyartificialintelligenceandretainthespiritofarchitectureandthecreativityofthedesigner?Thispaperdevelopsandtestsadesignprocessintegratingfractalgeometryasameanstoproducehigherqualitydesigns.Theresearchdevelopsageneticalgorithmtoselectforgeometriesthathavehigherfractaldimensioninsuccessivegenerationsofdesignvariants.Afteranumberofconverginggenerations,thehighestfractaldimensiongeometrieswereselectedandusedasschematicdesignimpetusesinahuman-centricdesigncharretteandthenreintroducedasbaselinesforadditionalGAruns.Resultsofthisstudyarepresentedgraphicallyanddiscussed.Keywords:Fractals,GeneticAlgorithm,UrbanPlanning,ArchitectureAmicroeconomicexplanationofcities’scalinglawsAuthor:FranciscoJMartínez(UniversityofChile)Abstract:Theempiricalevidenceofscalinglawsincitiesasuniversalregularity(Bettencourt,etal.,2007,2008)hasbeenfollowedbyseveralmodelssearchingforatheoreticalexplanationofthisphenomena,mostthembasedonmodelsfromphysics.InMartínez(2016)Ishowthatthislawemergesforlandrentsfromthemicroeconomicassumptionofrationalagentswithstochasticbehavior.Inthisresult,theextremevaluedistributionisakeyassumption,whichistheoreticallyjustifiedinthecontextofautilitymaximization.Inthiswork,thisinitialresultisfurtheranalyzedboththeoreticallyandonitsimplicationsforcitiesevolution.Moreover,anewresultistheemergenceofscalinglawsintheproductionofgoods&services,regardingtheindustrytypedefinedbyitsreturnstoscale,andinthelabormarket.Thesetheoreticalresultsconsideropencitiesinasystemofcities(e.g.acountry),wheremigrationleadstomarketequilibriuminthewholesystem.Insum,thisworkusesmicroeconomicprinciples–individuals’behaviorandmarketequilibrium-andextremevaluedistributionsofbehavior,tobuildatheoreticalmodelofthecitieslanduseconsistentwiththeemergenceofscalelawsonitseconomicvariables.Buildingapolycentriccityfromthebottomup:Anagent-basedmodelingapproachAuthors:FatemehJahamiriandDawnParkerDespitetheirdifferences,citiessharebasicpropertiesthatconnecttheirbuildingblocksinapowerlaworder. Mathematical frameworks have been proposed to explain the origin of these regularities.However,theunderlyingchainofprocessesthatisresponsibleforthesepatternsisyettounveil.Inourpreviouswork,wehavedevelopedanagent-basedmodelthatgeneratespower lawdistributedurbanblocks from very basic dynamics. The model suggested that the complexity of urban physical formemerges from very basic human interaction processes that operate locally. In the present research, Iextendourpreviousworkbymodelingendogenousevolutionofurbandensityandthebehaviorofthemodel based on aggregation and segregation parameters. The simulation results indicate thatpolycentric patterns emerge in model landscape with spatial and statistical fractal properties. Theresults contribute to the theory of urban complexity by providing a possible explanation for thepolycentricstructureincitiesandthelinkbetweenthepowerlawsocialandspatialprofiles.Keywords:Urbanform,powerlawsizedistribution,fractal,agent-basedmodel,polycentricity.

Session8:IdentifyingtheModellingNeedsofPolicyMakersandPractitionersOrganizer:DawnParkerJune21,2018;10:20AM-12:20PMLocation:

ContemporaryChallengesatMPAC:Equitabledataacquisition,maintenance,andapplicationAuthors:RusselEnns(MPAC)andRobertBabin(MPAC)Abstract:TheMunicipalPropertyAssessmentCorporation(MPAC)representsthelargestpropertyassessmentjurisdictioninNorthAmerica,responsiblefortheclassificationandvaluationofoverfivemillionpropertiesacrossOntario.Theabilitytodevelopwell-performingvaluationmodelsishighlydependentonthequalityofdatathatgoesintothem.OneofthelargestchallengesatMPACismaintainingexistinginternaldataandestablishingprotocolsforthesystematicingestionandequitableapplicationofnewdatasources.MPACmaintainsthemostcomprehensivedatasetofpropertycharacteristicsintheprovince;however,theabilitytocaptureandvalidatedata,especiallypertainingtotheinteriorofbuildings,islimitedbytheincreasingdesireofhomeownersforprivacy.Whiletheamountofopendataprovidedatthemunicipallevelhasbeenincreasingovertime,thequalityandavailabilityofthesedataarenotstandardacrosstheprovince.ToensurethatvaluationatMPACiscompletedequitably,datamustbecollectedinasystematic,standardizedfashionwithongoingfeedsofupdatesasthedatachanges.Keywords:PropertyAssessment,Valuation,Data,EquityMonitoringChangeintheCentralTransitCorridor(CTC):Author:TristanWilkin(RegionofWaterloo)Abstract:In2011TheRegionofWaterloocommittedtoimplementLightRailTransit(LRT).Fromthebeginning,theRegionhasrecognizedtheimportanceofmonitoringchangeintheCentralTransitCorridor(CTC)overtime.ION,thenewrapidtransitsystem,willdomorethanjustincreasetransitaccessthroughouttheRegion.Itcreatesanopportunitytobuildhealthyandvibrantcommunitiesalongtheroute.TheCTCmonitoringprogramwillmonitorthecorridorfromthebaselineyearof2011untilatleast2021,afterIONhasbeenconstructed,openedforservice,andisfunctioningwithinthecommunity.WithanobjectivetoquantifyION’sbenefitsinmovingpeopleandbuildingcommunitywithintheCTC,sixteenbaselineindicatorswereselectedand,wheredatapermits,aremonitoredannually.ThemedindicatorsareselectedeachyeartoprovideamorecomprehensivelensformonitoringCTCactivitybypresentingselecteddimensionsinamorerobustway.TheRegionofWaterlooenteredapartnershipwiththeUniversityofWaterlootoengageinathree-yearstudytoquantify,monitorandmeasuresocioeconomic,andcore-areaintensificationimpactsofIONontheCTCinordertoexploreandunderstandthecausaldynamicsofthesechanges.Theresearchintendstolinkconnectionsbetweenindicatorsofeconomicsuccessandsocialequitytosocio-economicandurbaninfrastructurechangesintheRegion.ThekeyoutcomeoftheUWresearchers’contributiontotheprojectwillbeanimprovementontraditionalassessmentsofpublicinterventionsandquantifiedcorrelationsamongstconditionsbeforeandaftergovernmentactions.Keywords:monitoringchange,transitcorridor,intensificationKitchenerInnovationHubAuthor:KarlAllen-Muncey(CityofKitchener)Abstract:Pending

Session9:LearningSession-DataVisualization ExploringFrontiersofHighResolutionDynamicSpatialModelingtoSupportDecisionMakinginComplexUrbanSystemsPresenters:TimonMcPhearson(TheNewSchool);DanielSauter(TheNewSchool);CarsonFarmer(UniversityofColoradoBoulder);RocioCarrero(TheNewSchool)andJaskiratRandhawa(TheNewSchool)SessionDescription:InthisLearningSessionweaimtoigniteaconstructiveandengagingdiscussiononthecurrentfrontiersandchallengesondevelopingusableandtransferableHighResolutionDynamicSpatialModelstosupportdecisionmakingincities.Workingontheinterfaceofgeo-computation,spatialmodeling,machinelearninganddatavisualization,ourteamhasdevelopedaflexiblemodelthatcanbeappliedtodifferenturbancontextsanddifferentpurposes(e.g.greeninfrastructureanalysis,vulnerabilitytoextremeevents,scenariossimulations).WehaveappliedourmodeltocitiessuchasNewYork(USA),SanJuan,PuertoRico,Valdivia(Chile)andHermosillo(Mexico)andco-developedscenariosthroughlocalcollaborationwithpractitionersandstakeholders.Intheprocesswehaveconfrontedand(tosomeextent)solvedmultiplechallengesfromdevelopingstakeholderengagementprocessesforscenariodevelopment,datalimitations,needforflexiblemodelingoptionsthatweproposetosharewithpeersinthisLearningSession.Specifically,weproposetocovertopicssuchas:data-drivenmodelsondata-scarcecities,prosandconsofmachine-learningapproaches,pseudo-customizablemodelingenvironmentsonline,integratingrealplanningintomodels,andapproachestovisualizingindicatorinteractionsanduncertainty.Wewillfacilitateaninteractivesessionwerewewillinviteparticipantstoexploretheaforementionedtopicswhilegeneratingadiscussiononhowmethodscanbesharedtowardsadvancingunderstandingofcomplexurbansystems.Insightswillbecapturedviasmart-phonessurveyssothesessioncollective‘knowledgeoutputs’canbesharedwiththeconferenceattendants.Weplanacollaborativepublicationasresultfromthelearninginthissession.

PosterContestOrganizers:WICIJune21,2018;5:00-6:00PMLocation:ASystems-OrientedVulnerabilityApproachtoFloodinginCave,WestmorelandAuthors:Tracy-AnnNicoleHyman(UniversityoftheWestIndies);DavidSmith(UniversityoftheWestIndies);LeonardNurse(UniversityoftheWestIndies)Abstract:TheCommunityofCaveisapproximately4.8km²andliesjustoffthesoutheastcoastofWestmoreland,Jamaica.Itisapproximately12kmfromtheparishcapital,Savanna-la-Mar,withmajoreconomicactivitiesnamelyfarmingandfishing.Likemanyislandeco-systems,Caveispronetotropicalcyclonesthatgiverisetoflooding.Ofspecialnoteisthe1979floodevent,whichcausedsignificantdamagetoterrain,disruptiontotransportationsystemsanddevastationtolivesandpropertyinWesternJamaica.TheparishofWestmorelandexperienced31ofthe41deaths,andmanypersonsweretrappedintheircommunities,unabletoevacuate.Stilltoday,therearelimitedrainfallgaugesandnofloodgauges,earlywarningsystemsnoranofficialevacuationshelter.Witha50-yearreturnperiod,anyrecurrenceofthistypeoffloodin2016,couldbeworsethanin1979.Theoverallaimofthisresearchwasthustoconstructabasicsimulationofa1979flood-likeeventassessingthevulnerabilityofstakeholderstofloodingin2016.Thisbasedongeographicalconsiderations,floodcharacteristicsand

themovementpatternsofresidents–analyzedthroughtheuseofAgent-BasedModels,GeographicInformationSystems,andimplementedwithNetlogosoftware.Preliminaryresultsrevealthatpossiblelossoflifetakesplaceoneachofthethreedayssampled,[1]withmostfatalitiesonaFriday.Theexpectedresultsfromthisresearchshouldguidelocalgovernmentintheappropriatecitingforevacuationshelters,safezones,alongwithimplicationsforearlywarningandlanduseplanninginSouth-EastWestmoreland.Keywords:Cave,flooding,agent-basedmodel,evacuationEstimatingVehicleEmissionsusingBigTrafficDataAuthors:AnjiLiu(UniversityofWaterloo);LipingFu(UniversityofWaterloo);MathewMuresan(UniversityofWaterloo)Abstract:Asclimatechangeandurbanairqualitybecomegrowingconcernsaroundtheworld,thereisgreaterneedtoquantifyvehicleemissions.Vehicleemissionsgenerateasignificantportionofgreenhousegases.Sincetheseemissionsareinfluencedbytrafficflowconditions,itisbeneficialfortrafficmanagementprofessionalstohavemethodstoestimateemissionsinrealtime.Unfortunately,directmeasurementofemissionsfromallroadvehiclesisbothcostlyandimpractical.However,emissionscanbeestimatedthroughtherelationshipsbetweenemissiongenerationandtrafficflow.Thisposterwillpresentamethodtoestimateemissionsinrealtimeusingbigtrafficdata.Trafficdatafromvideocameras,Wifidetectors,andloopdetectorsatintersectionsareusedtomodelthetrafficflowalongaroadcorridor.Anemissionmodelisdevelopedtousethetrafficflowasinputtoestimatetheemissionsgenerated.Newsourcesoftrafficdata,suchasvehicletrackingthroughvideocamerasusingimageprocessing,provideopportunitiesforimprovedtrafficflowmodellingand,subsequently,emissionestimation.Tocalibrateandvalidatetheemissionestimationmethods,apre-validatedsimulationapproachistaken.ThetrafficnetworkissimulatedinVissim,amicroscopictrafficflowsimulator,andcalibratedaccordingtothetrafficdata.OutputsofthesimulationareinputintoMOVES,anemissionsimulatordevelopedbytheEnvironmentalProtectionAgencyUS,togenerateemissionestimates.Theseresultsserveasthegroundtruthusedtoevaluatethereal-timeemissionestimationmethod.Keywords:emissionmodelling;trafficdata;trafficsimulation;trafficflowmodellingFractalcities:cellularautomataandurbanlandusepatternsAuthor:MartinPhamAbstract:Fractalsaremathematicalobjectsthatexhibitahighdegreeofself-similarityandscale-freecomplexity.Fractaldimensionisameasureofthecomplexityorspace-fillingcapacityofapatternacrossdifferentresolutionsofmeasurement.Cellularautomataareaclassofdiscretespatialmodelsusedtoconsiderevolutionaryphenomenonbasedonsimplerulesdeterminingthestateofeachcell.Presentedisanapproachtoinvestigatethegrowthofurbanformsandarisinglandusepatternsinmetropolitanareas.Amulti-statecellularautomatoncoupledtologisticpopulationgrowthisdevelopedtosimulatethespatialstructureofdifferenttypesofurbanlanduses.Therelationshipbetweenthefractaldimensionoftheurbanizedareaandspatialentropyisestablished,withimplicationsonhowtoapproachfractal-basedmodelling.Applicationstopolicy-makingandplanningarethendiscussedbyamotivatingexample:greenbelts,greenwaysandurbanopenspacearesimulatedfortwincityregionsinordertoinvestigateurbansprawlandedgecities.Finally,furtherimprovementsanddirectionsofresearchareexploredKeywords:fractaldimension,urbanlanduse,cellularautomata,spatialentropy

Session10:Workshop-AScienceofCitiesforSustainableDevelopmentOrganizer:PerinRuttonsha(UniversityofWaterloo)June22,2018;5:00-6:00PMLocation:Description:Citiesareincreasinglygarneringattentionontheinternationalpoliticalstage,inlightofthechallengesandopportunitiesurbanizationtrendsengenderforsustainabilityandresilience.Meanwhile,scienceofcitiesdiscourseismobilizingonthepremisethatitcouldleadtoaunifiedunderstandingofsustainability.WiththeUnitedNationsincorporatingacity-centriccategoryintothenewestSustainableDevelopmentGoals(SDGs),aunificationofpurposebetweensustainabledevelopmentandurbanreformisnascent.Thisgoal(Goal11)islistedasoneamongseventeen,thoughithasalsobeenacknowledgedthataconsiderablenumberoftheotherslinkdirectlywithorcouldbeimplementedthroughurbancontexts.Theambiguouspositionofcitieswithintheglobalsustainabilityproblemspaceisindicativeofamorecentralchallenge:Bothsustainabilityandcitiesareimpactedbyandarisefromdynamic,interactive,cross-scalephenomena.Inthiscollaborativeworkshop,wewillexaminehowtheintegrationofqualitative,quantitativeanddesign-basedanalysesofcitiesascomplexadaptivesocio-ecologicalsystemscouldinformsustainabledevelopmentagendas.Forexample,howcouldthesemethodshelpusinterpretthesystemscomplexityfromwhichwearebeginning;therangesofhuman-environmentinteractionsthatperpetuateunsustainability;theindirectimpactsofhumanactiononecologicalsystems;pathdependentdevelopmenttrajectories;thecumulativeeffectsofdevelopment;theunderlyingdriversofresourceflows;thenetworksthatenablequalityoflife;thescalesatwhichoursystemsaremostrobust;theentrenchmentofvaluesandbehaviours;emergent,globalizedpatternsofsystemsorganization;and,pointsofleverageforsystemschange?