TheEuropeanColloquiumonTheoreticalandQuantitative

Geography(ECTQG)

7-11September2017York,UK

Welcometothe2017EuropeanColloquiumonTheoreticalandQuantitativeGeography(2017),organisedbytheSchoolofGeographyandtheLeedsInstituteforDataAnalytics(UniversityofLeeds).ItisagreatdelighttowelcomeyoutoYork;acitythatwasestablished2,000yearsago,andhasbeenasubstantialhumansettlementforconsiderablylonger.Forthoseofyouwithanappetiteforhistory,theRomanWalls,Minster,RailwayMuseum,andShamblesmaybeamongmanysitesofinterest.Forthoseofamoregregariouspersuasion,thenthemanybarsandrestaurantsofthecityandriversidemaybemoreappealing!TheSchoolofGeographyattheUniversityofLeedsissomewhatlessancientthanthecityofYork.Nevertheless,wehavealongtraditionasacentreforresearchinspatialanalysisandquantitativemethods,andaftermanyyearsofparticipationintheECQTGweareproudtohostyouinYorkshireforthefirsttime.Wehopeyouwillfindstimulationaseverinthepresentationsandconversationwithacquaintancesoldandnew.ItisaspecialpleasuretowelcomeouroldfriendandmentorProfessorSirAlanWilsonasakeynotespeaker.Inhalfacenturysincethelandmarkpublicationsofthe1960s,Alan’sinfluenceonthedisciplinehasbeenpeerless.WehopethatyouwillespeciallyenjoythesessionsthathavebeensetasidefortheappreciationofAlan’swork,andwilljoininlivelydiscussionsaboutitscontinuingimpactandlegacyforthefuture.WelcomingyoutotheconferenceatthistimeisalsoparticularlyexcitingfortheorganisingcommitteebecausewehaverecentlybeenawardedsubstantialfundingtocreatetheConsumerDataResearchCentre(CRDC)and,tosupportthisinitiative,theUniversityofLeedshasalsoinvestedheavilyinthenewLeedsInstituteforDataAnalytics(LIDA).Theseprogrammesaimtobringtogetherdiversegroupsofacademics,businesses,andpolicymakerstoworkoncriticalgeographicalsocialissues.Assuch,theECTQGisoneofthemostrelevantandimportantforaforourwork.Bestwishes,MarkBirkin,MichelleMorris,NickMalleson(ECTQGLocalorganisingcommittee)

CONFERENCETIMETABLE.......................................................................................................................................4

USEFULINFORMATION...........................................................................................................................................5

REGISTRATIONDESK...................................................................................................................................................5CONFERENCEDINNER..................................................................................................................................................5WIFI........................................................................................................................................................................5LUGGAGE..................................................................................................................................................................5

KEYNOTESPEAKERBIOGRAPHIES...........................................................................................................................6

SESSIONOVERVIEW...............................................................................................................................................7

FRIDAY8THSEPTEMBER................................................................................................................................................7SATURDAY9THSEPTEMBER..........................................................................................................................................11SUNDAY10THSEPTEMBER............................................................................................................................................14MONDAY11THSEPTEMBER..........................................................................................................................................16

ABSTRACTS............................................................................................................................................................18

KEYNOTEPRESENTATIONS...........................................................................................................................................18SPATIALINTERACTIONMODELLINGSYMPOSIUM..............................................................................................................201A-ENVIRONMENTANDHEALTH.................................................................................................................................211B-MOBILITY..........................................................................................................................................................242A-HEALTHANDLIFESTYLE.........................................................................................................................................272B-SPATIALANALYSIS:APPLICATIONS..........................................................................................................................30SPECIALSESSION–BIGDATAANDOBESITY.....................................................................................................................343A-GEOGRAPHYANDECONOMICS1.............................................................................................................................363B-SPATIALANALYSIS:CLUSTERING.............................................................................................................................384A-GEOGRAPHYANDECONOMICS2.............................................................................................................................404B-SPATIALANALYSIS:VISUALISATION.........................................................................................................................435A-GEOGRAPHYANDECONOMICS3.............................................................................................................................475B-TEXTUALBIGDATA..............................................................................................................................................496A-URBANSYSTEMS.................................................................................................................................................526B-ACCESSIBILITY.....................................................................................................................................................557A-DEMOGRAPHICS.................................................................................................................................................597B-BIGDATA..........................................................................................................................................................638A-POLITICSANDDEMOGRAPHICS..............................................................................................................................668B-LANDUSE..........................................................................................................................................................69

ConferencetimetableDate Time ActivityThursday7thSeptember2017 16:00-19:00 Registration 20:00 Buffetdinner Friday8thSeptember2017 09:00-10:30 Keynote1-ProfessorSirAlanWilson 10:30-11:00 Coffee

11:00-12:30SpecialSession–SpatialInteractionModelling

12:30-13:30 Lunch 13:30-15:00 Parallelsession1 15:00-15:30 Coffee 15:30-17:00 Parallelsession2 17:00-18:30 Break 18:30-19:30 Specialsession–BigdataandObesity 19:00 Buffetdinner Saturday9thSeptember2017 09:00-10:30 Keynote2–ProfessorPaulLongley 10:30-11:00 Coffee 11:00-12:30 Parallelsession3 12:30-13:30 Lunch 13:30-15:00 Parallelsession4 15:00-15:30 Coffee 15:30-17:00 Parallelsession5

19:00Conferencedinner-NationalRailwayMuseum

Sunday10thSeptember2017 10:30-12:00 Parallelsession6 12:00-13:00 Lunch

13:00-14:30Keynote3-ProfessorAnneVernezMoudon

14:30-18:00 Fieldtrip 19:00 Buffetdinner Monday11thSeptember2017 09:00-10:30 Parallelsession7 10:30-11:00 Coffee 11:00-12:30 Parallelsession8 12:30-13:30 Lunch 13:30-15:00 Keynote4-ProfessorChrisBrunsdon 15:00 Depart

UsefulInformation

RegistrationDesk

Theregistrationdeskwillbeopenfrom4pm–7pmonThursday7th,9am–3pmonFriday8th,Saturday9thandSunday10th,and9amtoconferencecloseonMonday11thSeptember.Ifatanypointyouhaveanyqueries,docometotheregistrationdeskorgraboneoftheconferenceorganisingcommittee.

Fieldtrip

WewillbegoingonaguidedtourofYorkonSundayafternoonwiththeAssociationofVoluntaryGuidestotheCityofYork.Ifyouwillbecomingalong,pleasemeetinthefoyerofthehotelat2.45pmtowalktothestartofthewalk.Therewillbeanotepostedontheregistrationdeskofthestartingpointofthewalk,soifyouwanttomakeyourownway,pleasecheckthereforthemeetingpointinYork.

ConferenceDinner

TheconferencedinnerwillbetakingplaceonSaturdayevening,attheNationalRailwayMuseum.ThevenueisashortwalkfromtheParkInn,butthosewhowouldliketowalkdowntogether,domeetusinthefoyerofthehotelat6.45pm.

Wifi

Pleasecometotheregistrationdeskforwifidetails.

Luggage

LuggagemaybeleftattheParkInnreception.

KeynoteSpeakerBiographies

SirAlanWilsonFBA,FAcSS,FRSisChiefExecutiveoftheAlanTuringInstituteandProfessorofUrbanandRegionalSystemsintheCentreforAdvancedSpatialAnalysisatUniversityCollegeLondon.HeisChairoftheHomeOfficeScienceAdvisoryCouncil.

Hewasresponsiblefortheintroductionofanumberofmodelbuildingtechniqueswhicharenowincommonuseinternationally–suchastheuseof‘entropy’inbuildingspatialinteractionmodels;summarisedinEntropyinUrbanandRegionalModelling.Hiscurrentresearchisontheevolutionofcitiesandglobaldynamics.HewritestheQuaestioblogonresearchandinterdisciplinarity.

HewasVice-ChancelloroftheUniversityofLeedsfrom1991to2004whenhebecameDirector-GeneralforHigherEducationinthethenDfES.From2007-2013hewasChairoftheArtsandHumanitiesResearchCouncilandfrom2013-2015,oftheLeadExpertGroupfortheGovernmentOfficeforScienceForesightProjectonTheFutureofCities.

HeisaMemberofAcademiaEuropaea,anFBA,anFAcSSandanFRS.Hewasknightedin2001.HisrecentbooksincludeKnowledgePower(2010),TheScienceofCitiesandRegions(2012),hisfivevolume(edited)UrbanModelling(2012),(withJoelDearden)ExplorationsinUrbanandRegionalDynamics(2015)andtwoeditedvolumes,GlobalDynamicsandGeo-mathematicalmodelling(2016).

PaulLongleyB.Sc.,Ph.D.,D.Sc.,FAcSS.holdsachairinGeographicInformationScienceatUniversityCollegeLondon(UCL),UK.HisresearchinterestsareinGeographicInformationScienceandSystems,andheisaco-authorofabest-sellingbookonthissubject.HehasworkedasPIofCo-Ionmorethan50researchgrantstotallingover£20millionandhassupervisedmorethan50Ph.D.students(mostfundedbyresearchcouncils).Hispublicationsincludenineteenbooks,andover150refereedjournalarticlesandcontributionstoeditedcollections.HecurrentlydirectstheConsumerDataResearchCentreatUCL.HisacademicandeditorialDutiesincludepasteditorshipofComputers,EnvironmentandUrbanSystemsandEnvironmentandPlanningB.Hehasalsoheldelevenexternallyfundedvisitingappointmentsandgivenmorethan150conferencepresentationsandexternalseminars.

AnneVernezMoudonisProfessorEmeritaofArchitecture,LandscapeArchitecture,andUrbanDesignandPlanning;AdjunctProfessorofEpidemiologyandCivilandEnvironmentalEngineeringattheUniversityofWashington,Seattle,whereshealsodirectstheUrbanFormLab(UFL).Prof.VernezMoudon’spublishedworksincludeBuiltforChange:NeighborhoodArchitectureinSanFrancisco(MITPress1986),PublicStreetsforPublicUse(ColumbiaUniversityPress1991),andMonitoringLandSupplywithGeographicInformationSystems(withM.Hubner,JohnWiley&Sons,2000).Herrecentpublicationsonbuiltenvironmentandbehaviourareintransportandhealthjournals.ChrisBrunsdonisaProfessorofGeocomputationandDirectoroftheNationalCentreforGeocomputationatMaynoothUniversity,Ireland.PriortothishewasaProfessorofHumanGeographyattheUniversityofLiverpoolintheUK,andbeforethisheworkedintheUniversitiesofLeicester,GlamorganandNewcastle.HehasdegreesfromDurhamUniversity(BScMathematics)andNewcastleUniversity(MScMedicalStatistics,PhDinGeography).HismostrecentbooksincludeGeocomputation:APracticalPrimer(withAlexSingleton,2015)andAnIntroductiontoRforSpatialAnalysisandMapping(withAlexisComber,2015).

SessionOverview

Friday8thSeptember

Time Room1 Room2

09:00-10:30 Keynote1-ProfessorSirAlanWilson

10:30-11:00 Coffee

11:00-12:30 AlanWilsonPlenary

SpatialInteractionModellingSymposium

Chair:MarkBirkin1. JohnStillwellComparingDistanceanditsFrictionalEffectonInternalMigrationinCountriesAroundtheWorld2. FrankSouthworthALookatFreightDemandModelingintheUnitedStates3. MartinClarkeNumericalExperiments:exploringthepropertiesofspatialinteractionmodelsusingrealandimaginarydata4. MarkBirkinAfamilyofspatialinteractionmodellers5. MichaelBatty

Time Room1 Room2

ScalingfromSpatialInteraction

12:30-13:30 Lunch

13:30-15:00 Parallelsession1

1A-EnvironmentandHealth

Chair:EmmaWilkins1. SoniaTrabelsi;IsabelleThomasOnmeasuring“green”:acomparativeanalysisoffourdatabasesinNamur(Belgium)2. GeoffreyCaruso;MarionLeTexier;MirjamSchindlerTheGreenMismatchHypothesis:conceptframingandempiricalviewsfromBrussels,LuxembourgandRouen.3. MarleneBouraHowdoesthespatialdistributionofgreenwithincitiesimpactcarbonuptake?4. ThierryFeuillet;HadrienCommenges;MehdiMenai;Helene

Charreire;EmmanuelleKesse-Guyot;RomainReuillon;Julie-AnneNazare;SergeHercberg;Jean-MichelOppert

WhydoIwalkhere?ComputingalargescaleGWRforhierarchisingtheenvironmentalcorrelatesofwalkinginFrance

1B–Mobility

Chair:EmilySheard1. RobinLovelaceImplementingspatialinteractionmodels:fromprototypetogloballyscalabletools2. Jean-PierreNicolas;NicolasPele;CyrilleFrancoisUrbanFormandDailyMobilityInteractions:anAnalysisofSocioeconomicandEnvironmentalImpactsThankstoLUTIModelSimulations3. NickMalleson;SusanGrant-Muller;FrancesHodgson;Gillian

HarrisonQuantifyingPersonalPollutionImpactstoInformTransportSchemeInnovationthroughNewGenerationMobilityData4. LarissaSilvaLopes;BenjaminMotte-Baumvol;ThomasTheveninUrbanmobilityandthespatialdistributionofeconomicactivitiesinRiodeJaneiro

15:00-15:30 Coffee

Time Room1 Room2

15:30-17:00 Parallelsession2

2A-HealthandLifestyleChair:MichelleMorris1. PaulWilliamson;XinGu;KarynMorrissey;FerranEspuny-PujolSmall-areaestimationofcomorbidity:anindirectsurveycalibrationapproach2. EmmaWilkins;MichelleMorris;DuncanRadley;ClaireGriffithsGeographicvariationinthevalidityoftwosourcesofsecondaryfoodenvironmentdata3. ElenaGukModellingoftheterritorialtouristandrecreationalsystemofNorilskRegion4. NikLomax;MichelleMorrisAssessingactivitylevelsofindividualsinalarge,self-selectingdataset5. RachelOldroyd;MarkBirkin;MichelleMorrisTheuseofnon-traditionaldataformonitoringfoodborneillness:methodologicalrecommendationsandconsiderations

2B-Spatialanalysis:ApplicationsChair:DenisePumain1. NirFulman;ItzhakBenensonWhyDoWeSearchforParkingforSoLong?2. YonatanShaham;ItzhakBenensonModelingCity-ScaleFireSpreadinMediterraneanandMiddleEasternCities3. MaximeColomb;MickaelBrasebin;JulienPerret;TannierCecileSimulationofarealisticresidentialdevelopmentwiththeintegrationoftwoexistingmodels4. MilanaGlebovaStructuralandspatialchangeinelectricalindustryoftheUKinthebeginningof21century5. HadrienFouilladeOrsiniCrimeslocationwithinspatialpatternsinsuingbymotorizednetworkinMarseille(SouthernFrance)

18:30-19:30 Specialsession–BigdataandObesityPresentationsfromtheESRCStrategicNetworkforObesitymembersChair:MarkBirkin

Time Room1 Room2

1. MichelleMorris,MarkBirkinWhatistheESRCStrategicNetworkforObesity?2. DuncanRadley;KateTimmins;MarkGreen;JamiePearceCan‘bigdata’contributewheretraditionalresearchfallsshort?Areviewofbigdatausageinobesityresearch3. EmmaWilkins;MarkBirkin;ClaireGriffiths;MichelleMorrisCanbigdatasolveabigproblem:theobesitydatalandscape?4. ThomasBurgoine:PabloMonsivaisDevelopmentofdata-driventoolsforpolicymakers:TheFoodenvironmentassessmenttool(Feat)asacasestudy

19:30 Buffetdinner

Saturday9thSeptember

Time Room1 Room2

09:00-10:30 Keynote2-ProfessorPaulLongley

10:30-11:00 Coffee

Parallelsession3

11:00-12:30 3A-Geographyandeconomics1Chair:JusteRaimbault1. MehdiBida;CelineRozenblat;ElfieSwertsModelinghierarchyandspecializationofasystemofcitiesasaresultofthedynamicsoffirms'interactions2. DenisePumainFromtheorytomodeling:whicheconomicsforevolutionarygeography?3. AntoninBergeaud;SimonRayAdjustmentcostsandfactordemand:newevidencefromfirms'realestate4. ClementineCottineau;ElsaArcaute;MaxNathanGeoindustrialclusteringofLondonbusinesses:modellingfirms'trajectoriesandtheirinteractionwiththeurbanfabric

3B-SpatialAnalysis:ClusteringChair:ChrisBrunsdon1. RichardHarris;DewiOwen;RoryKramerModellingProcessesofUrbanEthnicDesegregationusingMultilevelIndicesofSegregation2. RogerBivand;DavidWongComparingimplementationsofglobalandlocalindicatorsofspatialassociation3. EmilySheard;NickMalleson;MarkBirkinExploringtheSpatio-TemporalDistributionofCarKeyBurglaryinWestYorkshire:RoutineActivityorPlannedBehaviour?4. PeterMandlGeographicalConceptsandReflectionsaboutAspectsofthe“DigitalAge”5. MatthewDawsSelf-excitedpointprocesspatternsincrimedata

Time Room1 Room2

12:30-13:30 Lunch

13:30-15:00 Parallelsession4

4A-Geographyandeconomics2Chair:JusteRaimbault1. JusteRaimbaultInvisibleBridges?ScientificlandscapesaroundsimilarobjectsstudiedfromEconomicsandGeographyperspectives2. OlivierFinanceTransnationalinvestmentdecisionsinandtowardsEurope:evidencesforasingleEuropeansystemofcities?3. JustinDelloye;RemiLemoy;GeoffreyCarusoHomotheticScalingofUrbanLandUseandPopulationDensityProfilesinMonocentricModels4. RogerWhite;GustavoRecio;WolfgangBanzhafTheNecessityofDisequilibrium

4B-SpatialAnalysis:VisualisationChair:MartinCharlton1. RobinCura;CecileTannierVisualandsemi-automatedexplorationofanarchaeologicalmodel:calibratingamodelofspatialreorganizationinNorth-WesternEurope,A.D.800to1100.2. PierreFrankhauser;OlivierBoninErrortermsanduncertaintyinfractalradialanalysis3. AlessandroAraldi;GiovanniFuscoRetailActivityandtheCity:ContributionfromtheAnalysisofUrbanFabrics4. MartinCharlton;ChrisBrunsdonUsinganimationtovisualisecorrelationstructureinmultivariatespatialdata

5. MonsuruAdepejuDevelopingahybridhotspotapproachforimprovingtheaccuracyofcrimeprediction

15:00-15:30 Coffee

Time Room1 Room2

15:30-17:00 Parallelsession5

5A-Geographyandeconomics3Chair:JusteRaimbault1. EricKoomen;DiogoVascoBridginggeographyandeconomicsinlocal-scaleland-usemodelling2. ZahratuShabrina;ElsaArcaute;RichardMilton;MichaelBattyModellingAccessibilityofAirbnbinGreaterLondonArea3. JorisBeckers;IvanDarioCardenasBarbosa;AnnVerhetselModellingtheurbanlayerinB2Ce-commercedistributionnetworks

4. Discussionandspecialsessionroundup

5B-TextualBigdataChair:RachelOldroyd1. FrancoiseLucchini;LenyGrassot;JulienBaudry;OlivierGillet;

BernardElissaldeSocialnetworks,eventsandcities2. MarionMaisonobe;LaurentJegou;BeatriceMilard;Michel

Grossetti;DenisEckertTheworldgeographyofscientificvisibility:adeconcentrationprocess(1999-2012)3. DenisEckert;MarionMaisonobe;JohnHarrison;FrancisHarveyTheevolvinggeographyofacademicplacesinFrance,Germany,andtheUK(1999-2014)4. CaterinaDeLucia;MarkBartlett;PasqualeBalena;Pasquale

Pazienza;DianaCaporaleModellingthegapbetweentouristpreferencesandlocalknowledgeasdriversoftouristicattractiveness

19:00 Conferencedinner-NationalRailwayMuseum

Sunday10thSeptember

Time Room1 Room2

Parallelsession6

10:30–12:00 6A-UrbansystemsChair:RogerWhite1. GaetanMontero;CecileTannier;IsabelleThomasOnurbanboundaries:threemethods,threesolutionsforBrussels2. PavelP.EmIsthereanylinkbetweenfractaldimensionandservicecompanies’entropyinMoscowandSeoul3. GiovanniFusco;AlessandroAraldiMultipleFabricAssessment:ExploringtheFormsofaMetropolitanArea.4. BranoGlumac;MarcosHerreraGomez;JulienLicheronAresidentiallandpriceindexforLuxembourg:Dealingwiththespatialdimension5. DaniBroitman;EricKoomenLivinginhistoriccities:Intensificationandincreasingdensitygradients

6B–AccessibilityChair:NikLomax1. CyrilleMedarddeChardonDetectingoptimalcut-throughlocationstoincreasepedestrianandcyclingmodalshares2. OlivierBonin;PierreFrankhauserAccessibility,AmenitiesandNeeds-aconceptualframeworkforexploringhouseholds’satisfaction3. JuliaH.TabbitaHousinginformalityintheBuenosAiresMetropolitanRegion:Aquantitative-spatialassessmentforregularizationandlandusepolicies4. JusteRaimbault;SoleneBaffiStructuralSegregation:AssessingtheimpactofSouthAfricanApartheidonUnderlyingDynamicsofInteractionsbetweenNetworksandTerritories5. EusebioOdiari,MarkBirkin,SusanGrant-MullerandNick

MallesonTheuseofbigdatainspatialmicro-simulationofrailwaypassengers

12:00–13:00 Lunch

13:00–14:30 Keynote3-ProfessorAnneVernezMoundon

14:30–18:00 Fieldtrip

19:00

BuffetDinner

Monday11thSeptember

Time Room1 Room2

Parallelsession7

09:00-10:30 7A–DemographicsChair:NickHood1. DavidBurgRecurringPatternsinUrbanGrowth2. FrancoisBavaud;TheophileEmmanouilidis;GuillaumeGuexHandlingsocialsegregationingravitymodelling:acomputablescheme3. DoignonYoannWhichspatialconvergenceofdemographicageinginMediterranean?4. KhneyzerChadiLebanonbetweenanalogyandspecificityofterritorialdevelopmentprocesses:ThenecessaryoriginalityofacredibledevelopmentstrategyfortheAkkar5. AnnaDmowska;TomaszStepinskiEvolutionofRacialDiversityinNewlyBuiltAmericanHousingSubdivisions

7B–BigDataChair:EusebioOdiari1. TomasCrols;NicolasMallesonQuantifyingtheAmbientPopulationusingBigDataandAgent-BasedModelling

2. ArnaudAdam;IsabelleThomasRevisitingtheinternalstructureofBrusselswithmobilephonedata:theoreticalrevolutionordataevolution?

3. RobinCuraMakinglargespatio-temporaldataanalysiseasier:Illustratedpleaforusing(geo)VisualAnalytics

4. HoracioSamaniego;MauricioFranco;BorisSotomayorTheTopologyofCommunicatingAcrossCitiesofIncreasingSizes,ortheComplexTaskof“ReachingOut”inLargerCities5. MarinaToger;ItzhakBenenson;SaritWeisburdThedeterrenceeffectrevisited:spatialanalysisoftheimpactofpolicepresenceonprobabilityofcrime.

10:30-11:00 Coffee

Parallelsession8

11:00-12:30 8A-PoliticsandDemographicsChair:ClementineCottineau1. CelioSierra-PaychaHowNationalUrbanSystemsshapeInternationalMigrationSystems:thecaseofColombia2. NickHood;MylesGould;JocelynEvans;PaulNormanIsyourlocalareasusceptibletotheradicalright?DifferentiationUKIPsupportusinganareabasedclassificationofelectoralwardsinEngland3. MylesGould;NicholasHood;JocelynEvans;PaulNormanIntroducinganEuropeancomparativemultilevelstudyof'radicalright'support4. StephenClark;MichelleMorris;NikLomaxEstimatingtheUKsreferendumonEUmembershipusinge-petitiondataandmachinelearningalgorithms

8B-LanduseChair:TomasCrols1. RemiLemoy;GeoffreyCarusoEvidencefortheHomotheticScalingofUrbanForms2. AlexandreOrnonAspatialmultiscalarmodelisationofurbanisationintheSouth-EastofFrance3. EmilieLerond;OlivierKlein;Jean-PhilippeAntoniDatatransformationforland-use/transportinteractionsexploration4. NikitaA.SinitsynCellularautomataforsuburbanizationmodelling:thecaseofBelgorod

12:30-13:30 Lunch

13:30-15:00

Keynote4–ProfessorChrisBrunsdon

15:00 Depart

Abstracts

KeynotePresentations

SirAlanWilsonThefutureofurbanmodelling

Abriefoverviewofthehistoryofthedevelopmentofcomprehensiveurbanmodelsispresentedandthisprovidesabasisforoutliningthepresentstateofthesciencefutureresearchpriorities.Theseareaddressedundertwomainheadings:thestraightforwardbutstilldifficult;andbiggerchallenges.Thefirstsetincludedatawranglinginthe‘bigdata’era;andbeingfullycomprehensive,addinghealthandeducationforinstance.Thesedevelopmentareillustratedwithexamplesfromrecentresearch:aworldmodel,aregionalmodeloftheSouthPacific,militarydeploymentandpiracy.Thesecondsetincludethechallengesofdynamicmodelling;theintegrationofapparentlycompetingmodellingperspectives;newmathsandnewalgorithms;andintegrationwithplanningandtheassociated‘wickedproblems’agenda.PaulLongleyQuantitativeGeographyandtheSmartCensus

Today,BigDataareenablingthickerempiricaldescriptionsoftheformandfunctioningofmanyaspectsofsocialsystemsthaneverbefore.Buttheachievementsofthe‘smartcities’discourseareselective,withgreatestfocusupontheuseofsensorwebsandothernewformsofdatatomonitorthelocationsortrajectoriesofelementsoftheInternetofThingsratherthanthecircumstancesofresidentandworkingpopulations.Spatialandtemporalheterogeneityofoutcomesinhealth,socialmobilityandsocialcapitalformationremainpoorlyunderstoodanddisconnectedfromtheoreticalformulationsofsocialjusticeinthesentientcity.Theabsenceofresearchdesigninthecreationandmaintenanceofnewformsofdatacreatesnewscientificandanalyticalchallengestotheinclusivityoftrulysocialscience,giventhatthesourceandoperationofbiasinnewformsofdataispoorlyunderstood.Thereisaneedtoaddresstheasymmetrybetweenthesophisticatedmethodsofgeographicinformationscienceandthequalityandreliabilityofmanyofthedatasourcesthatunderpintheirusage.

AnneVernez-MoudonBuiltEnvironmentandHealthintheAgeofBigData

Evidenceismountingthatthebuiltenvironmentofwherepeoplelive,workandplayhasaninfluenceontheirhealth.Asaresult,publichealthpeoplearereachingouttocityandtransportplannersinanefforttomakehealthiercities.However,howexposuretothebuiltenvironmentaffectshealthstillneedsclarification.Pastresearchfocusedonhowthehomebuiltenvironmentwasassociatedwithhealth.Yettheresidentialcontextisonlyasmallpartofdailyenvironmenthostingpeople’sactivities.Thereisaneedtoexamineassociationsbetweenhealthbehaviorsandbuiltenvironmentinaprecisespatio-temporalframework.Thisneedisbeginningtobeaddressedbyrapidadvancesinsensortechnology,which,combinedwiththeubiquitoususeofsmartphonesloadedwithApps,arenowprovidingnotonly“big”butalso“detailed”dataonpeople’slocation,mobilityandactivitypatterns.Sensorscanalsocaptureenvironmental(airbreathed,ambientnoise,etc.)andhealth-relatedconditions(physicalactivity,heartrate,stress,etc.).

Paralleladvancesingeographicinformationsystems(GIS)bringdetaileddataonthebuiltandtransportationenvironment.Sofar,few“bigdata”researchershaveusedthesedata,optinginsteadtoemploy“blackbox”shortcutstocharacterizebothmobilitybehaviors(e.g.,todetecttravelmodes)andbuiltenvironmentcharacteristics(e.g.,tomeasurewalkability).ThispresentationreviewspracticalapproachestoattachbuiltenvironmentdatatothelargenumbersofGPSpointsincludedinbigdata.The

aimistocapturespatiallycontinuous,time-basedmeasuresofexposuretobuiltenvironmentattributesalongmulti-dayGPStraces.ThepresentationalsointroduceswaystomodelthebuiltenvironmentinGISinordertostreamlineenvironmentaldatacapturealongGPStraces.ChrisBrunsdonShiningalightonNOIR-RethinkingScalesofMeasurement

The'NOIR'referredtohereisactuallyanacronymforthefourscalesofmeasurementproposedbyStevensin1946:Nominal,Ordinal,Interval,andRatio.Despitebeingproposedover70yearsagothiscategorisationisstillinfluential-andcanshapethewaypeoplethinkaboutchoicesfordataanalysis.Anumberofsoftwarepackagesandtextbooksarestructuredatleastlooselyonthisframework.However,ithasnotgoneunchallengedanditwillbearguedherethatthisapproachisattimesanunhelpful.ThereareinconsistenciesinrecommendationsarisingfromtheNOIRcategorisation.AlsoNOIRomitsseveralimportantscalesofmeasurement.Inthistalk,Iwilldiscusspracticalexamplesoftheseinconsistenciesandomissions,andconsiderspecificcasesofomittedscalesofmeasurementfocusingondirectionaldata,compositionaldataandpartiallyorderedsets.

SpatialInteractionModellingSymposium

1. JohnStillwell

ComparingDistanceanditsFrictionalEffectonInternalMigrationinCountriesAroundtheWorld

Thispresentationexamineshowinternalmigrationdistanceanditsfrictionaleffectvarybetweencountries.Suchcomparisonsarehamperedbydifferencesinthenumberandconfigurationofspatialunitsforwhichdataareavailable–themodifiableareaunitproblem(MAUP).Oneapproachistouseflexibleaggregationroutinestoelucidatescaleandpatterneffectsinasetofcountriesforwhichfinelygrainedorigin-destinationmatricesareavailable.Abespokesoftwareplatformhasbeendesigned,calledtheIMAGEStudio,inordertoperformthespatialaggregationproceduresbutalsotocomputearangeofinternalmigrationindicatorsandcalibrateadoublyconstrainedspatialinteractionmodelforeachspatialconfigurationateachscale.Whilstanexponentialrelationshipbetweenmeanmigrationdistanceandmeanareasizehasbeenidentified,themodellingresultsshowthatthefrictionaleffectofdistanceremainsremarkablystableacrossspatialscale,exceptwherezoneshaverelativelysmallpopulationsandarepoorlyconnected.Thisstabilityallowsrobustcomparisonsbetweencountrieseventhoughzonalsystemsdiffer.Meanmigrationdistancesvarywidely,beinghighestinlarge,lowdensitycountriesandpositivelyassociatedwithurbanisation,HDIandGDPpercapita.Thissuggestsapositivelinkbetweendevelopmentandmigrationdistance,parallelingthatbetweendevelopmentandmigrationintensityreportedbyBelletal.(2016).Lessvariationisfoundinthebetaparameterthatmeasuresdistancedecaybutthereappeartobeclearvariationsbetweenmoredevelopedcountries,withlowerfrictioninlarger,lessdensecountriesundergoingrapidpopulationgrowth.2. FrankSouthworthALookatFreightDemandModellingintheUnitedStates

ThispresentationoverviewsthedifferentapproachestoestimatingandforecastingthedemandforfreightservicesintheUnitedStates,pointingoutthemostcommonin-practiceapproachesandhowrecentandon-goingresearcheffortsarelikelytomovethispracticetowardsnew,improved,andincreasinglyinvolvedmodelapplications,makingusingofavarietyofdatasources.Thesedevelopmentsarediscussedinthecontextofbringingmoredetailintothefreightplanningprocess:byaddingindustry,commodity,modal,network,behaviouralandlogisticaldetailstofreightactivitymodelsatanumberofdifferentregionalscales.Thediscussioniscentredontheinterplaybetweenfreightvolumesandfreightcosts.Emergingmethodsincludetheintroductionofsupply-chainconsiderationsintofreightactivitymodels,theuseofmicrosimulationtechniques,notablyinsupportofbehaviourallymotivatedagent-basedfreightmodelling,andtheinclusionofanexpandedrangeoffreightcostfactors,includingdeliverytimereliabilityandotherinventoryrelatedcarryingcosts.Supportingtheseeffortsareparalleldevelopmentsinnewlyavailabledatasources.Drivingmuchofthismodellingefforttodayisthesearchforpolicy-relevantandplan-sensitivefreightperformancemeasures,atatimewhentheconditionandcarryingcapacityofthenation’smulti-modalfreightnetworksarecomingunderincreasedscrutiny.3. MartinClarkeNumericalExperiments:exploringthepropertiesofspatialinteractionmodelsusingrealandimaginarydata

Spatialinteractionmodelshavebeeninusebybothacademicsandpractitionersforover50years.InthispaperwereviewtheextensiveapplicationofthesemodelsoverthisperiodinLeeds.Inparticularwedescribehowthepropertiesofspatialinteractionmodelscanbeexploredbyundertaking“numericalexperiments”toobservehowdifferentmodelsolutionsemergethroughvariationsinmodelparameters.Wealsodescribetheextensionstothetraditionalmodelsthatarerequiredtoensurethemodelsperformadequatelyinarealworldcommercialcontext.Theseincludefactorssuchaselasticdemand,brand

preference,agglomerationeffects,measuresofaccessibilityandsoon.Exampleswillbegivenfromarangeofsectors.Wefinallylookintothepossibilitiesofusingthesemodelstoaddressnewtypesofproblemsinthecurrentenvironmentof“BigData”,wherenewsourcesofconsumerspatialinteractionactivityarebecomingavailable.4. MarkBirkinAfamilyofspatialinteractionmodellers

AlanWilson’s‘FamilyofSpatialInteractionModels’(EnvironmentandPlanningA,1971,477citations)isoneofthehighlightsinanoutstandingportfoliooforiginalandhighimpactcontributionstothedisciplinesofgeographyandregionalscience.Inthispaper,Iwishtodrawattentiontothecomplementaryimportanceofthefamilyofspatialinteractionmodellerswhohavedonemuchtoconsolidate,developandbroadenthisimpactfromoneindividualtoa“School”withevenmorewide-rangingandcontinuinginfluenceonQuantitativeandTheoreticalGeographyinEuropeandbeyond.

Workingthroughafamilytreefromthe1960stothepresentday,wewillseehowtheconceptofspatialinteractionmodellinghasbeencomplementedandembellishedundertheinfluenceofcomplexityandbifurcationtheory,microsimulation,agent-basedmodellingandlatterlydatascience.

Withaparticularfocusontherangeofprojectswhicharestillongoing,thecurrentshapeoftheportfoliowillbedescribedandevaluatedinrelationtobroaderfieldsofacademicenquiry.Prospectsforcontinuedinnovationandfurtherprogresswillbeconsidered.5. MichaelBattyScalingfromSpatialInteraction

Spatialinteractionmodelsdevelopedoverthelast50yearsusinganalogieswithclassicalmechanics,namelythroughNewton’ssecondlawofmotioninwhichtheinversesquareruleisallsupreme.Mostofthefieldthathasfocusedonmakingthesemodelsoperationalforpurposeofpredictingmovementandonlyquiterecentlyhastherebeenanyreflectiononthemoresubstantiveconcernsastohowscaleandgeometryareembeddedwithintheirstructure.Buttherecentfocusinurbantheoryandmodellingondynamicshasthrowntotheforetheideathatascitieschange,astheygetbigger,andasthebalanceofspatialinteractionswhichtiestheircomponentstogetherandenablescitiestofunctionchanges,thenthescaleofcitieschangesqualitatively.Thisisseenmostdramaticallyinthenotionthatascitiesgrowtheirattributessuchasincome,poverty,crimeandsoonchangebyscalingallometrically.Muchofthisthinkingistiedtobiologicalquestionsbutsomeistiedtosocialphysics,particularlycityandrelatedsizedistributions.Inshort,thereisnowamovementtothinkmoresubstantivelyaboutthepropertiesofcitieswithrespecttoscaleandtoexplorethemeaningofchangethroughallometry,ranksizeandotherkindsofscalingassociatedwithcitysize.Inthistalk,Iwillsketchhowthefieldisdeveloping,introducingideasthatarebynomeanscompletebutwhichseektoreacttoideasaboutspatialinteractionintermsofscale,drawingonvariousdevelopmentsincomplexitytheoryandsocialphysics.Toanextent,theseideasdosetanewagendafortheybroachthenotionthatthesystemswearedealingwithareneverstable,theyarecontinuallychanginginqualitativeterms,andaregettingevermorecomplex.

1A-EnvironmentandHealth

1. SoniaTrabelsi;IsabelleThomasOnmeasuring“green”:acomparativeanalysisoffourdatabasesinNamur(Belgium)

The(further)developmentofgreenspacesinurbanareashasbecomeatrendingtopicinthelastdecade.Studiesonhedonicpricing,environmentalandecosystembenefitsandhumanhealthissues,allconsidergreen(urban)areasfromdifferentperspectives.AninterdisciplinarysystematicreviewonthemeasuresofgreenspacesconductedwithintheGRESP-

HEALTHresearchproject(Trabelsietal.2016)showsthatawiderangeofdatasetsandmeasuresaretobefoundintheliterature.Inthisworkweaimatlookingat,andidentifying,differenttypologiesofurban,peri-urbanandruralareasbymeansoffourdifferentlandusedatabasesonthesamestudyarea.Forthispurpose,welookintolanduseinformationprovidedbyCorineLandCover,UrbanAtlas,NDVI,andaself-developeddatabasefromGoogleEarthandGoogleStreetImages.ThestudyisconductedontheareaofNamur,acityof100.000inhabitantsinBelgiumwithurbanandruralfeatures.Weusedescriptivestatisticstocomparetheinformationprovidedbythefourdatabases,andassessthevarianceandthegapamongthem.Wefurthercomparethedatabylookingintosurface,shape,borderandothermorphometriccharacteristicsusingGISandFragstats.Thecomparisonofthefourdatabasesissubjecttothechoice/definitionofacommonclassificationofthelanduses,asthedataisprovidedindifferentformats(i.e.vector,raster;surface,percentage…).Fivemainclassesoflandusesaredefined:builtup,agriculturalandpasture,roadandrail,water,generalgreenareas.Thesefiveclassescanhavesub-classesaccordingtothedetaillevelofthedatabase.Firstresultsshowbothagreatvariationinthepercentageofsurfaceidentifiedbyeachdatabaseforsomelanduses(i.e.builtup),butalsoconsistencyinsurfaceforsomeotherclasses(i.e.agricultureandpasture).Themaincauseofsuchvariationscanbeexplainedbytheaggregationlevel(andthereforelowdefinition)ofthepatches.Finerdefinitionofthedataallowsforanalysisoftheintertwiningoftheclassesinthestudyarea:howdoesgreeninteractwiththebuilt-up?Whichtypologyofgreenislocatedwhere?Arethereroadsallowingtheaccesstogreenspaces?Inourworkwewilllook,amongotherthings,howthefourdatabasescanhelpansweringthesequestions.Thefourdatabaseshaveprosandcons:easyaccessbutlowimagequality;freeaccessandgoodqualitybutlimitednumberofareascovered;highprecisionofdatabuttimeconsumingprocessofdatacreationandlowreproducibility.Outcomeswillprovideareflectiontoolforselectingthemostappropriatedatabaseaccordingtodifferentstudyneeds.

2. GeoffreyCaruso;MarionLeTexier;MirjamSchindlerTheGreenMismatchHypothesis:conceptframingandempiricalviewsfromBrussels,LuxembourgandRouen.

Urbangreenspaceprovidesmultipleecologicalservicesandthereiswidespreadevidenceofthesocialbenefitsitbringstocitizens.Yetknowledgegapspersistinquantifyinghowandwhetherthesocio-economicbenefitsofgreenspaceaccruetoallhouseholdswithinanurbanareaoronlytoaportionofthemdependingontheirsocio-economicstatusandresidentiallocation.Thereisempiricalliteratureontheequityofaccesstogreenbutitisnotmuchharmonisedsothatconclusionsremaincontextual.Itisalsolargelya-spatial,consideringneighbourhoodsascontainerswithveryfewattentionpaidtogeographicalmechanismsthatcanleadtoinequitiesinaccesstogreenspace.Thissituationleadsustoproposingatheoreticalframeworkwheregeographicalmechanismsareexplicitandcanthereforebetested.WemimicKain’sspatialmismatchhypothesis,whichlookedatthespatialdifferentiationbetweenjobsandresidences,andproposea'GreenMismatchHypothesis',definedasthespatialseparationbetweentheprovisionofpublicgreenspaceandtheresidentiallocationofthosewhoarethemostinneedsforthembecausetheycannotcompensatewithprivategreenspace.Severalmechanismsarearguablyatstaketoexplainsuchadiscrepancy.Therearedirecteffectssuchashousingmarketsandthesortingofhouseholdsalongtheurban-suburbangradientorurbaninfillpoliciesthatdecreasegreenspaceinspecificneighbourhoods.Therearealsomoreindirecteffects,suchasparksrelatedhousingvaluationandgentrification,ortheendogeneityofresidentialchoiceandthedemandforlocalgreenspace.Inordertodisentangletheseeffectsempirically,wesuggestthatdatamustbegatheredalongthreedimensions:(i)householdsandtheirsocio-economiccharacteristics,(ii)centralitytocapturestandardhousingcosts-

transportcoststrade-offs,and(iii)local/neighbourhoodgreenspaceprovision(exogenousgreenexternality).InthispresentationweframetheGreenMismatchHypothesis,reviewtheempiricalliterature,andshowpreliminarycomparativeresultsfrommultinomiallogitandquantileregressionsappliedtoasurveyconductedinBrussels,LuxembourgandRouen.3. MarleneBouraHowdoesthespatialdistributionofgreenwithincitiesimpactcarbonuptake?

Cities-particularlythedenserones-areoftendepictedassustainablesystemsthatreducetheconsumptionoflandandoptimizeenergyuseforbuildingsandtransport.Cities,however-exactlybecausetheyconcentratehumanactivities-constitutethemainsourceofCO2emissionsintotheatmosphere.Yetitisimportantfromaresilienceperspectivetoassesstowhatextentcitiescanthemselvescopewiththeirownemissions.Weaddressthisissuebyexamininghowtheinternalspatialorganizationofcitiescanimpacttheflowofcarbonbetweentheirmainsources,i.e.roadsandbuilt-upland,andtheirmainstorageinfrastructure,i.e.urbangreenspaceandforests.Isitbettertohaveadensecorewithaperiphericgreenbelt?Largegreenpatcheswithinthecorecenter?Orsmallandfragmentedgreenspaces?Carbonflowsareakindofspatialinteractionbetweenlocatedsourcesandlocateddestinations,henceemissionfactors,storagefactorsanddiffusionparametersshouldinteractwithdistancebetweencarbonemissionsoriginsandcarbonsequestrationdestinations.Theobjectiveofthepresentworkistotestwhethertheinternalspatialorganizationofcitiesmattersforevaluatingcarbonuptakepotentialsofcitiesorwhethercitiescansimplybeconsideredassingleobjectswithaquantityofcarbonemissionsandcarbonsinkcapacityderiveddirectlyfromaggregatelandusedata.WeconductananalysisattheEuropeanscaleusingtheGMES/CopernicusUrbanAtlas2012databaseassoleinput.Thedatacomprisesdetailedlanduseinformationfor697Europeancities(FunctionalUrbanAreas)andaninventoryoftreeslocatedwithinartificiallanduseclasses(StreetTreeLayer).Weconstruct(andautomate)acarbonemissionpotentialmapfrombuilt-upland,densitiesandroads,andacarbonsequestrationpotentialmapfromgreenlandusesandtrees.Forsimplificationbothconstructsrelyonhomogeneousphysical,biologicalandurbanassumptionsacrosscities(e.g.coniferousasrepresentativevegetation,constanttrafficperroadtype,...).Ourpotentialsarenotvalidatedagainstempiricalcarbonemission/sequestrationdatabecausetheydonotexistconsistentlyfortheentiredatasetandbecauseouraimistocomparedifferenturbanstructuresandanalyzehowefficienttheyaretocapturetheemittedcarbon,independentofspatialheterogeneitiesinvegetationandenvironmentalconditionsacrossEurope.Wethenformalizetherelationshipandflowsbetweensourcesandsinksasafunctionofdistance,diffusionparameters(especiallywindspeed)andexogenousgeographicalvariables(latitudes,temperature,...).Weintendtorevealwhichspatialorganizationexhibitshigherresiliencetointernalemissionsandconcentrations.Further,weanalyzethesensitivityofresultingcarbonbudgetspercitytochangingdiffusionparametersanddistancedecayfunctionalforms.Thisallowsustocontrastafullyaggregatedviewwheretherelativepositionsofdifferentlanduses(emissionandstorage)doesnotmatter,afullydisaggregatedviewwithnodiffusionwhereagaintherelativepositionsofdifferentlandusesdoesnotmatter,withallsituationsinbetweenwheretheinternalorganizationwillmatter.Thenthebudgetistheresultofvariationsbetweenfragmentationofgreenspacesandthediffusionparameters.4. ThierryFeuillet;HadrienCommenges;MehdiMenai;HeleneCharreire;EmmanuelleKesse-Guyot;

RomainReuillon;Julie-AnneNazare;SergeHercberg;Jean-MichelOppertWhydoIwalkhere?ComputingalargescaleGWRforhierarchisingtheenvironmentalcorrelatesofwalkinginFrance

BackgroundWalking,asthemaincomponentofdailyphysicalactivity,contributestoreducetheriskforcardiovascularandotherchronicdiseases,andtoincreasewell-being(Warburtonetal.,2006).Manyscholarsthereforeseekatexploringwalkingenvironmentaldeterminantsinordertoidentifypotentialleveragesforfavoringashiftingtowardshealthierandmoresustainablebehaviors.However,thissearchfordeterminantidentificationstillsuffersfromtwomajorconcerns.(i)Thebulkofempiricalstudiesdoesnottakeintoaccounttheconsequencesofspatialheterogeneity,inboththestatisticalanalysesandtheirinterpretations.Thisspatialheterogeneityimpliesthattheresultsofanystudyofalimitedareadependexplicitlyontheboundsofthatarea(Goodchild,2009).(ii)Putativedeterminantsareoftentakenindividuallyorthroughinteractiontermsthatarelimitedtoafewpairsofvariables,whiletheyshouldbeconsideredascombinationsoffactors,freetovary,intermsofhierarchy,bygroupsofindividualsand/orbyplaces.Toaddressthosetwoconcernssimultaneously,welookedatamethodologicalmean(largescaleGWR)allowingspatialcontextstobedrawn,andinwhichthehierarchyofenvironmentalcorrelatesofwalkingwouldvary.DataandmethodIndividualdataregardingwalkingbehaviorsandotherpersonalcovariateswerederivedfromtheNutrinet-Santéstudy,anongoingweb-basedcohortlaunchedinFranceinMay2009(Hercbergetal.,2010).Thesampleusedinthisstudyincluded40,374respondents(mean±SDageof48.8±14.4years).Thetwooutcomevariablesincludewalkingforerrandsandleisure(timespentwalkingduringthepast4weeks,declaredbyquestionnaire).Independentenvironmentalvariablesinclude7GIS-basedbuiltandsocioeconomicvariables,assessedaroundeachhomeaddressrespondent.We(i)performedalargescale(thewholeofFrance)geographicallyweightedlogisticregression(GWR)aftersplittingthestudyareaandparallelizingalgorithmstoovercomecomputationalissues(duetothesamplesize–n~40,000)and(ii)carriedoutak-meansclusteringoftheGWRscaledcoefficients.ParallelcomputingwasenabledthroughtheHuma-NumVeryLargeResearchInfrastructure,aserviceprovidedbytheFrenchNationalCenterforScientificResearchinthefieldofhumanandsocialsciences.ResultsandconclusionsTheGWRcoefficientclustersweremappedandrevealedspatialcontextsinwhichthehierarchyofenvironmentalcorrelatesofwalkingvaries.Beyondtheexpecteddistinctionbetweenruralandurbanareas,resultsinterestinglysuggestthatwalkingcorrelatesvarybycities.Morespecifically,spatialclusterswereassociatedtothesizeandthetype(e.g.,corecity,surburbancity)ofcities.Forinstance,populationdensityisassociatedtowalkingfortransportationonlyinsmalltomedium-sizedcities,butnotinlargestones.Moreover,aregionalscalespatialstructurealsoappears,indicatingthatsimilarcitiesintermsofsizeandcentralitydonotencompassthesamecorrelatesofwalkingaccordingtotheregion.Intermsofpublichealthimplications,ourresultssuggestthatplanningpolicyencouragingwalkingshouldbeconsideredlocallyratherthanglobally.

1B-Mobility

1. RobinLovelaceImplementingspatialinteractionmodels:fromprototypetogloballyscalabletools

Origin-destination(OD)dataformsthebasisofmuchresearch,intransport,migrationandtransportstudies.Inparallelwiththegrowthinthenumberandsizeofsuchdatasets,methodsforsimulatingandupdatingthemhaveproliferated.Manyofthesemethodsareknownasspatialinteractionmodels(SIMs).SIMsarethusvitalforfurtheringourunderstandingoflarge-scalehumanmovementpatterns.However,muchoftheacademicliteraturefocussesonthedevelopmentofnewandsophisticatedmethods,rather

thantheimplementationofSIMsonlargedatasets.ThisisproblematicforpractitionerswishingtouseSIMsintheirwork:whilethereismuchinformationonwhichSIMsaremostflexibleoreffectivetheoretically,therearefewresourcesforassessinghowscalabledifferentmethodsare'ontheground'.Takingabroaddefinitionofscalable,thispaperwillexploreSIMsintermsscalabilityandcomputationalefficiency.Theresultswillbedemonstratedwithreferenceaplannedmodellingproject,whichwouldusegloballyscalableSIMswiththeaimofinformingeffectivesustainabletransportpoliciesworldwide.2. Jean-PierreNicolas;NicolasPele;CyrilleFrancoisUrbanFormandDailyMobilityInteractions:anAnalysisofSocioeconomicandEnvironmentalImpactsThankstoLUTIModelSimulations

Thespatialorganizationofcitiesdeeplychangedallalongthe20thcentury,buttheeffectsofurbanformondailytripsarestillunderdebate(EwingandCervero,2010).However,methodsusedtoappreciatetheseimpactsarenotreallyefficienttoseparatethedifferentfactors:aswellasthroughcitycomparisonsorthroughdiachronicanalysis,eventhankstoLUTImodelling,itisdifficulttoseparateurbanformimpactsfromeconomicalorculturalcontextsorgeneralchanges(Echeniqueetal.,2012).Inaddition,thetoolstoassessurbanplanningandtransportpoliciesareoftenfocusedononedimensionofsustainabledevelopment,withrestrictedeconomicorenvironmentalapproaches,andrarelytakethesocialdimensionintoaccount.Last,theresultsareprovidedatanaggregatedlevel,withoutdifferentiationbetweenhouseholdtypes(dependingontheirlocation,levelofincome,etc.)Themainpurposeofthatpresentationistoproposeasimulationexercisecarriedoutfromarealcitycaseandtestingtheimpactsofdifferentiatedtendenciesofurbanform,allotherthingsbeingequal.Theseresultswillbeprovidedforthe3pillarsofsustainabledevelopmentanddistinguishedbytypeofhouseholds,bothforthehouseholdexpenditures(PeléetNicolas,2016)andfortheenvironmentalimpactsoftransport(Françoisetal.,2017).TheSIMBADLUTImodel,calibratedontheLyonurbanarea(France)bytheLAET(Nicolasetal.,2013),hasbeenusedtosimulatedifferenturbanformtrendsandtoassesstheirsocial,economicandenvironmentalimpacts.Moreprecisely,theresultsarebasedontheconstructionof3urbanformscenariosinspiredfromtheVILMODesproject(collaborationbetweenTHEMA,LVMTandLAETlaboratories,supportedbytheFrenchPREDITprogram–Antonietal.,2015):₋Ascenarioofapolycentricurbanarea,withsub-hubsdistributedalongatram-trainnetwork,consistentwiththeTransitOrientedDevelopmentprincipals(Calthorpe,1993);₋Ascenarioofasprawledurbanarea,withastrongerproportionofpeoplelocatedintheremotepartsofthecenter;₋Ascenarioofamonocentricurbanarea,compactedaroundadensecitycenter.Antoni,Bonin,Frankhauser,Houot,Nicolas,Thierry,Tomasoni,Toilier,Vuidel,2015.VILMoDes-Villeetmobilitésdurables.Calthorpe,1993.TheNextAmericanMetropolis:Ecology,Community,andtheAmericanDream.PrincetonArchitecturalPress,NewYork.Echenique,Hargreaves,Mitchell,Namdeo,2012,"Growingcitiessustainably-doesurbanformreallymatter?".JournaloftheAmericanPlanningAssociation,72(2),pp.121-137.François,Gondran,Nicolas,Parsons,2017,“Environmentalassessmentofurbanmobility–amethodbasedonlifecycleassessmentoftheresultsofaLUTImodel”.EcologicalIndicators,72,pp.597–604.Ewing,Cervero,2010.TravelandtheBuiltEnvironment:AMeta-Analysis.J.Am.Plann.Assoc.76,265–294.Nicolas,Bonnel,Bouzouina,Cabrera,Pascal,Pluvinet,Toilier,Zuccarello,2013.Laprospectiveurbanisme-transportaveclaplateformeSIMBAD.LAETreport.Pelé,Nicolas,2016,«L’impactdulieuderésidencesurlamobilitéquotidiennedespériurbains:une

approcheparlamodélisationdeleursdépenses».ColloqueASRDLF,7-9juillet2016,Gatineau,Canada.11p.3. NickMalleson;SusanGrant-Muller;FrancesHodgson;GillianHarrisonQuantifyingPersonalPollutionImpactstoInformTransportSchemeInnovationthroughNewGenerationMobilityData

Animprovedunderstandingofthespatialandsocialdistributionofthehealthimpactsthatarisefromtheintroductionofnewtravelinitiativeswillsupportmoretargetedandefficientpolicydevelopment.Furthermore,oncethehealthbenefitsassociatedwithcertainschemeshavebeenreliablyquantified,policymakersmayfinditeasiertointroducepoliciesthatmightotherwiseproveunpopularwithcitizens(e.g.roadpricing,carparkingcharges,etc.).Tothisend,therecently-fundedHABITSresearchprojectwillinterfacenewgeneration‘TrackandTrace’informationonindividualcitizens’locationandmodechoices(detectedasmobilephoneapp-basedsensordata)incollaborationwithNewcastleCityCouncil,intheUK,toassesstheefficacyofexistingtransportpoliciesandtosupportthedesignofnewpolicies.Aprimeexampleofwheresuchresearchwillhavevalueisinthequantificationofthediseaseburdenassociatedwithoutdoorairpollution.IntheUK,exposuretooutdoorairpollutionisestimatedtocontributetoapproximately40,000deathseachyear[1].Thereisagrowingbodyofevidencethattraditionalexposureestimatesthatonlyconsiderresidentialpopulationsanddisregardmobilityaresignificantlyunderestimatingrealexposurestoambientpollution[2-4];totheextentthattheHealthEffectsInstituterecommendmodelsthataccountforpersonalexposureorincludetime-activitydatacanproducethe“best”estimatesofhumanexposure[5].ThispaperwillpresentthepreliminaryresultsfromongoingHABITSworkthataddressesthefollowingresearchquestion:“canhigh-resolutionlocationandactivitydata,coupledwithreliablemodelsofairquality,beusedtomoreaccuratelyquantifythetrueexposureofindividualstoairpollutionandderiverobustspatio-temporallyexplicitpoliciestoreducethisdiseaseburden.”Specifically,thepaperwillpresentapreliminaryriskmodel,highlightingthetimesandplaceswherehighlevelsofpollutionintersectthelargestambientpopulations.Thisnewknowledgewillcontributetotheprocessofcollaborativelyderivingrobustnewpoliciestoreduceexposurewhereitismostacute.References[1]RoyalCollegeofPhysicians(2016).Everybreathwetake:thelifelongimpactofairpollution.London:RoyalCollegeofPhysicians.[2]Dhondt,S.,C.Beckx,B.Degraeuwe,W.Lefebvre,B.Kochan,T.Bellemans,L.IntPanis,C.Macharis,andK.Putman(2012).Integrationofpopulationmobilityintheevaluationofairqualitymeasuresonlocalandregionalscales.AtmosphericEnvironment59,67–74.[3]deNazelle,A.,E.Seto,D.Donaire-Gonzalez,M.Mendez,J.Matamala,M.J.Nieuwenhuijsen,andM.Jerrett(2013,May).Improvingestimatesofairpollutionexposurethroughubiquitoussensingtechnologies.EnvironmentalPollution176,92–99.[4]Smith,J.D.,C.Mitsakou,N.Kitwiroon,B.M.Barratt,H.A.Walton,J.G.Taylor,H.R.Anderson,F.J.Kelly,andS.D.Beevers(2016).LondonHybridExposureModel:ImprovingHumanExposureEstimatestoNO2andPM2.5inanUrbanSetting.EnvironmentalScience&Technology50(21),11760–11768.[5]HealthEffectsInstitute,2010.Traffic-relatedairpollution:acriticalreviewoftheliteratureonemissions,exposure,andhealtheffects.SpecialReports.HealthEffectsInstitute,Boston,MA.4. LarissaSilvaLopes;BenjaminMotte-Baumvol;ThomasThevenin

UrbanmobilityandthespatialdistributionofeconomicactivitiesinRiodeJaneiro

IndevelopingcountriessuchasBrazil,theeconomicgrowthisgraduallyimprovingthepopulation'sstandardofliving,resultinginmajorimpactsonurbanmobility,particularlywithregardtomotorvehicleaccess.Between2001and2012,thetotalgrowthofthecarfleetinBrazilwasaround3.5million(Rodrigues,2013),with5.4%ofthisgrowthonlyintheMetropolitanAreaofRiodeJaneiro.Presently,thecityisexperiencingseverecongestionepisodes(Beyer,2012),duetosaturationofthemainroads,andafragmentedandinefficientpublictransportsystem.Foracountryineconomicdevelopmentinthe21stcentury,itisveryimportanttoplanandmonitortheevolutionofurbanmobilityinordertomakeitefficienttoitsusersandinaccordancewiththegoalsofsustainabledevelopment.Inthiscontext,landusetransportinteraction(LUTI)modelscanbeimportanttoolsinunderstandinghowthetransportsystemsaffectlandoccupation,whichmayexplainthespatialdistributionofeconomicactivities—andthetraffictheygenerate—intheterritory.Since2006,theFrenchresearchlaboratoryThéMAhasbeendevelopingoneofthesemodels,anagent-basedLUTIsimulationplatformcalledMobiSim,dedicatedtounderstandtherelationshipbetweenurbansprawlandtransportnetworks.OneofitsapplicationsistakingplaceinRiodeJaneiro,andconsistsinthesimulationofpossibleevolutionscenariosoftheurbangrowthandthetransportsysteminitsMetropolitanAreauntil2050.Thispaperwillpresentthefirstphaseoftheproject:adiagnosisoftheurbanmobilityandthespatialdistributionofeconomicactivitiesinRiodeJaneiro.Weaimtojointlyanalyzethreeimportantvariablesofurbanmobilityintheregion:theaccesstotransport,throughtherateofmotorizationandtheavailabilityofpublictransportation;thelocation(concentrationordispersion)oftheseactivities;andthesocio-spatialsegregation,stronglypresentinthecity.Bymodellingandanalyzingthespatialdistributionofhouseholdsandeconomicactivities,alongwiththetransportlines,weintendtobetterunderstandthemobilitygeneratorzonesandtheareasofprecariousaccessibility.Forthisdiagnosis,over4millionhouseholdsand380,000economicactivitiesweregeoreferencedfromtheNationalRegistryofAddressesofIBGE(BrazilianInstituteofGeographyandStatistics),allowingtheanalysisofthelanduseinaverydetailedscale,somethingunprecedentedinBrazil.Itisexpectedadirectlinkbetweenareasofprecariousaccessibilityandthemunicipalitiesthatpresentedthegreatestincreaseinmotorizationinthepastyears,andastrongconcentrationofthemobilitygenerators–especiallyformalemployments-inthehistoricalcenterandrichestpartsofRiodeJaneiro.Inacitymarkedbystrongsocio-spatialsegregationlikeRio(Silva,2012),thelackofaccesstotransportcanbeanimportantformofsocialexclusion(Lucas,2012).Therefore,weexpectthisprojecttobeanimportantscientificsupportforthelocalurbantransportationactors,indirectlycontributingtoreducingvulnerabilitycausedbyuncontrolledurbanizationandtoamoresustainabledevelopmentofurbanmobilityintheregion.

2A-HealthandLifestyle

1. PaulWilliamson;XinGu;KarynMorrissey;FerranEspuny-PujolSmall-areaestimationofcomorbidity:anindirectsurveycalibrationapproach

Withanageingpopulation,theprevalenceofpatientswithmultiplehealthissues(co-morbidity)isincreasing.Patientswithcomorbidityaremorecomplextotreat,placingadditionalpressuresonlocalhealthservices.Understandingthespatialdistributionofsuchpapersprovidesafirststeptoensuremoreappropriatedistributionofresourcesandprovisionofservices.Knowledgeoftheunderlyingprevalenceofcomorbiditywouldalsoallowidentificationofpatternsofunder/over-diagnosisandserviceuptake.However,atpresent,informationoncomorbidityisconfinedonlytothosethatself-presentandaredoctor-diagnosed.Theaimofthispaper,therefore,istoestimatethespatialdistributionofpatientswithcomorbidityatthelocallevel(UKLowerSuperOutputAreas).Theapproachtakenistocalibrateexisting

datafromanexistinghealthsurveyoftheprivatehouseholdpopulationtofitknownlocalpopulationattributes,suchasage,sexandsocio-economicstatus.Theresultspresentedherearenovelin(a)validatingtheresultingestimatesagainsttheclosestcomparablemeasuresofhealthavailablefromhealthadmindata;and(b)quantifyingtheuncertaintyassociatedwiththeseestimates.2. EmmaWilkins;MichelleMorris;DuncanRadley;ClaireGriffithsGeographicvariationinthevalidityoftwosourcesofsecondaryfoodenvironmentdata

Background:Secondarydata(i.e.datacreatedforpurposesotherthanresearch)onthelocationsoffoodoutletsisincreasinglyusedbybothresearchersandpolicymakerse.g.toexaminegeographicdisparitiesinfoodenvironments,toidentifyareaswhereaccesstohealthyfoodislimited,andtoexplorelinksbetweenfoodenvironmentsandhealthoutcomessuchasdietandobesity.However,evidenceforthevalidityofUK-specificdataislimited,withlittleknownaboutpotentialgeographicbiases.ThisstudythereforeexaminedvariationinthevalidityoftwoUKsourcesofsecondaryfoodenvironmentdata(SFED):OrdnanceSurveyPointsofInterest(POI)dataandfoodhygienedatafromtheFoodStandardsAgency(FSA),acrossdifferentenvironmenttypes,withstreetauditsusedasthe‘goldstandard’.Methods:Auditswereconductedacross54LowerSuperOutputAreas(LSOA)inEngland,coveringsixenvironmenttypes(characterisedbyrural/urbanclassification,andthreelevelsofdeprivation).AllstreetswithineachLSOAwerecoveredtoidentifyallfoodoutletstherein.Audit-identifiedoutletswerematchedtooutletsintheSFEDtoidentifytruepositives(TP:outletsinboththeSFEDandtheaudits),falsepositives(FP:outletsintheSFEDonly)andfalsenegatives(FN:outletsintheauditsonly).Agreementwasassessedusingpositivepredictivevalues(PPV:TP/(TP+FP))andsensitivities(TP/(TP+FN)).PPVsreflecttheprevalenceoferroneousentrieswithintheSFED;whereas,sensitivitiesreflecttheprevalenceofmissingdata.Bothstatisticsvarybetween0and1,withhighervaluesindicatingbetteragreement.VariationinPPVsandsensitivitiesacrossenvironmenttypeswasassessedusingChi-squaredtests.Results:ForPOIdata,PPVswerestatisticallysignificantlyhigherinrural(0.85,CI:0.81-0.89)thaninurbanenvironments(0.76,CI:0.73-0.79,p<0.001).TherewasalsoatrendforPPVtodecreasewithincreasingdeprivation,althoughthiswasnotstatisticallysignificant.Sensitivitywasnotassociatedwithenvironmenttype.FortheFSAdata,neitherPPVsnorsensitivitieswereassociatedwithanyenvironmenttype.Conclusion:ThereisnoevidenceofgeographicbiasinFSAdata,providingconfidenceinusingtoolsandresearchbasedonthesedata.Conversely,geographicbiasappearstobepresentinPOIdata,withPPVshigherinruralthanindeprivedareas.ThismeansPOIdatahasahigherprevalenceoferroneousentriesinurbanareascomparedtoruralareas,whichmayreflectahigherturn-overoffoodoutletsinurbanareas.Densitiesoffoodoutletstendtobehigherinurbanthaninruralareas,andthebiaswithinthePOIdatatendstoamplifythisdisparity.Forexample,thePOIdatasuggestedthattheratioofoutletsinurbanareastoruralareaswas2.0,whereastheauditsindicatedaratioofonly1.7.Suchbiascouldleadtoconfoundingifnotappropriatelycontrolledfor.Theabovenotwithstanding,consideringPOIhasadvantagesoverotherSFED(e.g.moredetailedoutletclassificationsandbetterspatialaccuracy),POIisstillausefulandrecommendedsourceofdata,providedthisgeographicbiasisconsideredandappropriatelycontrolledfor.3. ElenaGukModellingoftheterritorialtouristandrecreationalsystemofNorilskRegion

Inearly20thcentury,manyareasoftraditionallanduseintheArcticwereturnedintonewindustrialareas.OneofthelargestminingplantsinSovietUnion,builtabove69°N,causedemergenceofNorilsk,andnowit’sthesecondmostpopulatedcityabovetheNorthernCircle,estimatedasoneofthemostpollutedsettlementsintheworld.Remotenessofthenewlybuiltareaincombinationwithhazardousclimateandindustrytriggeredemergenceanddevelopmentoflocalrecreationalpractice.Tourismandrecreationare

studiedthereinacombinationbecauseoftheirhighinterconnectivityintheregion:mosttouristsinthesystemareNorilskresidentswhosemainpurposeoftravel–withintheregionoroutofit–istorecoverandimprovetheirhealth.Thefirst-stagepurposeofresearchwastorevealpeculiaritiesofrecreationdevelopmentinNorilskRegion,whichisdefinedinthiscaseasareaofaccessibilitybylocaltransportfromNorilsk.TheresearchhasshowedthatthisregionincludesneighborhoodsofNorilsk,seaporttownDudinkaandlakesofwesternPutoranaPlateau(roughly100-200kmfromthecity),andthatdozensofrecreationalcampshadbeenbuilttheresince1940s.Thefieldwork,donein2013,hasresultedinidentifyingdifferentformsofoutdoorrecreationalactivitiesandfacilitiesintheregion.Ithasbeenconcludedthatinchangingeconomicandsocialconditionslocaltourismandrecreationhasshoweditsresilience:itremainsanessentialpartofeverydaylifeanddeterminantofwell-beingofregioninhabitants,notonlyduetoitsnecessityforhealthcareinecologicalconditionsbutalsobecauseofunderdevelopmentofotherservices,includingpublictransportation(bothinterregionalandlocal),andwaysofspendingfreetime.Thus,tourismandrecreationplaysimportantroleinsustainabilityofNorilskRegionasanactivitythathelpstobalanceeconomicprioritiesofNorilskNickel,ecologicalneeds(areaisalmostnotdamagedbyrecreationalactivities)andhumanneedstorecover.ThesecondstageoftheresearchistorevealtheprincipleshowthetouristandrecreationsysteminNorilskRegionisbuilt.Theresearchshowedthatitconsistsofsuchelementsascustomers(localsandincomingtourists),touristattractions(naturalandhuman-madesights,NationalReserve,museumsetc),tourisminfrastructure(accommodation,transport),subsidingorganizations(NorilskNickel)andgovernment.Aslongastouristandrecreationsystemintheregionsignificantlydependsoneconomicandlegalissues,thesystemishighlyinterconnectedwithoverallstructureofregionaleconomy,healthcaresystem,well-beingandefficiencyofhumanresourcesandthussustainabilityofeconomyandcommunity.Thehypothesisoftheresearchisthatusingmethodsofmodellingingeography,withmodeltestingonanotherregions,canshowthemostefficientand/orsustainablemodeloftourismandrecreationsysteminNorilskRegion,whichprobablycanbeusedforanotherregions.4. NikLomax;MichelleMorrisAssessingactivitylevelsofindividualsinalarge,self-selectingdataset

Thispaperpresentsfindingsonthephysicalactivitylevelsof~500,000individualswhoareregisteredtouseBounts,alifestyleandfitnessappwhichlaunchedin2013.Bountsallowsuserstoconnectotherfitnessappsandwearabledevicesdevelopedbythirdparties(e.g.Fitbittrackers),aswellasofferingitsownsmartphoneappwhichcanbeusedtotrackstepsandcheck-inatnumerouslocationswhereexerciseandfitnessactivitiescanbeundertaken(e.g.gymsandclimbingwalls).Thetechnologyincentivisesuserstoundertakephysicalactivity,astheyareawardedpointswhichcanbeexchangedfordiscounts,offersortheopportunitytojoinvariouschallenges.Thesedataprovideinsightintothelevelandtypeofphysicalactivity,engagementwithtechnologyandspatialmobilitypatternsofalargegroupofindividualsovera12monthtimeperiod.Physicalactivityisaleadingriskfactorforavarietyofnon-communicablediseasessuchasdiabetes,cardiovasculardiseaseandcancer(WorldHealthOrganisation,2015).Globally1in4adultsarenotactiveenough.Insufficientphysicalactivitycontributesdirectlytooverweightandobesitywhicharerecognisedasaglobalepidemic(Caballero,2007),contributedtobyobesogenicenvironments(LakeandTownshend,2006)andincreasinglysedentarylifestyles(JebbandMoore,1999).Latestestimatesputthenet-costofhealthcareandwelfareforoverweightandobesepeopleintheUKat£2.47billionperannum(Tovey,2017).Partofthesolutionfortacklingtheobesityproblemisthroughtechnologicalinterventions(Bacigalupoetal.,2013).Thispaperaddstothisdebatebypresentingfindingsfromanongoingschemewhichincentivisespeoplewhosignuptobecomemoreactive.Inordertodothis,itallowsuserstoengagewiththeschemeusingawiderangeoftechnologies,fromwearabletrackerstosmartphoneapps.Wepresentthedemographicprofileoftheseusers,investigatedifferencesbetweengroups,overtimeand

acrossgeographies.WepresentsomeinsightsintohowschemessuchasBounts,inwhichusersself-selectandareincentivisedtobephysicallyactive,mighthelpincreasefitnesslevelsinsomegroups.Keyfindingsincludecorroborationwithexistingliteratureaboutthegenderbalanceofuptakeforhealthandwellbeingtechnologyandthenumberofstepstakenonadailybasis.Thereareclearseasonalpatternsinthelevelofactivityindividualsundertakeandnotablechangesinthenumberofusersasmarketinginterventionsareintroduced.Thereisapositiverelationshipbetweenageandaveragestepstakenperday,contrarytofindingsreportedinpreviousstudies.5. RachelOldroyd;MarkBirkin;MichelleMorrisTheuseofnon-traditionaldataformonitoringfoodborneillness:methodologicalrecommendationsandconsiderations

TheFoodStandardsAgency(FSA)estimatethat1.7millioncasesoffoodborneillnessarecontractedeachyearintheUK,ofwhich22,000casesresultinhospitalisationand700resultinfatality(Tametal.,2014).Theburdenoffoodborneillnessonthepopulationandeconomyremainsunacceptablyhighandthereareseveralproblemsassociatedwiththewaytraditionaldataisusedtomonitortheproblem.AsmanyvictimsoffoodpoisoningneitherreporttheirillnessnorvisittheirGP,nationalsurveillancedataseverelyunderestimatetheincidenceoffoodborneillness.GPdatapublicationtakesapproximatelytwoweeksfollowinganappointment.Subsequentlythedataisunsuitableforthetimelyidentificationofharmfulpathogens(Achrekaretal.,2012).Inrecentyears,Twitterhasemergedasausefulsourceofinformationformonitoringandmappingbothdiseaseandpublichealthoutbreaks.Availableinnearrealtime,Twitterdatacanbeattributedtoaspecificlocationandcanincludeawealthofinformationnotavailableintraditionaldata.Studieshaverangedfrommappinginfluenzaoutbreaks(Culotta,2010)tomonitoringdentalpain(Heaivilinetal.,2011),howeverstudiesfocusingonfoodborneillnessarelimited.Thisstudyusedsystematicscopingmethodstoretrieveandanalyseallprimaryresearchconcernedwithmonitoringpublichealthanddiseaseoutbreaksusingnon-traditionaldatasuchasTwitterandrestaurantreviews.Emergingfromthisstudyweremethodologicalrecommendationsforusingnon-traditionaldatatocalculatetheincidenceofapublichealthailmentordisease.Thispaperwilldiscusstheserecommendations,andconsidertheuseofnon-traditionaldatatomoreaccuratelyquantifytheincidenceofdiseaseandtofurtherinformfoodestablishmentinspectionproceduresintheUK.Acknowledgements:ThisresearchissupportedbytheEconomicandSocialResearchCouncil[grantnumberES/J500215/1].

2B-Spatialanalysis:Applications

1. NirFulman;ItzhakBenensonWhyDoWeSearchforParkingforSoLong?

Thereasonwhyparkingsearchinthecitycenteristoooftenadisasterisevident-demandforparkingthereisalwayshigherthanthesupply.Analyticalmodelspredictlowaveragecruisingtimeuptoaveryhigh,ca.98%,averageparkingoccupationrate,whileinsimulationscruisingtimeishighstartingfromca.90%occupation(Levy,Benenson,2013).Weclaimthatsimulationsbetterreflecttherealityanddemonstratethatlongparkingsearchiscausedbyinevitablespatialheterogeneityofparkingdemandandsupplythatismissedinanalyticalmodels,andpresentanewanalyticalmodelofcurbparkingsearchwhichoutcomesarefullycompatiblewiththesimulationresults.Ouranalyticalmodelofparkingsearchconsidersgroupsofdriversthatsearchforparkingτ=0,1,2,…time

units,τ≤τmax,ina“gridcity”,anddescribesdynamicsofthenumbersofcruisingcars,occupiedparkingspotsandcarsthatsearchedforlongerthanτmax.Oursimulationmodelconsidersagridcitywitharatioof1:80betweenthenumberofbuildingsandparkingspots.Toreflectoveralldemand(T)tosupply(P)ratioT/P=q<1weassumethateachbuildinggenerates80*qparkingdemand.Weassumethatthedemandforparkingisessentiallynon-uniformand,givenq,thedemandforafractionsofbuildingsis(q+α)*80,whereq+α>1.Interestingcasesarethoseofhighq,asq>0.9,andessentialfractionofbuildingswithhighdemand,i.e.,s~0.5.Thedynamicsofparkingarestudiedfordifferentdrivers’parkingsearchtactics,suchasconstantsearchroutearoundthedestination,orrandomwalkbiasedtowardthedestination.Withbothanalyticalandsimulationmodelswedemonstratethat,irrespectiveofthesearchtactics,parkingsearchtimeisdefinedbythevaluesofsandαthatreflectheterogeneityofdemand.Forsufficientlyhighsandαevenforarandomallocationofbuildings,thoseofhighdemandformoneormorelargeclusters.Withinandaroundtheseclusters,thenumberofcarsthatarriveandsearchforparkingisessentiallyhigherthanthenumberofparkingspotsthatarevacatedduringτmax.Forexample,forq=0.95,α=0.2ands=0.5,ca.12.5%ofthedriverswhosedestinationsarewithinclustersofhighdemandcruiseforlongerthan10minutes.Attheleveloftheentirecitythisresultsin5%ofthedriverscruisingforτmaxandlonger,despiteclosetozerosearchtimeincasethedestinationbuildingislocatedbeyondalargeclusterofbuildingswithhighparkingdemand.WeapplyourmodeltotheIsraelicityofBatYamandshowthatdespitealow,ca.70%,averagedemandtosupplyratio,spatialheterogeneityofthedemandandsupplycauseessentialfractionofdriverstosearchforparkingfor5ormoreminutes.Wethendiscussparkingpoliciesthatcouldreducecruisingforcurbparkinginthecity.2. YonatanShaham;ItzhakBenensonModelingCity-ScaleFireSpreadinMediterraneanandMiddleEasternCities

Firesareamajorrisktolifeandproperty.Firefightingservicesareestablishedtocopewithroutineof3-5structuralfiresperdaypermillioncitizens.However,inacaseofseveralsimultaneousfires,firefighters’capacityisinsufficient.MultipleIgnitions(MIs)canbecausedbyseverewildfires,industrialcatastrophes,earthquakes,orduringwarandrocketlaunching.MIsposegreatthreattocitiesandtheonlychoiceistoprioritizefirefighters’reactionanddelayorevennotrespondtofiresoflowerpotentialdamage.NewstrategiesandpoliciesofMIsmanagementshouldbesoughttoestablishthesepriorities.Thesestrategiesandpoliciescannotbeassessedbysimpleextrapolation,sincespatio-temporaldynamicsofMIsandthelackoffirefightersshouldbeaccountedfor.Wethusneedhigh-resolutionspatially-explicitmodeloffirespreadincities.Existingcity-scalefirespreadmodelsweredevelopedforUSAandJapanwherethemajorityofconstructionsareflammable.OurstudypresentsamodelofforMediterraneanandMiddleEastern(MME)cities,wherealmostallconstructionsarenon-flammableandfirespreadbetweenbuildingsisdefinedprimarilybytheman-plantedflammablevegetationbetweenconstructions.TheMMEmodelaccountsfortheinternal3Dstructureandventilationschemeofeverybuilding.Incasebuilding’splanisunavailable,weestablishitanew,applyinganovelalgorithmforpartitioningbuildings’footprintsintoapartmentsandrooms.Outdoorfirespreadisdefinedinthemodelbytheatmosphericconditionsandspatialpatternandtypeofvegetation.Themodelrevealsthat,unlikeUSAandJapan,firespreadinMMEcitiesishighlysensitivetospatialpatternofvegetationnearthebuildings.ImplementingthemodelinHaifa,Israel,wefoundthatignitionofvegetationisresponsibleforover60%ofthebuilding-to-buildingfirespreadevents,whiletherestwereduetocombinationofvegetationignitionandfirebrands.Nobuildingwasignitedbyradiationfromotherbuildings.ThisisdifferentfromtheresultsobtainedinthesimilarmodelstudyintheUSA,wherevegetationisresponsiblefor30%ofbuilding-to-buildingsfirespreadcases,whiletherest70%arecausedbydirectcontactandradiation.Wefurtherdemonstratethatpropermanagementofurbanvegetationin

Haifacanpreserveitscoveragewhilereducingfirespreadtherebyalmost90%.Thegoalofthecurrentmodelistoconstructthemapsofrisksandexpecteddamages.Theriskmapsrepresenttheprobabilitythat,givenweatherconditions,afirethatstartedinacertainbuildingwillspreadtothebuildingsaround.The(dynamic)mapsofdamagerepresenttheexpectedfireareatminutesafterthefirestartsandisnotmanaged.Bothseriesofmapsservefirefightersforestimatingtheurgencyofreactiontothefire,dependingonitslocationinthecity,duringMIscenarios.Ournextstepistoextendthemodeltowardsspatially-explicitagent-basedmodelthatincludesfirefighteragentsandtheirresponsetothefiredynamics.ThemodelwillbeusedasaSerious-GameenvironmentfortrainingfirefightersinscenariosofMIscausedbythewildfiresandearthquakes.3. MaximeColomb;MickaelBrasebin;JulienPerret;TannierCecileSimulationofarealisticresidentialdevelopmentwiththeintegrationoftwoexistingmodels

Inaglobalcontextofurbansprawlandincreasinghousingcosts,policiesforresidentialdevelopmentareanimportantaspectofurbanandregionalplanning.Therearetwomajorplanningconcerns:firstly,toensurethathousingsupplymeetshousingdemandbothquantitativelyandqualitatively,andsecondly,toreducethenegativeenvironmentalimpactsthatmayresultfromresidentialdevelopment,especiallyanincreaseinthelengthandthenumberoftripsbycarandthefragmentationofnaturalandagriculturalareas.InFrance,severalplanningdocumentsexistthataimtocontrolresidentialdevelopment.RegionalplanningpoliciesarecontainedintheSchémadeCohérenceTerritorial(Territorialcoherenceframework)orSCoT,andthePLH(LocalHousingPlan).Inaddition,eachlocalauthorityhasitsownlocalurbanmasterplan(inFrench,PlanLocald'Urbanisme-PLU).ThePLUdefinesplaceswhereresidentialdevelopmentscanoccurandalsospecifiesthedesignparametersofallowablebuildingsonthesite,suchasbuildingheight,orientationtoexistingcontext,andbuildinglocationwithintheparcel.Aproblemariseswhileapplyingtherecommendationsandthezoningscontainedineachofthesedocuments.Theformandlocationofresidentialdevelopmentsasdefinedintheseplanningdocumentswilloftencontradictoneanother.Inthispaper,wepresentaspatialsimulationplatformtosupporttheintegrationofregionalandlocalplanningpoliciesthataimtocontrolresidentialdevelopment.Thissimulationplatformintegratestwomodels:MUP-CityandSimPLU.MUP-Citysimulatesscenariosofresidentialdevelopmentforanentireurbanregiongivenasetofplanningrules.Thefirstruleensuresthatthepatternofcellsworthurbanisingisfractal.Theotherplanningrulestakeaccountoftheproximityofroads,accessibilityofshopsandservices,accessibilitytopublictransport,proximityofopenspaces,andthepresenceofareasthatcannotbebuilton.Theidentificationofcellsworthurbanisingtakestheformofarastermapofpotentialbuildingcells,eachcharacterisedbytheworthofurbanisingthem.Howworthwhileitisforacelltobeurbanisedrangesfromzerotoone.SimPLUsimulatesthepotentialbuildingpossibilitiesinaclusterofhousesbygenerating3DbuildingconfigurationsthatrespectstheconstraintsofthePLU.Thosepotentialbuildingsmaximizefunctions,forexamplefloorsurfaceandareinsertedintostaticparcels.CouplingMUP-CityandSimPLUimpliesseveralmethodologicalchallenges.WearefocusingonhowtoselectoneregionalspatialconfigurationofresidentialdevelopmentamongallconfigurationsthatcanbesimulatedwithMUP-City,withtheintentofusingthisdataasinputforasimulationwithSimPLU.WetesttheapplicationofthesimulationplatformontheurbanregionofBesançon,knownasGreaterBesançon,ineasternFrance.Thisurbanregionincludesacorecity(117,000inhabitants)andperi-urbanresidentialareaswithlowpopulationdensitiesspreadacrossfifty-eightsmalllocalauthorities.Thesimulationplatformwasusedinthiscontexttoexplorethepossibleoutcomesofspatialplanningpolicies,setbyboththeGreaterBesançonauthorityandeachlocalmunicipality,onresidentialdevelopmentoveratwentyyearperiod.

4. MilanaGlebovaStructuralandspatialchangeinelectricalindustryoftheUKinthebeginningof21century

TheUKpowerindustryhaschangedconsiderablysincetheendofthe20thcentury.Thispaperconsidersthepowersupplyreliabilityincreasethroughenergyresourcediversificationandpromotinglocalenergysources(includingrenewableones)whichisnoteasyforanislandcountrywithfewgridlinkstoneighboringcountries.Weconsidertheimpactofthepowerindustrygeographicredistributionandtheindustryreformpolicies,thattheUKhadestablishedpriortothesubjectbecamethemainstreamincontinentalEurope.TheexperiencegainedhasbeenappliedtosubsequentEUreforms.WewillspecificallyconsidertheUKcross-borderpowertransmissionandelectricitytrade.SuchatradeisextremelyimportantfortheofthelargestelectricityimportersinEurope.Statisticalanalysisandpowerdistributionmapshaveenabledtocometoaninterimconclusion:First,theUK(mostlyEnglandandWales)isasuccessstoryoftheindustry’smarketreforms.Theconsumerpricedropwasshort-lived,however,theintroductionofcompetitioninthepowerindustryandestablishingamarketplaceforelectricitywasapromptandsuccessfulmove.Second,thepowergenerationstructurebysourcehaschangedconsiderably:besidesunequivocalrejectionofcoal,large-scaleintroductionofwindandotherrenewablesources,thecountryhasalsoreducedtheamountofimportedgasusedforpowergeneration(since2000,ithasbeencutbyhalf).Third,despitesomeattemptstoensureenergyindependence,thereisnorealopportunitytobecomeself-dependentinthefuture:thecountryisoneofthelargestelectricityimportersinEurope,anditssuppliesfromFrance(mostly)coverthepowershortage.Fourth,thepowergenerationstructurechangesdirectlyaffectitsgeography:Shuttingdown(orconversingtobiofuel)thecoal-firedpowerplantswilldamagetheentirehistoricalbeltofcoalgenerationfacilitiesintheMidlands.Fifth,despitetheproblemswithelectricpowerself-dependenceandhighenvironmentalsafetystandards,theUKkeepshouseholdelectricitypriceatEurope‘saverage;thepricehasevenslightlydecreasedsince2015.Besides,thepriceincludesalargeshareoftaxesandlevieswhichareusuallyaburdenforconsumersinthecountrieswithextensivealternativepowersources;still,thegenerationcost(andthefuelcost)isnaturallyhigh:thecountrylacksalotoflocalandcheapenergysources.5. HadrienFouilladeOrsini

CrimeslocationwithinspatialpatternsinsuingbymotorizednetworkinMarseille(SouthernFrance)

Duringthe1920’sMarseillewascalledthe"FrenchChicago"duetothepresenceofanactivedisreputablebackground.Illegalactivitiesweremainlylocalizedwithincentralneighbourhoodsnamedas"LePanier"and"L’Opera"bothcontiguoustotheports(Montel,2014).Seeingthattheimportanceofdrugstraffickingparticipatesintothebadreputationofthecity,MarseilleisconsideredasthemostcriminalcityinFrance(Peraldietal.,2015).Marseilleisoftendescribedasacitydividedintotwoparts:Anorth–concentratingapoorpopulation,withsocialissuesrelatedtopovertyanddrugstrafficking,andasouthwhereawealthyhouseholdersaresupposedtogathertogether.Publicauthoritypolicyhasdefinedseveral"sensitiveurbanzones"replacedin2014bynew"prioritydistricts".Thoseareashavebeendelimitedbythelegislatoraccordingtotheconcentrationofpovertyusinga200metressidelengthgridreference.Suchdistrictsincludeneighbourhoodswheremajorityofresidentialpopulationhasfinancialresourcesinferiorthan60%ofthefrenchaverageincome.Frenchinnercitypolicyisparticularlyfocusedintheseareas.InMarseillethereare35"prioritydistricts"covering35squarekilometreswherelives28%oftheglobalcitypopulation.“Settlementsofscores”betweenthecriminalgroupshasbeenusedtoidentifycriminogenicspaces.Suchcrimesarerelativelyuncommon–about15factsayear–tojustifyacompletemediacoverage.Neverthelesstheyareenoughfrequenttoenablethedistinctionofrelevantcrimehotspots.Thelocationoftotal“settlements”committedfromJanuary2010toJanuary2016andclassifiedbythepoliceofthe

departmentshowstheirconcentrationinreducedpartsinsidethemunicipality'sboundaries.theillicitnarcoticsmarketrelatesallofthesemurderswithinthemetropolis.Themainlocationsarehousingestates,mostlybuildinthenorthernarrondissements.However,notallhousingcomplexeswereaffectedbycrime.Thepurposeofthisresearchistoexplaintheinteractionbetweencrimespatialpatternswithaccessibilityandurbanstructurebyusinganetwork-orientedapproach.Whenanalysingnetworkstructurewecandefineanurbanhierarchydistinguishingintegratedareasandtheiroutskirts.FreetoolboxessuchasSpatialDesignNetworkAnalysisandUrbannetworkAnalysisavailableinQuantumgisorinArcgisareusedtomeasureroadnetworkindexes.MathematicalnetworkanalysisiscarriedoutatdifferentmetricsandangularintervalsinordertounderstandtherelationbetweenspatialpatternsandcrimeslocationinMarseille(Hillier,2004).Literatureexposes,inotherspatialcontexts,atypologyofurbanpatternsandtypesofcrime(Alford,1996),theaimofthisstudyistoexplainthelocationofthemurdersbyphysicalcharacteristicsofthebuiltenvironment.REFERENCESALFORDValery(1996),“CrimeandSpaceintheInnerCity”,UrbanDesign.Studies,nr.2,45-76.HILLIERBill(2004),Canstreetsbemadesafe?UrbanDesignInternational,Vol.9,Issue1,pp31-45.MONTELLaurence(2014),“MarseilleChicago”:naissanced'unereprésentation,FaireSavoirs,n°11,2014PERALDIMichel,DUPORTClaire,SAMSONMichel(2015),SociologiedeMarseille,Paris,LaDécouverte,coll.“Repèressociologiques”,124p.

SpecialSession–BigDataandObesity

1. MichelleMorris,MarkBirkinWhatistheESRCStrategicNetworkforObesity?

OverweightandobesitypersistintheUK,andworldwide,placingbothpersonalandeconomicstrainonsociety.Thestrongassociationofexcessweightwithotherchronicconditionssuchastype2diabetesandcardiovasculardiseasefurtherincreasethisburden.Theemergenceofnewformsof‘bigdata’providenovelopportunitiestounderstandwaysinwhichtheenvironmentpromotesordiscouragesbehavioursassociatedwithobesity.Suchdatainclude:thenumberofgymmembershipsinanarea;proximitytogreenspace;consumerbehaviourtrendsforfoodpurchases;GPStrackdatafrommobilephonesorphysicalactivityapps;andmanymore.TheESRCStrategicNetworkforObesitybringstogetherexpertsfromallovertheUK,andbeyond,fromarangeofdisciplinesinacademia,retail,localgovernmentandcharitiestoexploreandgenerateideastomakebestuseofthewealthofdatatoinformpositivechange.Thefocusofthenetworkistowardsunderstandingelementsoftheenvironmentwhichbothpromoteanddiscouragethelikelihoodofbecomingoverweightorobesewithviewtopositivelyimprovetheseforahealthierfuture.Thistalkwillprovideanoverviewofthenetworkactivitiestodate.2. DuncanRadley;KateTimmins;MarkGreen;JamiePearceCan‘bigdata’contributewheretraditionalresearchfallsshort?Areviewofbigdatausageinobesityresearch

Facilitatedbyadvancesincomputinghardware,softwareandnetworking,‘bigdata’havebeenheraldedasapowerfulnewresourcethatcanprovidenovelinsightsintoobesity.Thereisnosingleagreeddefinitionofbigdata,butitiswidelyacceptedthatgreatervolume,complexity,coverageandspeedofavailabilityarecriticalcomponents.Often,thesedataaredrawnfromnon-traditionalsources,whicharenotoriginallyintendedforacademicresearch.Despitethegrowinginterestandhypesurroundingthesenewformsofdata,therehasbeennoattempttosynthesiseevidenceoftheiruseinobesityresearch.Theaimofthis

paperistoreviewthecontributionoftheseformsofdatatoobesityresearchtodate,andconsidertheimplicationsforthefutureof'bigdata'inthisfield.Anarrativereviewwasconductedtoidentifyandcollatepeer-reviewedresearchstudiesinwhich’bigdata’wereadoptedtoinvestigateobesity-relatedoutcomes.DatabasesearchesconductedbetweenJanuaryandApril2017foundoriginalstudiesusingavarietyofdatatypesandsources.Theseincluded:socialmedia;transportdata;geospatialdata;point-of-purchasesales;andcommercialdata.Anarrativesynthesisidentifiedthreesignificantwaysinwhichthesenewformsofdatacancontributeoverandabove‘traditional’data:firstly,inmovingbeyondconstraintsinscope(intermsofcoverage,size,andtime);secondly,inprovidingobjective,quantitativemeasureswheretraditionalresearchhashadtorelyonself-reporteddata;andlastlyinitspotentialforevaluatingreal-worldinterventions.However,severalissueswerefoundincluding:dataquality;inferenceandinterpretation;dataprocessing,managementandlinkage;andethicalquestionsaroundaccesstoandownershipofdata.Therewasapredominanceofdescriptivestudies,ratherthancausalorexplanatoryresearch,limitingthequalityofresearchfindings.Itisclearthatdespitesomeinitialprogressandpromiseinworkingwithbigdata,theimpactonresearchandpracticeinobesityhassofarbeenlimited.3. EmmaWilkins;MarkBirkin;ClaireGriffiths;MichelleMorrisCanbigdatasolveabigproblem:theobesitydatalandscape?

ObjectiveThebasicdriversofobesityaresimple(moreenergyconsumedthanexpended).However,theaetiologyiscomplex.TheForesightObesitySystemsMapdepictsover100inter-relatedfactorsthatoperateonmultiplelevelsanddomainsincludingindividual,societalandenvironmentalfactors.Policymakersarenowadvocatingfor‘wholesystems’approachestoobesity,targetingthewholeobesogenicsystemratherthanisolateddrivers.However,keytosuchapproachesisunderstandinghowthesysteminteracts.Suchdataareinherentlydifficulttocollect,especiallyonalargescale.Withalmost10yearssincethepublicationoftheForesightReport,mostresearchstillfailstoconsidertheinterplaybetweenfactorsspanningmultipledomainsonanationalscale.However,newandalternative‘bigdata’sourcescanbeusedtofillthisresearchgap.Theobjectivesofthispaperaretodevelopaframeworkinwhichtoeffectivelyreportbigdataforuseinawholesystemsobesitylens,tousethenewframeworktoreportindicativedataexamplesinlinewiththeForesightdomainareasanddiscusskeychallengesassociatedwithusingalternative‘bigdata’sources.MethodsSynthesisofexpertopinion,collatedfromaseriesofnationalworkshopsfundedwithintheESRCStrategicNetworkforobesity,culminatedinthedevelopmentofareportingframework‘BEE-COAST’forreportingthefeaturesofanewlargedatasets.ResultsSevenexemplarytypesof‘bigdata’wereincludedinthisreviewasprovidingvaluabledatafor‘WholeSystems’obesityresearch:(i)OrdnanceSurveyPointsofInterestdata;(ii)FoodStandardsAgencyfoodhygienedata;(iii)supermarketloyaltycarddata;(iv)physicalactivityapplications/wearables;(v)newtechnologiestorecorddiet;(vi)Acxiomdata;and(vii)CallCreditdata.ThefeaturesofthesedatasetsweresummarisedinaccordancewiththeBEE-COASTReportingFramework.ConclusionsPublichealthandacademicdecisionmakingisvulnerabletothelimitationsoftheavailableevidence.Reductionistideologyhasledtopolicyandpracticeoftentargetingindividualisedandisolatedcausesof

obesity.However,thereisawealthofexistingandemergingbigdatathatcanbeusedtohelpunderstandthemechanismsdrivingobesityfromawholesystemsperspective.4. ThomasBurgoine:PabloMonsivaisDevelopmentofdata-driventoolsforpolicymakers:TheFoodenvironmentassessmenttool(Feat)asacasestudy

Data-driventoolsarebecominganestablishedwaytodrivediscoveryanddevelopmentoftheoryandhypothesesamongresearchers.Thesetoolsalsopresentanopportunityforknowledgetranslationwithpolicyaudiences,whoareincreasinglyusingdataandevidencetoinformpolicy.Aswebtechnologieshavematured,moreexamplesofdata-driventoolshavebeenpublishedonline.Thesetoolscantakedifferentforms,fromsimplyprovidinginformationoncurrentconditions(‘whatis’)totoolsthatcanquantitativelymodelcounterfactual(‘whatif’)scenarios.Data-driventoolscanhelppublichealthprofessionalsunderstandthegeographicandsociodemographicdistributionofriskfactorsandquantifysocialandenvironmentalconditionsthatarerecognisedupstreamdeterimantsofhealth.However,therearefewavailabletoolsthatfocusonaspectsofthebuiltenvironmentthatshapebehaviouralriskgenerally,anddietaryintakeinparticular.Networkresearchershaverecentlydevelopedaninteractivedata-driventoolforpolicyrelatedtothefoodenvironment:theFoodenvironmentassessmenttool(Feat).Featwasdevelopedtoaddressaneedforpublichealthagenciestomeasureandmonitorthelocalfoodenvironment.ThistalkwillfocusonthedevelopmentofFeat,asacasestudy:frombasicscience,torealisationofneed,toalphadevelopment,testing,andfeedback,tobetadevelopment,launchofaminimumviableproductwithpublicitystrategy,toourintendedstakeholderengagementandimpactevaluationplans,explorationandacquisitionoffurtherfunding.Challengesincludedtheneedfortechnicalandwebexpertise,feedbackintegrationand‘missioncreep’,identificationoffundingsources(seedandongoing)andappropriatefundingmodels,needforfurthermaintenanceanddevelopment,andprovisionoftrainingandusersupport.WedrawparallelsandcontrastsbetweenFeatandthePropensitytoCycleTool(PCT),whichwasalsodevelopedbyNetworkresearchers.Finallyweattempttoderivesomegenerallessonsthatmayapplytochallengesassociatedwiththecreation,disseminationandsustainingofdata-driventoolsforpolicy.Wecontendthatdespiteanumberofchallenges,suchtoolspromisealevelofconnectionbetweenresearchersandarangeofaudiencesthatisarguablydeeper,broaderandarguablythatbettermeetsourobligationsaspubliclyfundedresearchersthancouldbeachievedbymostothermeansofknowledgetranslation.

3A-Geographyandeconomics1

1. MehdiBida;CelineRozenblat;ElfieSwertsModelinghierarchyandspecializationofasystemofcitiesasaresultofthedynamicsoffirms'interactions

Thetwomaincharacteristicsofsystemsofcitiesarethesizedistributionandthespecializationofthecities.Thesecharacteristicshaveextensivelybeenstudiedbygeographers(Christaller,1933;Berry,1964;Pred,1977;Bourne,1984;Pumain,1982;Batty,2005;Pumainetal.,2006),andmorerecentlybyphysicists(Makseetal.,1995;Schweitzer,&Steinbrink,1998;Bettencourtetal.,2007).AllthisliteratureunderlinestheremarkableconstancyinspaceandtimeofZipf’slawofdistributionofcitysizes(Zipf,1941).Alongsidetheurbanhierarchy,thedegreeofcities’economicspecializationfollowsanoppositetrendtothesize.Ithasbeenshownthatthesoleaccountofthediffusionofinnovationsacrossthenetworkthatcitiesform,isnecessaryandsufficienttoreproducetheobservedcharacteristicsofurbansystems(Buraetal.,1996;Pumain,Sanders,2013).However,thedevelopedmodelsalwaysconsideredtheentirecityastheunitin

theurbansystemlikeintheSimpopmodel(Pumainetal.,2017).Inourapproach,weproposeamodelwherethecitiesformingtheurbansystemaretheresultsofmicro-agents’interactions.Thismodelalsoaimsreproducinghierarchyandspecializationofurbansystems,butwithacompletebottom-upapproachfromthemicro-agents,tothemesolevelofeachsinglecityuntilthemacrolevelofsystemofcities.Weuseanagent-basedmodelwhereagentsarefirmsthatmakeevolvetheircooperationnetworkforinnovationinageographicalspace.Innovationsstemfromafirm,oragroupoffirmsthatcooperateandpropagateacrossthemaccordingtotheirgeographicaldistanceandtheirpositionintheagents’networkofcooperation.Wewillunderlinetowhatextentthisbasicmodelwillbeabletoreproducetheurbansystemhierarchy.Inasecondstepwewilladdasupplementaryeconomicdimensionthatconsistsindifferenteconomicsectors.Theneweconomicspacecombinedwiththegeographicalonewillmodulatetheinteractionpatterns.Inthisconfigurationofthemodel,wewillidentifythenecessaryconditionstoreproducebothhierarchyandspecializationofurbansystems.2. DenisePumainFromtheorytomodeling:whicheconomicsforevolutionarygeography?

Weobservealackofcommunicationbetweenurbantheoriesthatarebuiltwithaneconomicorgeographicalfocus,whichmayhamperthecumulativityofurbanknowledgeandbedetrimentaltothequalityofurbanpredictionsandprescriptionsforurbanpolicies.Wereviewanddiscussherehoweconomicprincipleswereintegratedintogeographicalmodelssimulatingthespatialandeconomicdevelopmentofsystemsofcitiesinaterritory.Whenmodelingthedynamicsofsystemsofcitiesfromageographicalperspectiveweareconfrontedtoaparadoxicalsituation:wehavetoacknowledgethekeyroleofeconomicdevelopmentasbeingcausal(albeititisacircularcausality)intheprogressofurbanization,aswellasbeingresponsibleofthemajorstructuralfeatureofsystemsofcities(differentiationbetweencitiesintermsofsizeandfunctions)anddeeplyinfluencingthehistoricaltrajectoriesofindividualcities.Ontheotherhand,economictheoryitselfprovideslittlecluesforintroducingprinciplesindynamicmodelsofurbansystemswhichwouldenablereasonablefittingtoobservations.Maindifficultiesarerelatedtotheconceptofagglomerationeconomiesthataredefinedforanisolatedcity,theconceptionofequilibriumversusopendynamicsandthesearchforuniversalmodels.Thehybridationoftheoriesisobviouslyanecessaryoption.WeshallreviewwhichdecisionsweretakenregardingtheintroductionofeconomicconceptsandprinciplesintheseriesoftheSimpopsimulationmodels,between1996and2016,thatwereconceivedforreconstructingthetrajectoriesofcitiesasobservedinsystemsofcitiesforadiversityofcountriesandtimeperiods.3. AntoninBergeaud;SimonRayAdjustmentcostsandfactordemand:newevidencefromfirms'realestate

Adjustmentcostsimpairtheoptimalallocationofproductionfactoracrossfirms.Inthispaper,weusethecostassociatedtocorporaterelocationtoexploretheeffectoftheadjustmentcostsofthepremisessizeonfactordemand.Werelyonthetaxonrealisedcapitalgainsonrealestateassetthatentailsvaryingrealestateadjustmentcostsacrossfirmstoempiricallystudytheeffectofthesefrictionsonfirms'behaviour.Wedevelopageneralequilibriummodelwithheterogeneousfirmsthatshedslightontheimplicationofthelevelofthefixedcostsassociatedwiththeadjustmentofrealestateonthechangeinfirms'labordemandfollowingproductivityshocks.Thismodelpredictsthatemploymentgrowthoffirmsfacingpositiveproductivityshocksshrinkswiththelevelofthefrictions.ConfrontingtheseresultsusingFrenchfirm-leveldataovertheperiod1994-2013,wefindthathigheradjustmentcostsconstrainrelocationandreducejobcreationofthemostdynamicfirms.Thehighlightedfrictionshasnoticeablemacroeconomiceffects.

4. ClementineCottineau;ElsaArcaute;MaxNathanGeoindustrialclusteringofLondonbusinesses:modellingfirms'trajectoriesandtheirinteractionwiththeurbanfabric

TheindustriallandscapeandbuiltenvironmentofLondonhaschangedconsiderablyinthepastfewdecades,forinstancewiththeemergenceofacreativeanddigitalclusteraroundOldStreetandEastLondon(referredtoinpolicydocumentsas'TechCity'orthe'Siliconroundabout').However,theinteractionandcompetitionbetweenamovingsectorofstartupswithflexibleofficeneedsandlatentgentrificationprocessesinthehousingmarketmakesthespatialanchorofbusinessesuncertainandmobile.Toevaluatethepotentialdirectionsofthis(andother)cluster’sexpansion,weputinplaceanewwayofdefininggeoindustrialclusters,basedonajointmeasureofspatialproximityandofindustrialproximity.Clustersofsmallareasaredelineatedfordifferentintervalsofapercolationanalysisderivedfromfirmleveldata.Weanalysetheevolutionoftheseclustersandtrajectoriesofindividualestablishmentsbetweenthemoverthelastfifteenyears.Thisanalysisprovidesabasisforundertandingandmodellingtheirdynamicsintermsofsinglefirmsgrowth,co-locationstrategiesandinteractionwiththebuiltenvironmentintoamultiplexnetworksframework.

3B-SpatialAnalysis:Clustering

1. RichardHarris;DewiOwen;RoryKramerModellingProcessesofUrbanEthnicDesegregationusingMultilevelIndicesofSegregation

Withintheliteratureonmeasuringethnic(de-)segregation,multilevelanalysishasbeenpromotedasatooltoevaluatechangesinboththenumericandgeographicscalesofsegregationbetweentimeperiodsforpopulationgroupswithinastudyregion(see,interalia,Leckie2012,LeckieandGoldstein2015,Jones2015,Harris2017).Thispaperwillusethemultilevelapproachtoshowhowmultilevelindicesofsegregationcanbeusedtomodelprocessesofurbanethnic(de-)segregation,takingahypotheticalexampleasdemonstrativeandthenapplyingtheapproachtoconsiderintercensalchangeinthetop100largestMetropolitanStatisticalAreasintheUnitedStates.Whereastraditionalindices,suchastheindexofdissimilarity(ID),“haveincommontheassumptionthatsegregationcanbemeasuredwithoutregardtothespatialpatternsof[populationgroups]inacity”(DuncanandDuncan1955p.215),thesameisnottrueofitsmultilevelequivalent,themultilevelindexofdissimilarity(MLID)(Harris,2017).Instead,theMLIDlooksatthetwoprincipaldimensionsofsegregationintandem,whichareunevenness(ameasureofspatialheterogeneitycapturedbythestandardID)andspatialclustering(ameasureofthescaleofsegregation,whichisnot).Bylookingatbothtogether,changesinthespatialpatternofsegregationarereflectedinchangestothemultilevelmeasuresofvarianceevenwhentheoverallIDscoreisunchanged.Consideredintermsoftheclassicanalogyofacheckerorchessboard,ifsomeoftheblackandwhitesquaresare‘flipped’incolourtherebychangingthescaleoftheblack-whitepatterningthenthemultilevelindex,unlikethetraditionalID,willdetectthechange.Inaddition,themultilevelapproachmeasuresscaleeffectsnetofotherscalesinthemodel.Hereitdiffersfromothermulti-scaleapproaches,notably‘egocentric’methods(Leeetal.2008,Reardonetal.2008,2009,Spielman&Logan2013,Hongweietal.2014),whichmeasureaggregationeffects–athowthemeasureofsegregationchangeswithaggregation–butdonotseparateouthowmuchofthemeasuredsegregationisduetoeachspecificlevelofaggregationalone.ThepaperwillreviewthemultilevelapproachanditsimplementationasaMultilevelIndexofDissimilaritywithintheRpackage,MLID.Havedonesothecasestudywillbepresented,lookingatpatternsofethnicsegregationintheUnitedStatesin1990,2000and2010andtheirchangingscales.Finally,someoftheshort-comingsoftheapproachwillbeconsidered,aswellasitsextensionintoamultigroupindex(amultilevelversionoftheentropymeasure,Theil’sH).

2. RogerBivand;DavidWongComparingimplementationsofglobalandlocalindicatorsofspatialassociation

Measuresofspatialassociation,andespeciallyofspatialautocorrelationhavebeenmadeavailableinmanysoftwareapplications.Measuresmaybeglobal,applyingtothewholedatasetunderconsideration,orlocal,applyingtoeachobservationinthedataset.Methodsofstatisticalinferencemayalsobeprovided,butthesewill,likethemeasuresthemselves,dependonthesupportoftheobservations,chosenassumptions,andthewayinwhichspatialassociationisrepresented;spatialweightsareoftenusedasarepresentationaltechnique.Inaddition,assumptionsmaybemadeabouttheunderlyingmeanmodel,andabouterrordistributions.Differentsoftwareimplementationsmaychoosetoexposethesechoicestotheanalyst,butthesetsofchoicesavailablemayvarybetweentheseimplementations,asmaydefaultsettings.ThiscomparisonwillconsidertheimplementationsofglobalandlocalMoran'sI,GandGeary'sC,andothermeasuresavailableinarangeofsoftwareincludingGeoDa,ArcGIS,R,Crimestat,andothers.3. EmilySheard;NickMalleson;MarkBirkinExploringtheSpatio-TemporalDistributionofCarKeyBurglaryinWestYorkshire:RoutineActivityorPlannedBehaviour?

Muchhasbeenwrittenaboutrepeatvictimisation,particularlyinrelationtoresidentialburglary,andtheassociatedtheoryunderpinsanumberofcrimemodels.Itis,however,importanttonotethedistinctionbetweendifferentareatypesandthenatureofrepeatoffendingpatternsthereinasthisislikelytohaveimplicationsforcrimeprevention.JohnsonandBowers(2004,p.252),forexample,suggestthatmoreaffluentareastendtoexperiencemorenear-repeatdomesticburglaries,wherebyoffencesclusterinbothspaceandtime,butlessaffluentareasaregenerallymoresusceptibletoexactrepeats.Giventheliteratureonhigh/lowoffenderrateareasandthejourneytocrime,itisanticipatedthattemporalclusteringofnear-repeatswillbemostmarkedinmoreaffluentareasi.e.relatedoffenceswillfollowinitialeventsinquicksuccession.Recognisingthattransienthotspotscanlimittheopportunityforreactiveintervention,thisresearchwillseektodevelopadynamicriskmodelforthepredictionoftemporallyclusteredcrimeseries.Carkeyburglarywillbeusedtoinformthedevelopmentofthemodelbecausethismodusoperandiistypicallyexpectedtopresentinmoreaffluentareas(desirablevehicles)andalsopointstoamore‘professional’offenderprofilethan‘conventional’burglary.Initialanalysiswillbeperformedonpolicerecordedcrimedata,bothtotestthevalidityoftheresearchhypothesis,namelythatthespatio-temporalsignatureofcarkeyburglarydifferstothatofconventionalburglary,andtoidentifypotentialriskfactorsforinclusioninthemodel.Theanalysiswilltaketheformofthreekeystages;thefirstwillconsiderthespatialdistributionofoffencesinWestYorkshireandanyrelationshipsbetweenidentifiedheterogeneityandin-area/neighbouringareacharacteristics,thesecondwillexaminethetemporaldistributionofoffences,andthethirdwilllookforevidenceofspatio-temporalclustering.Resultswillbepresentedattheconferenceandreviewedinthecontextoftheenvironmentalcriminologyliteratureandconventionalburglary.Acknowledgement:ThisworkwassupportedbytheEconomicandSocialResearchCouncil[grantnumbersES/J500215/1,ES/L011891/1].References:Johnson,S.D.andBowers,K.J.2004.TheBurglaryasCluetotheFuture:TheBeginningsofProspectiveHot-Spotting.EuropeanJournalofCriminology.1(2),pp.237-255.4. PeterMandl

GeographicalConceptsandReflectionsaboutAspectsofthe“DigitalAge”

Itissaidthatweliveatthebeginningofthe“DigitalAge”Thisshouldinfluenceallareasoflife,especiallyalsogeographyasascience.Incontrastgeography,itsconcepts,theoriesandwayofthinkingalsoimpactsdiverseaspectsoftheDigitalAge.Suchinterdependencyarepresentedanddiscussedinthispaper.Firstthereisthespatio-temporalaspectinadigitalworldwhichisanalysedandstudiedingeographysincemanyyears.StartingfromtheconceptsbyThorstenHaegertstrand’s“TimeGeography”,includingthe“ModelsofSpatialProcesses”byArthurGetisandBarryBootsandthereflectionsbyDonnaPeuquetaboutthe„RepresentationsofSpaceandTime“manyideasweredescribedinthegeographiesofthe1970iestothe1990ies.Theseconceptscanbethebasisforspatio-temporalanalyticsandpredictionsinamoderndata-basedviewoftheworld.Thetalkshowswhatwecanlearnanddeducefromthesereflections.Thesecondaspectarethespatialbigdata,whicharethebaseformanyempiricalstudiesundertakeningeographysincedecades.Startingwithquantitativeaswellasstructureddatamanyyearsagonowadaysalsoqualitativeaswellasunstructureddata,liketexts,images,videosetc.areavailableandused.Theschemesandmodelsofopen,linkedandusergeneratedspatialdata,whichhavebeendevelopedingeographyorforOGCspecifications,areveryimportantsourcesforthestructures,linksandprocessingmodelsforspatio-temporaldataintheDigitalAge.ReflectionsfromtheGI-Science,likethe„LanguageforSpatialComputing“byWernerKuhnor„TheLanguageofSpatialAnalysis“byESRIarethebasisforthethirdaspect,thefunctions,whichusethebigdatainconceptstocontributetoproblemsolvingintheDigitalAge.ThesethreeaspectsoftheDigitalAge,whicharespatio-temporal-concepts,spatialbigdataandfunctions,areconsolidatedinsomefinalbestpracticeapplicationexamplespresented,discussedandcriticisedinthistalk.ItwillbeclearthattheDigitalWorldisubiquitoustoday.Itisveryusefulasamodernpragmaticinformationtoolifitisappliedcorrectlyandcritically.Inthistalkitisshownandsuggestedwhat“geographicalthinking”cancontributetothepracticeoftheDigitalAge.

5. MatthewDawsSelf-excitedpointprocesspatternsincrimedata

Existingmethodsofpredictingcrime(forexample,burglaryevents)commonlymakeuseofthe(near)repeatvictimizationhypothesis:thetendencyofvictimsofcrimeto,inthenearbyfuture,toberepeatvictims,andofnearby(say)buildingstoalsobefuturevictims.Thatis,acrimeeventataspatial/temporallocationtendstoimplyahigherrisk,localisedinspaceandtime,fornearbylocations.Inthelastfewyears,anexplicitstatisticalmodel,theSelfExcitedPointProcess,hasbeenstudiedinrelationtocrimeprediction.ThiswaspreviouslyappliedtoEarthquakemodelling,whereoneattemptstopartitionearthquakesinto"background"events(whichoccuratrandom,butinaninhomogeneousway)and"aftershock"events,whichareearthquakescausedbypreviousEarthquakes.Thereareclearparallelswiththenearrepeatrepeatvictimizationhypothesis,althoughitisfarfromcleariftheexactparametricformofthemodelwhichisappropriatetoearthquakesshouldbeusedtomodelcrime.

Wewilldiscusstheuseofnon-parametricmodelsintheexistingliterature,andsomeofthedifficultieswehaveencounteredinreplicatingtheseexistingstudies.Wewillthendiscusssomenewsemi-parametricmodels,andsomeofthetechniquesweusedtodealwithreal-worlddata.

4A-Geographyandeconomics2

1. JusteRaimbault

InvisibleBridges?ScientificlandscapesaroundsimilarobjectsstudiedfromEconomicsandGeographyperspectives

UnderstandingScienceinaperspectivistapproach(Giere(2010)),itisnaturalandnecessarythatdisciplinesorfieldsproposeverydifferentperspectivesonrealworldobjects.Theyet-to-exploreborderregionsattheinterface,inwhichinterdisciplinaritydrawsmostofitsstrengths,arehowevernotwellunderstoodintermsofprocessesofknowledgeproductionsuchasdomainscross-fertilisation,butalsonotnecessarilytheobjectofconsensusesforresearchpolicies.WeproposetoexploretheseissuesontheparticularcaseofEconomicsandGeography,betweenwhichbridgesseemdifficulttobuildinthecurrentstateofdisciplines.WetakeaQuantitativeEpistemologyapproach,morepreciselybycombiningcitationnetworkanalysiswithtext-miningandsemanticnetworkanalysis,usingmethodsandtoolsdevelopedinRaimbault(2016)toreconstructwhatcanbeseenasascientificlandscape.Wechoosetoworkontwocasestudiesofobjectsthathavebeenextensivelystudiedfrombothperspectives:RelationsbetweenNetworkandTerritories,andUrbanGrowth.Weconstituteforeachaninitialcorpusofkeyreferencesinbothdisciplines,fromwhichthebackwardcitationnetworkatdepthtwoisreconstructed.Wethencollectabstractsforasignificantproportionofnodes,extractrelevantkeywords,andcouplethecitationnetworkwithasemanticnetworkwhichcommunitiesforexampledefineendogenousresearchdomains.Resultsoverallshowthatasexpecteddisciplinesarehighlyclusteredintermsofcitationpractice,butstillformafullyconnectedgraph.Thecommunitiescorrespondtoclearlydistinctcontentsintermofsemanticlandscape,withagoodagreementbetweencommunitystructuresinbothnetworks.InthecaseofNetworkandTerritories,intermediateorsub-disciplinesemergeascommunitiesinthecitationnetwork,asspecializationsonAccessibilityorTransit-Oriented-DevelopmentthatpositionattheinterfaceofPlanning,GeographyandEconomicGeography.Similarly,unexpected“newcomers”suchasphysicistsappearatequaldistanceofGeographyandEconomicsandmaybeaninterestingdirectiontosearchforbridges.OnthesubjectofUrbanGrowth,thedistinctionsareevenstronger,withafinalrangeofsub-disciplinesspanningfromtechnicalGISstudiestoentrepreneurshipand“creativeclusters”studies.Thisillustrateshoweachdisciplinehasconstructeditsownobjectsandepistemologies,andhowdifficultitcanbetoactuallyworkonacommonobject.Itisinterestingthentoformulateassumptionsontheroleofauxiliaryandneighbordisciplinesinshapingresearchsubjectsofagivendiscipline,andinparticularofperformativebranchesthathaverapidpracticalapplication(e.g.insocio-economicpracticesandpoliciesforEconomics,inPlanningandUrbanismforGeography)thatseemtosimultaneouslydrivethemapartbutalsobringnewpotentialcommonobjects(suchasthe“smartcity”).Wepostulatethatbridgingapproachesarecloserthantheyseembutthattheywillrequireahighlevelofreflexivityandawarenessofscientificlandscapeasthisexampleillustrates.ReferencesGiere,R.N.(2010).Scientificperspectivism.UniversityofChicagoPress.Raimbault,J.(2016).Indirectbibliometricsbycomplexnetworkanalysis.In20eAnniversairedeCybergeo.

2. OlivierFinanceTransnationalinvestmentdecisionsinandtowardsEurope:evidencesforasingleEuropeansystemofcities?

Systemsofcitiesexhibitseveralengagingpropertieslikea“hierarchicalandspatialstructureas[…]multipleinterdependenciesbetweencitiesbelongingtothesamecountry”[Bretagnolleetal.,1997]–orbyextensionthesameregion.InthecaseofEurope,evidencesofthefirstpropertieshavealreadybeenproducede.g.throughZipf’slaw[Bretagnolleetal.,1999].EvidencesofthefunctionalunityofaEuropeansystemofcities[e.g.Hall&Hay,1980,Cattanetal.,1999]couldbecompletedinacontextofglobalizationlinkedwithgrowingtransnationaleconomicinterdependencies.Goingfurtherthannationalboundariesisessentialas“thepositionofmajorcitiesintheirnationalstateshavebeenredefinedowingtothe

multiplicationofinternationalcommunications”[PumainD.,Saint-JulienT.,1993].TheEuropeaneconomicpolicies,theprocessesofmetropolisation,therestructuringofEuropeancompanies,all“adduptoaprospectiverestructuringoftheEuropeanurbansystem,fromasetofdistinctnationalsystemsintosomethinglikeasingleintegratedurbansystem”[Cheshire&Gordon,1995].ButhowfargoesthefunctionalintegrationinthecaseoftheEuropeanurbansystem?Inthisresearch,weusestatisticalmodelingtoexploreifoneoftheseinterdependenciesscaleswithcitysizeinthewholeEurope.Scalinglaws,asone-parameterpower-lawrelationships,areincreasinglyusedingeographytodescribequantitiesdistributedinsystemsofcities[Westetal.,1997/Pumainetal.,2006/Pumain,2007/Bettencourtetal.,2009/Pumainetal.,2009/Batty,2013/Arcauteetal.,2014/Bettencourtetal.,2014/Cottineauetal.,2016].WewillinparticularobservetransnationalinvestmentdecisionsbetweenEuropeancitiesandtowardsEuropeancities,registeredbytheFinancialTimesfrom2003to2016intothefDiMarketsdatabase.Ifthesescalinglawsareusefulandefficienttocapturethesedecisionstrends,weshallbeabletoconcludethatEuropeancitiesexhibitaconsistencyasasinglesystem,furtherthanastatisticaladditionofnationalsystems[Bettencourt&Lobo,2016].Theselocationaldecisionsrevealboththehigherattractivenessofsomecitiesandthetransnationalfirms’locationstrategiesinacontextofeconomicglobalization[Heldetal.,2000/Taylor,2003/Arrault,2007/Scott&Storper,2014].Consideringtheseinterrelations,territoriesarediverselyintegratedandexcludedatvariousscales.WewillespeciallyexplorethemacroandmesolevelsoftheEuropeansystemofcities,muchmoreappropriatethaninternationalcomparisons,as“capitalisglobal[and]work[…]local”[Beck,1999]andascitiesarethemajornodesofeconomicactivitiesandglobalizednetworks[Sassen,1991/Beaverstocketal.,1999/Alderson&Beckfield,2004/Derudder,2006].Wecanexpectastrongconcentrationofthesedecisionsintotheupper-partoftheurbanhierarchy,asitisthecaseintheFrenchsystemofcities[Finance,2016].Wecanalsoexpectsomestrongnationalandregionaleffectsthatmayhavetoberegardedpreciselytoconsiderthemintothestatisticalmodel.

3. JustinDelloye;RemiLemoy;GeoffreyCarusoHomotheticScalingofUrbanLandUseandPopulationDensityProfilesinMonocentricModels

Urbanscalinglawswithrespecttototalpopulationhavebecomeamajorfieldofresearchinurbanstudies.However,thescalingpropertiesofthepopulationdensityprofilesresultingfromthemonocentricmodelsofurbaneconomicsarestillunclear.Thispaperteststheabilityofurbaneconomicmonocentricmodelstoprovideatheoreticalfoundationofthethree-andtwo-dimensionalhomotheticscalingofthelanduseandpopulationdensityprofilesthatwereempiricallyidentifiedbyLemoyandCaruso(2017)*forthe300largestEuropeancities.First,twofunctionalformsoftheAlonsomodelwithlog-linearutilityareproposed,respectivelycalledAlonsoandAlonso-LUmodels,withconstantandexponentiallydecreasingprofilesofhousingland-use.Bothland-useprofilesarethencalibratedusingtheempiricalhousinglanduseprofileofLemoyandCaruso(2017).Second,thepopulationdensityprofilesoftheAlonsoandAlonso-LUmodelsarecalibratedaswell,usingtheempiricalpopulationdensityprofile.ResultsshowthattheAlonsomodelisconsistentwithathree-dimensionalhomotheticscalingofthepopulationdensityprofile,andthatitconjecturesascalingpowerof1/3thatmatchesempiricalevidence.RegardingtheAlonso-LUmodel,itisnotonlyconsistentwithathree-dimensionalhomotheticscalingofpopulationdensitywithpower1/3,butitalsoprovidesamuchmoreaccuratedescriptionofthehousinglanduseprofilewhichscaleshorizontallywithpower1/2.However,thesystem-of-citiesinterpretationintermsofagglomerationeconomiessuggests,forbothmodels,thateitherhouseholdsdevotemorethan50%oftheirincome(netoftransportcost)tohousing,orthatthereexistsanegativerelationshipbetweenpopulationsizeandtransportcost.Thisresultstrivesagainsttheclassicinterpretationoftransportcostasanagglomerationcost.Consequently,althoughthe

Alonso-LUmodelprovidesasatisfactorydescriptionofthehomotheticscalingoflanduseandpopulationdensityprofilesforEuropeancities,itstillpresentssometheoreticaldrawbacks.*Lemoy,RémiandGeoffreyCaruso(2017)"Scalingevidenceofthehomotheticnatureofcities",arXiv:1704.06508.Thispaperwillalsobesubmittedtothe2017EuropeanColloqiumofTheoreticalandQuantitativeGeography(ECTQG).

4. RogerWhite;GustavoRecio;WolfgangBanzhafTheNecessityofDisequilibrium

Classicaltheoryinurbanandeconomicgeographywasdevelopedonthebasisoftheideathatsystemstendtowardequilibrium,andthattheyarethuspredictable,sincetheequilibriumstateisinsomesensestable.Thisideawasborrowedfromeconomics,anditcontinuestoformthebedrockofthatdiscipline.Muchrecentworkintheoreticalgeography,however,involvesdynamicmodellingusingsuchtechniquesascellularautomataandagentbasedmodelling.Theseareessentiallynon-equilibriumapproachesandarethusabletoaddressphenomenathatdonotfiteasilywithintheframeworkofformaleconomics—phenomenasuchasinnovationandtheevolutionofformandstructure.Buteconomicfactorsarefundamentaltotheevolutionandfunctioningofcitiesandregions,andsoitisincreasinglyclearthatweneedtotranscendthelimitationsofcurrentequilibriumbasedeconomictheory.Tothatend,wedevelopanagentbasedmodelofaneconomyconsistingofproducers(firms)andconsumers.Firmsarecharacterisedbyalocation,atechnology,andpossessions—specificallymoney,capital,andastockofinputsandoutputsthathavebeenproducedbutnotyetsold.Ateachiteration,firmsmakeaproductionplanwhichwillrequirethemtopurchaseinputs,hirelabour,andusetheirtechnologytoproduceaproductforthemarket.Ingeneralthereareanumberoffirmsusingeachtechnology,witheachtechnologydefiningasector.Someproductsareintermediategoods,usedtoproduceotherproducts,andothersarefinalconsumptiongoods.Consumersusetheirlabourearningstobuyconsumergoods.Whenprofitsinaparticularsectorarehigh,newfirmsappeartoproducethatproduct,whichtendstolowertheprice.Ifasectorbecomesunprofitable,someproducersfailanddisappearfromthesystem.Therearethuscontinualendogenousfluctuationsinthesystem.Fromtimetotimenewtechnologiesappear.Someoftheseproduceanexistingproductusingdifferentinputs,usuallyinresponsetoahighpriceoftheproductduetoashortageoftheinputsrequiredbytheexistingtechnology.Othernewtechnologiesproduceaproductthatisnewtothesystem.Theappearanceofanewtechnologyfrequentlyinducesabriefperiodofgreatinstabilityinthesystemasmeasuredbyprofitsorprices,accompaniedbyalesserfluctuationinthenumberoffirmsproducinginasector.Whenallfirmsusingatechnologyfail,thattechnologydisappearsfromthesystem;inasimilarway,productsoccasionallydisappear.Ingeneralthetendencyistowardincreasingcomplexity,aswellasincreasingefficiencyaslessproductivetechnologiesarereplacedbymoreeffectiveones.Thesefluctuationsandtrendsrepresentthestructuralevolutionofthesystem.Themodelfocussesonthedynamicsandevolutionofthesystem—orinotherwordsonmarketinstabilityandinnovation—fundamentalphenomenathatcannotbetreatedinanysystematicwaywithintheframeworkofneo-classicaleconomictheory.Becausethefirmsrequirelandandthushavelocations,themodelisessentiallyspatialandcanthusaddressgeographicissuesrelatedtoeconomicchange.

4B-SpatialAnalysis:Visualisation

1. RobinCura;CecileTannier

Visualandsemi-automatedexplorationofanarchaeologicalmodel:calibratingamodelofspatialreorganizationinNorth-WesternEurope,A.D.800to1100.

Mostoften,whenbuildingamodel,themodellerhastochoosebetweentwotypesofmethodsforevaluatingitsmodel.(1)The“facevalidation”,basedonavisualandqualitativeassessmentofthemodeloutputs,allowsforaquickanddirectfeedbackofthemodelbehaviourconsideringthedifferentvaluesofparametersused.(2)Morerecently,andtohaveamorepervasivemethodofexploration,manymodellersleantonHigh-PerfomanceComputingtodevelopautomatedwaysofexploringmodelsoftenbasedonfullfactorialdesignsoronmoreparsimoniousmethodslikegeneticsalgorithms.Inthispresentation,weadvocateforcombiningtheefficiencyoftheautomatedexplorationsandthequalitativefeedbackoffacevalidation,inparticularinacontextofstronginterdisciplinarityinvolvinggeographers,archaeologistsandhistorians.OurcasestudyisamodelofsettlementsystemevolutionoverthelongtimethataimsatabetterunderstandingofthesocialandspatialprocessesthathappenedinNorth-WesternEuropebetween800and1100A.D.Thisperiodischaracterizedbyamajorchangeinthesettlementsystem,towhichwereferasthe“800-1100transition”(Tannier&al.,2014).Thepeasanthouseholds,mostlysprawledaround800,tendtoclusteraroundfunctionalattractors,mainlycastlesandchurches.Theclusteringandfixationofhouseholdsareattheoriginofvillagesandsmalltowns.WiththeGregorianReform,whichledtoastrongerreligiouscontrol,agridofparishesemergedaroundthechurches.Wehaveintroducedtheseprocessesinanagent-basedmodelbuiltwiththeGAMAplatform,withtheaimtotesthypothesesontriggersandmechanismsthatproducedtheobservedchanges.Forexample,itshouldhelpunderstandingifpopulationgrowthisessentialtotheobservedclustering.Duetothelackanduncertaintyofempiricaldata,themodelmostlyreliesonexpertqualitativeknowledge.Consequently,wecannotdefinefitnessfunctionsorsimpleoutputindicatorsthatwouldallowafullyautomatedevaluationofthemodel.Moreover,themodelcontainsalargenumberofparameters.Inordertofindafirstsetofparameterssothatthemodelreproducesasituationthatexpertswouldvalidate,weusedtwocomplementaryapproaches.First,wecarriedoutasemi-automatedexplorationbasedonroughparametersvalues,inordertorestraintheparameters’spacetoanalyse.Tobeparsimonious,eveninthecomputationpowerrequired,welimitthisexplorationtothemostobviousparametersaffectingthesimulationresults.Wethenusedavisualapproach,basedonmultipledisplaytypestoallowthespecialiststounderstandandassesstheoutputofanexperiment.Forthis,weusedGeoVisualAnalyticsmethodsintroducedinacustomplatformdedicatedtotheanalysisoflong-timespatio-temporalsimulationdata.Thiscommunicationaimsatpresentingthewaythoseseeminglyopposedparametrizationmethodshavebeenarticulated,andthesimulationresultsthusobtained.References:TannierC.,Zadora-RioE.,LeturcqS.,RodierX.,LoransE.(2014).«Uneontologiepourdécrirelestransformationsdusystèmedepeuplementeuropéenentre800et1100»,OntologiesetmodélisationparSMAenSHS,DenisPhan.

2. PierreFrankhauser;OlivierBoninErrortermsanduncertaintyinfractalradialanalysis

Infractalanalysisofbuild-upareasincities,determiningthelocalfractaldimensionisarathercommonmethod,oftencalledradialanalysis.Toproceed,acircleisdrawnaroundachosenbuild-upsite,andtheradiusofthecircleisgraduallyincreased.Ateachstep,thetotalnumberofoccupiedsitesinsidethecircleiscounted.Thisinformationallowsestimatingalocalfractaldimension.Investigationshaveshownthatthistypeofanalysisenablesgettinginformationaboutrupturesinthespatialorganizationofbuild-upspaceonthescaleoftownsections(Thomasetal.2010).Thistypeofanalysiscanbereplicatedforeachbuild-upsiteofthearea.Thenamapcanbedrawn,whereforeachbuild-upsiteitsfractaldimensionisrepresentedbyacolourcodecorrespondingtoitsvalue.Wecallthismultiradialanalysis(Frankhauser2015).However,thefractaldimensioncanbetrickytoestimateempirically.Indeed,errortermsarehighlycorrelatedbyconstructionbecauseanyvoidthatistakenintoaccountindiskwithradiusrwillbetakenintoaccountforallradiuseslargerthanr.Thiscorrelationcanbiastheestimationofthefractaldimensionifnotcaredfor.Moreover,uncertaintiesandempiricaldeviationstothefractallawcanhavepower-lawdistributions,iftheyarelinkedtothefractalgenerator,ornormaldistributionsiftheyobeysomekindofcentrallimittheorem.Thus,itsisunclearifthefractaldimensionisbestestimatedwithlog-logregression,ordirectlywithpowerregression.Inthispaper,weproposeinsightsonthesequestionsbytestingonconstructedfractalsaswellasondataonrealcitiestheeffectsofdifferentkindsoferrorsanddeviationstothefractallaw.Weshowthatgiventheadditiveormultiplicativenatureoferrorssomespecificcaremustbetakenduringtheestimation,especiallytomodeltheautocorrelationoferrortermsinherenttotheradialapproach.Wetrytoseparatetheeffectsofgridresolutionandorientationfromothersourcesoferrors,andtounderstandwhydirectestimationisnumericallyimpossibleinmanycases.Weproposeaprotocoltoanalysethenatureoftheerrorterminthefractalestimation,andcomparedifferentwaystoestimatethefractaldimensionswithexplicitmodellingoferrorautocorrelation.Thebetterunderstandingoferrortermsinfractalestimationenablesustoapplymoresafelythemethodtorealurbanfabrics,andtodiscusstheresults.WeapplythemethodologytotheFrenchnewtownofCergy-Pontoisethatmixeshistoricalcities,smallvillagesaswellaslargeurbaninterventionstypicaloftheurbanismofthe70sandthe80s.Frankhauser,P.(2015).Fromfractalurbanpatternanalysistofractalurbanplanningconcepts.Computationalapproachesforurbanenvironments,geotechnologiesandtheenvironment,MarcoHelbich,JamalJokarArsanjaniandMichaelLeitnereds.,Springer,pp.13-48.Thomas,I.,Frankhauser,P.,Frenay,B.,Verleysen,M.(2010).Clusteringpatternsofurbanbuilt-upareaswithcurvesoffractalscalingbehaviour.Environment&PlanningB(Planning&design),37(5),pp.942-954.3. AlessandroAraldi;GiovanniFuscoRetailActivityandtheCity:ContributionfromtheAnalysisofUrbanFabrics

Inthelastdecades,theso-calledthirdrevolutionincommerceisimpactingandmodifyingretaildistributionincities(DesseandLestrade2016).Whilethefirsttransformationproducedacentralizationofretailindepartmentstoresincitycentres,andthesecondcreatedthenewconceptoftheshoppingmallinthesuburbs,thecurrentoneisintroducingthenewconceptofomnipresenceofsaleactivity.E-commerceandm-commercerepresentnewchallengesfortraditionalretailers.

Asaresultofthesesocietalandeconomicchanges,thegrowthofretailvacancyhasbeendetectedintheUKsincethelate‘80s;inthelast20years,otherwesterncountriesfacedthesametransformations.Vacancyratiosarenotuniforminurbanspace:withinthesameurbanregion,vacancyratesevolvedifferently;macroeconomictendenciesarenotsufficienttounderstandwhysomeneighbourhoodsaremoreaffectedthanothersbythisprocess.Thepresenceofretailersinastreetorinaneighbourhoodisaparamountaspectforthesociallifeofacity(Jacobs1961);awareoftheevolvingsituation,localadministrationsimplementpoliciesofretailrevitalizationandurbanrequalification.Thesepolicieswouldbenefitfromabetterunderstandingoftheinfluenceofurbanformonretailactivities.NetworkanalysishasbeenwidelyappliedinthisfieldofstudyindifferentcitieswithSpaceSyntaxorMultipleCentralityAssessment.Nevertheless,otheraspectsofurbanformcouldbeconsideredasrelevantforretaildistribution(Svetsuk2014).InadifferentpresentationatETCQGweproposeMultipleFabricAssessment(MFA),anewautomatedapproachintheanalysisofurbanformfromthepedestrianpointofview,consideringthedifferentcomponentsofurbanfabricsaswellastheirspatialrelations.Herewecross-analysetheresultsofMFAwithretailactivitypresenceinthecity.TraditionalstatisticalanalysisandBayesiannetworksexploretherelationbetweenobservableurbanformsandtheintensityanddiversityofretailactivity.ThecasestudyisthemetropolitanareaoftheFrenchRiviera(France).Historicaldataofretailpresence(2000-2016)isconsideredinordertorecognizedivergentpatternsinretailvacancyatamicro-scale.Theaimofthisstudyisalsotoidentifywhichsubsetofmorphologicalcharacteristicsbetterdescribestheurbanfeaturesfavouringorhinderingretailpresence.Ouranalysesshowthatdifferenturbanfabricsareecosystemsforretaildevelopmentmoreorlessapttofosteringthedifferentformsofretailoffer.Notsurprisingly,traditionalsmallandmediumretailersarebettersupportedbytraditionalurbanfabricscharacterisedbyconnectivestreetnetworksandhigh-density,adjoiningbuildings.Afutureresearchperspectivewillbethecombinationofconfigurationalandurbanfabricapproachestourbanforminordertobetterunderstandsmall-scalevariationsofretaildistribution.DesseS.andLestradeS(2016)Mutationdel’espacemarchand.PressesUniversityofRennes.JacobJ.(1961)Thedeathandlifeofgreatamericancities,RandomHouse,NewYorkSevtsukA.(2014)“LocationandAgglomeration:TheDistributionofRetailandFoodBusinessesinDenseUrbanEnvironments“.JournalofPlanningEducationandResearch34(4):37493.4. MartinCharlton;ChrisBrunsdonUsinganimationtovisualisecorrelationstructureinmultivariatespatialdata

Conventionallyweusedstaticdisplaystovisualiserelationshipsinmultivariatespatialdata.Suchdisplaycanincludescatterplots,boxplots,lineplots,andmaps.Howeverthechallengeformultivariatedataisbeingabletoviewseveralrelationshipsatonce.Scatterplotmatricesbecomeclutteredwhenthenumberofvariablesexceedsarelativelysmallnumber,andwhileprincipalcomponenttransformsallowustoconcentrateonthemostimportantrelationships,anumberofissuesstillremain.Inparticular(i)aresuchrelationshipsstableovergeographicspace(ii)whatlevelofsamplinguncertaintymanifestitselfinthevisualisations.Weproposetoaddresstheseissuesthroughtheuseofanimatedvisualisations:inparticulartohighlighttheeffectsofsamplingvariabilityandspatialheterogeneity.ThisisachievedusingopensourcesoftwarebasedontheRstatisticalandgraphicsprogramminglanguage,togetherwithotheropensourcesoftwaretools.Inadditionthesetechniqueslendthemselvestoweb-basedresourcestoexplorethesevisualisationapproachesandalltechniquesarefullyreproducible.AnumberofexampleswillbegiveninthepresentationusingdynamicvisualisationappliedtoIrishsocio-economicdata

5. MonsuruAdepejuDevelopingahybridhotspotapproachforimprovingtheaccuracyofcrimeprediction

Theabilityofdifferentcrimehotspotmethodstocomplementeachotherhasbeendiscussedinpreviousstudies.Thispaperextendstheideaofcomplementarityofthehotspottodevelopahybridapproachinwhichoutputsofmultiplemethodsarecombinedinordertoimprovethepredictiveaccuracy.AcasestudyofChicagocrimeispresentedtodemonstratethataconsiderablelevelofaccuracycanbegainedbyusingthehybridapproach.

5A-Geographyandeconomics3

1. EricKoomen;DiogoVascoBridginggeographyandeconomicsinlocal-scaleland-usemodelling

Modelsthatsimulateland-usepatternsoftenuseeithergeographic,inductive,data-drivenapproachesordeductive,economictheorybasedmethodstodescribetherelativestrengthofthesocial,economicandbiophysicalforcesthatsteerthespatialpatternsinthevarioussectorsinthelandsystem.Thispresentationproposesanintegratedframeworkthatincorporatesbothapproachesbasedonaunifiedassessmentforlocallandsuitabilityfollowingamonetary,utilitybasedlogic.Theframeworkisillustratedwithahedonicpricinganalysisofurbanlandvaluesandanetpresentvalueassessmentforagriculturalproductionsystemincombinationwithstatistics-basedassessmentsoflandsuitabilityforothersectors.Theresultsshowthatlimiteddifferenceexistsbetweenthemostcommonlyappliedinductiveapproachesthatuseeithermultinomialorbinomiallogisticregressionspecificationsofsuitability.Land-usesimulationsfollowingthebinomialregressionbasedsuitabilityvaluesthatwererescaledtobidprices(reflectingrelativecompetitiveness)performbetterforallindividualland-usetypes.Performanceimprovesevenfurtherwhenalandvaluebaseddescriptionofurbanbidpricesisaddedtothisapproach.Interestinglyenoughthebetterfittingdescriptionofsuitabilityforurbanareasalsoimprovestheabilityofthemodeltosimulatecorrectlocationsforbusinessestatesandgreenhouses.Thesimulationalternativesthatconsiderthenetpresentvaluesforagriculturaltypesoflanduseshowtherelevanceofthisapproachforunderstandingthespatialdistributionofthesetypesoflanduse.Thecombineduseofurbanlandvaluesandnetpresentvaluesforagriculturallanduseindefininglandsuitabilityperformsbestinourvalidationexercise.Theproposedmethodologycanalsobeusedtoincorporateinformationfromotherresearchframeworksthatdescribetheutilityoflandfordifferenttypesofuse.Initialapplicationsofthisnovelmethodologyinoperationalland-usemodelingframeworksindicatetheirvalueforspatialpolicypreparation.2. ZahratuShabrina;ElsaArcaute;RichardMilton;MichaelBattyModellingAccessibilityofAirbnbinGreaterLondonArea

ThegrowthofAirbnb,aPeertoPeer(P2P)platformforshorttermrentals,hasincreasedexponentiallysinceitsestablishmentin2008.ThedatafromInsideAirbnb,whichhascollectedpubliclyavailableinformationofAirbnblistings,showsthatinMarch2017thereweremorethan53,000listingsinLondonalone.TherehasbeenasignificantincreaseofAirbnblistingsinonlynineyearssinceitwasfirstintroducedinLondonwheremostoftheselistingsarelocatedinTowerHamlets,HackneyandWestminsterarea.ThispaperinvestigateshowoptimalthelocationsofAirbnbarebycomputingaccessibilitymeasuresusinggravity-spatialinteractionmodelsforGreaterLondon.TheaccessibilityofAirbnbdependsontherelativelocationofAirbnb(origins)comparedtothetouristattractions(destinations)assumingthatthemajorityofthepeoplewhostayinAirbnbcometovisitandtraveltothesetouristicdestinations.

WeapplytheaccessibilitymodeltopredictwherethenextAirbnbmightbelocatedusingseveralsteps.First,weaggregatethesurvivingAirbnbcountaccordingtoLSOA(LowerSuperOutputAreas)2011boundariesinLondon.TheAirbnbdatathatwehavearetherentaloutletsthatsurviveandarestillavailablein2017wherealltheoutletsfrom2008areconsidered.Forthepurposeoftheresearch,weonlyuseAirbnblistingsthathaveoneofmorereviewsasaproxyforactivelistings.Second,wecreateanaccessibilitymatrixofallLSOAswiththe90touristattractionsinLondonthathaveinformationonthenumberofannualvisitsin2015.Thenwecomparethiswiththematrixof3579LSOAthathaveoneofmoreAirbnb’sandcomputetheaccessibilityasafunctionofattractiveness(numberofvisitstotouristattractions)andcost(timeandrent).WeuseOpenTripPlanner,anopensourcemulti-modaltripplanner,tocalculatethetimetotravelfromeachLSOAtothetouristattractionsgivenasetofconstraintsincludingfastestroutesaswellasminimumnumberofmodechangesasthetimevariable,andmedianhousepricesinLSOAlevelfromLandRegistryastherentvariable.WethencomputethecostfunctiontoobtaintheAirbnbaccessibilityscoreforeachLSOA.ThemodelisfittedandcalibratedtofindthebestfitcomparedtotheexistingTransportforLondon(TfL)dataforthePublicTransportAccessibilityLevel(PTAL)scores.TheresultprovidesinputforpredictingtheoptimisedlocationsforAirbnbconsideringthecostfunction.Usingthemodel,itispossibletopinpointwherethenextAirbnbmightlocate.Thisisbeneficialininformingtheoptimumutilisationofthistypeofdigitalinnovationthatchangesrapidlyandinfluencesthecity’surbandynamics.Overall,thispapercontributestoprovidingimportantinsightsintothequantitativemodellingoffastgrowingurbanphenomenonsuchasAirbnb.3. JorisBeckers;IvanDarioCardenasBarbosa;AnnVerhetselModellingtheurbanlayerinB2Ce-commercedistributionnetworks

Onlineshoppingisgainingpopularityeveryyear.From2010to2014,Europesawanaverageannualincreaseinonlinesalesof18%(EcommerceEurope,2015).Withthesurgeofbusinesstoconsumer(B2C)electroniccommercecomesanincreaseinhomedeliveries(Hesse,2002).Sincemostdeliveriesconsistofasmallnumberofitems,theshareofbundledfreightflowsdecreasescomparedtotraditionalretaildistributionprocesses.Thisincreasesdeliverycostsduetohigherstopperparcelandhigheremissionsperparcelratios(Gevaersetal.,2011).Inaddition,highcompetitionintheparcelindustryresultsinlowdemanddensitiesformostplayers,beingoneofthemajorcostdrivers(Browne,2001).This,togetherwithpressurefromconsumersandpolicymakers,pushesparcelcarrierstoinnovateinordertobalancetheefficiencyandsustainabilityofB2Cdeliveries.Withthecreationofdistributioncentresclosertotheconsumer,logisticscarrierscancontainmostofmentionedproblemswithinthelastpartofthedelivery,i.e.thelastmile,whiletheimpactsoftheB2Cgrowthontheremainderofthesupplychainarelessprofound(Fernie&Mckinnon,2004).Morerecently,asanextensionofthepreviousmeasure,carriersareexpandingtheirpickuppointnetworkstofurtherminimizethedistancetotheconsumer,herebyconvertingretailspaceintoalogisticswarehousewithinthecities’borders,whichweidentifyastheurbanlayerofe-commercedistributionnetworks.Inthispaper,similartoearlierwork(Clarkeetal.,2015;Faragetal.,2006),wefirstidentifytheBelgianonlineshopper.Unlikepreviouspublications,thegoalhereisnottoassesse-commercepenetrationinitself,buttotestwhetherpopulationdensitysufficestomodele-commercedemand,orthatademanddensitybasedonthesocio-economiccharacteristicsofthepopulationisnecessary.Toverifythis,weconductastatisticalanalysisofasurveythatpolledtheonlineshoppingbehaviourofover1500internetusers.Theseresultsserveasinputforageographicalmodelthatcomparesthespatialpatternofboththepredicteddemanddensityandpopulationdensity.Inasecondstep,themodeloutcomesareusedtoassessthecurrentpickuppointnetworksinthecaseofAntwerp,Belgium.

Fromthestatisticalanalysis,weconcludethatthereisnosignificantdifferencebetweentheprobabilitiesofurbanandruralinternetuserstoshoponline.Furtheritwasclearthatthewell-educatedmaleinhisthirtieswithagoodjobseemstobemoreeagertoshoponline,whichissimilartopreviousfindings.Thegeographicalmodelprovedthatmodellingsolelybasedonpopulationdensitywouldsignificantlyover-andunderestimatethedemandincertainareas.Thiswasespeciallythecaseinpoorerurbanregions,suburbanBelgiumandthemostruralareasofthecountry.Giventhatmanymodels,bothacademicandprofessional,equatethepopulationdensitywiththedeliverydensitytocalculatedeliverycostsorsustainabilityimpacts,significanterrorswilloccurincertainregions.Ignoranceoftheseerrorsmayleadtomiscalculationsofbothinternalandexternalcosts.

5B-TextualBigdata

1. FrancoiseLucchini;LenyGrassot;JulienBaudry;OlivierGillet;BernardElissaldeSocialnetworks,eventsandcities

BasedontherecordedgeolocatedTwittermessages,ourpaperattemptstoidentifytheurbanrhythmsbyselectingthevariousmodesofurbaneventsfromtheurbanroutine.Wetriedtolinktherelationshipbetweentypeofusers,typeofeventsandthetransmissionofmessagesontheTwittersocialnetworkforParisandtheIledeFranceregion.Inthisareatweetshavebeenrecordedin2015fromMaytoNovember.Thecorpusgatheredreachesanamountof1,300,000geolocatedmessagessentexcludingduplicates.Wefocusedonseveraltypesofeventslike:sports,rockmusicfestivals,orterroristattacks.Todothis,weusedmethodologiesofgraphsthatenabletocombinespatialandtemporalsimplifications.Ourmethodshowsthespatio-temporalvariationofnodesandflowsforlongtimeseriesofspatialsituations.Thelocationsandrhythmoftweetsemittedinthisterritory,revealsrecurringplacesorothersplaceswithsporadicactivity.Forthesameurbanspace,whetherhotspotscanbeeasilydetected,thereisagreatvariabilityoffrequenciesandrhythms.Thislackofautomaticityandthiskindofarrhythmiamakeitpossibletoapproachtemporaryclustersanddiscontinuityintheurbanspace.Accordingtowhethertheeventissingularorcyclical,plannedorunforeseen,whetherittakesplaceinasingleplace,followingarouteortendstoappropriateaspecificneighbourhood,thesetweetshighlightchronotropicurbanspaces.2. MarionMaisonobe;LaurentJegou;BeatriceMilard;MichelGrossetti;DenisEckertTheworldgeographyofscientificvisibility:adeconcentrationprocess(1999-2012)

Becauseoftheinternationalscopeofscientificactivities,studiesonscientificactivitiesspeaksdirectlytodebatesinurbanstudiesliteraturesaboutglobalization.Forsomeresearchers,increaseinexchangesimpliesafocusonmobilityratherthanonstablesocialentities(Urry,2007;Adey,2014).Forotherauthors,thisfluidityofexchangesbenefitsfirsttoverylargeurbanagglomerations,sometimesreferredtoas"world"or"global"cities(Sassen,1991;Taylor,2004),whoseprivilegedsituationandvisibilityallowsthemtocapturetheflowsofresourcesandpeopleandcreatemorewealththanothers.Thiscommunicationaimstoascertainwhethertheterritorialredistributionobservedinthegeographyofscientificproductionbetween2000and2007(Grossettietal.,2014)translatedintoaredistributionofthegeographyofcitations,andthereforeofscientificvisibility.Arepublicationsfromformerlymarginallocationsabletoinfluenceresearchersbasedin“worldcities”,oristheirimpactmostly“provincial”?Becausethedistributionofcitationsisextremelyasymmetrical(Larivièreetal.,2010),itcouldverywellbethatthegeographicde-concentrationofproductionactivitiesdidnotleadtothegeographicde-concentrationofcitations,butinsteadcontributedtocreatingincreasinglyasymmetricalflowsof

informationforthebenefitof“central”citiesandcountries.Thiscommunicationaimstoverifywhetherthisisthecasebyanalysingthegeographicdistributionofcitationsreceivedovera3-yearperiodbypublicationsproducedbetween2000and2007,usingamethodforlocalisingthepublicationsindexedintheWebofSciencebyurbanareas.Resultsshowagrowingconvergencebetweenthegeographyofscientificproductionandthatofscientificcitations.Thenumberofcitationsreceivedbytheworld’s30toppublishingcountriesandcitiestendedtoedgeclosertotheglobalaverage.WhileSingapore,China,IndiaandIransufferedfromadeficitofvisibilityin2000,theirlevelconsiderablyimprovedby2007.Moreover,adecreaseinthediscrepancybetweencities’scientificvisibilityhasbeenobservedinalmostallcountriesoftheworld,exceptforthree:Sweden,EgyptandDenmark.Tofinish,ourresultsshowthatthegapbetweentheshareofcitationsandtheshareofpublicationshasdecreasedacrossalldisciplines.AsignificantasymmetryinfavourofEnglish-speakingcountrieshasremainedinthedistributionofcitationsinhumanitiesandsocialsciences(butitisdiminishing).ReferencesAdey,P.,Bissell,D.,Hannam,K.,Merriman,P.,&Sheller,M.(2014).TheRoutledgeHandbookofMobilities.London:Routledge.Grossetti,M.,Eckert,D.,Gingras,Y.,Jégou,L.,Larivière,V.,&Milard,B.(2014).Citiesandthegeographicaldeconcentrationofscientificactivity:Amultilevelanalysisofpublications(1987-2007).UrbanStudies,51(10),2219-2234.Larivière,V.,Macaluso,B.,Archambault,É.,&Gingras,Y.(2010).Whichscientificelites?Ontheconcentrationofresearchfunds,publicationsandcitations.ResearchEvaluation,19(1),45-53.Sassen,S.(1991).Lavilleglobale :Newyork,Londres,Tokyo.(D.-A.Canal,Trad.).Paris:Descartes&Cie.1996.Taylor,P.J.(2004).WorldCityNetwork:AGlobalUrbanAnalysis.London:Routledge.Urry,J.(2007).Mobilities.Oxford:PolityPress.3. DenisEckert;MarionMaisonobe;JohnHarrison;FrancisHarveyTheevolvinggeographyofacademicplacesinFrance,Germany,andtheUK(1999-2014)

ThefullgeocodingoftheaddressescontainedintheWebofSciencedatabase(1999-2014)allowstoperformabulkofgeographicalanalysisonthedistributionofacademicresearchanditsevolutioninthelast25years.Thecurrentgeographyofacademicresearch,i.e.thespatialdistributionofresearchactivities,isofmajorconcernforfundingagenciesandgovernmentsinallmajorEuropeancountries.Thesepublicorsemi-publicbodiesarewillingtopromoteandfinancean“optimal”spatialdistributionofresearch,inordertofosterthedevelopmentofknowledge-basedsociety.Thishasledinthelast25yearstoabulkoftargetedpoliciesaimingatassociatingfundingswiththepromotionofspatialconcentrationofscience(thetypicalexamplebeing“excellence”policiesinGermany–Exzellenzclusters-orinFrance:Politiquesd’ExcellencesuchasLabexandIdEx).However,thereisasignificantlackofempiricalevidencethatwouldsupporttheeffectivenessofsuchpolicies.Suchpoliciesrelyon“commonlyheldbeliefs”(Grossetti2016)aboutthegeographyofscientificactivities.Ourideaistomobilizemostrecentspatializedbibliographicdataavailable(ourmainsourcebeingtheWebofScience/ScienceCitationIndex)in3majorEuropeancountries:France,

Germany,andtheUK,inordertoanalyseandcheck1-theactualspatialdistributionofacademicresearchinthese3countries,anditsevolutionovertime(1999-2014):aretheyconcentrationorspecializationtrends,ornot?2-thecoherenceoftheseevolutionswiththefundingpolicies,inordertoassesstheeffectivenessoffundingschemesassociating«criticalmasseffect»withspatialconcentration.Wewill,forthatpurpose,usethemostrecentdatageocodedbyourresearchteamsduringthecourseofpreviousresearchprojects(30millionaddressesofpapersregisteredintheWebofSciencesince1999),extractallrelevantdataforGermany,France,andtheUK,andproposesomeinterpretationsofthemajorevolutionarygeographicaltrends,whichcould–ornot,meettheexpectationsofthepoliticiansand“experts”whohave,overthelast25years,designedmostofpublicpoliciesforthedevelopmentofacademicresearch.ReferencesEckert,D.,Grossetti,M.,Jégou,L.,Maisonobe,M.(2014),Lesvillesdelasciencedanslemonde,Mappemonde,n°116,http://mappemonde-archive.mgm.fr/num44/articles/art14401.htmlGrossetti,M.,Eckert,D.,Gingras,Y.,Jégou,L.,Larivière,V.,&Milard,B.(2014).Citiesandthegeographicaldeconcentrationofscientificactivity:Amultilevelanalysisofpublications(1987-2007).UrbanStudies,51(10),2219-2234.Harrison,J.,Smith,D.P.andKinton,C.(2016),Newinstitutionalgeographiesofhighereducation:Theriseoftransregionaluniversityalliances,EnvironmentandPlanningA,DOI:10.1177/0308518X15619175Maisonobe,M,Eckert,D.,Grossetti,M.,Jégou,L.,Milard,B.(2016),Theworldnetworkofscientificcollaborationsbetweencities:domesticorinternationaldynamics?JournalofInformetrics,http://dx.doi.org/10.1016/j.joi.2016.06.0024. CaterinaDeLucia;MarkBartlett;PasqualeBalena;PasqualePazienza;DianaCaporaleModellingthegapbetweentouristpreferencesandlocalknowledgeasdriversoftouristicattractiveness

ApuliaRegionisoneofItaly’smostvisitedtouristdestinationsforitsbeaches,food,localtraditions,culturalandreligiousheritage.In2015,theNationalAgencyofTourismrankedApuliaRegionthirdamongsouthernItalianregionsintermsoftouristarrivalsandBari(thecapitaloftheregion)anditscoastsresultsthemostvisitedfromforeignvisitors.Similarly,in2016theUKTourismStatisticsrankedtheYorkshire(UK)amongthetop10mostvisitedregionsintheUK,withthecityofYorkalone,withitsfamousMinster,amongthetop5mostvisitedcitiesintheUK.Thisworkpursuestwoaims.Thefirstoneistoinvestigateonthemainfeaturesthattouristsexpectfromaplacetovisit.Furthermore,oneaspectoftourismimpactsonaplaceisthatthevisitor’sinterestandsatisfactionisasourceofcommunitypride.Thisissuesseemsdebatedbecauseresidentstakeforgrantedtheirlocalresources.Residentperceptionsoftourismislargelydebatedbythescientificcommunity.Generally,themajorityofstudiesfocusonwhatresidentsperceive.Departingfromthesestudies,thesecondaiminvestigateswhyresidentsperceivetheirownplaceofresidenceasaplaceoftouristicattraction.Toachievetheaboveaimsweuseadecisiontreeandastochasticapproachtounderstandeitherthemainfeaturesorwhatcombinationsoffeaturesofaplaceareimportanttovisitors,andatextminingapproachtouncoverlocalknowledgeasadriverofresidentperceptionsoftourism.Theanalysesarebasedonasurveyconductedonsocialnetworksandconsideracomparativecasestudybetweentwodifferentgeographiclocations,specificallyApuliainsouthernItalyandYorkshireinthenorthofEngland.

Themainresultsuncovermainfeaturesmovingtouristdecisionstovisitforaplaceandlatentinformationofresidentperceptionsoftourism,helpcommunityparticipationinthetworegionstoclosethegapbetweenlocalandvisitorattitudestotourism;andsupportbottom-uptourismdevelopmentandplanningpolicies.

6A-Urbansystems

1. GaetanMontero;CecileTannier;IsabelleThomasOnurbanboundaries:threemethods,threesolutionsforBrussels

Delineatingurbanboundariesisachallengingandimportanttopicforresearchersandplanners:empiricalanalyseshighlydependonthedelineationofthestudyareaandthelimitsofthecitiesareofparticularinterestastheychangeovertimeandspace.Urbangovernancecan’tbeconductedinanefficientwayifurbanboundariesarenotclearlydefined.Manyscientificmethodsexisttodelineateurbanborders.Theyaremainlybasedonfunctionaland/ormorphologicalcriteria.Weherelimitourselvestomorphologicalanalysesusingthefootprintofthebuilt-upassoleinput.Thefirsttwomethodsdealwith“complextheory”anduserespectivelytheMorpholimmethodology-ageographicalapplicationofthefractaltheory(Tannieretal.,2011)and,theNaturalCitiesmethodology-atopologicalapplicationofthecomplexnetworktheory(JiangandMiao,2015).ThethirdmethodisamoreclassicaloneusingtheEuclideanapproach:aLocalDensityIndex.Eachmethodleadstospecificresults(i.e.adifferentdelineationoftheurbanspace).Thegoalofthisanalysisistomeasureandunderstandeachmethodologyandwhere/whyarethedifferences.Whatdotheydoexactlyandwhatdotheyshow?SpecificitiesandbiasesareidentifiedandillustratedusingtheoreticalcitiesandanempiricalanalysisisprovidedontheBrusselsMetropolitanAreainBelgium.Morpholimmeasuresadistancethresholdshowingabreakintheorganisationofthebuilt-upatdifferentscalesandwithoutanyaprioridelineationofthetructure.Thepresenceofadiffuseperiurbanisationand/orthepresenceofabuiltcorridorbetweentwodistincturbanstructuresdeeplyinfluenceMorpholimresults.TheabsenceofcontiguityintheNaturalCitiesmethodology,andtheuseofthecentroidofgroundsurfaceareaofthebuildingsinsteadofthecadastraldetails,explainwhytheNaturalCitiesmethodologyendsupwithurbanspacesthatcorrespondtotheclusteringofminimumthreecentroids.TheDensitymethodologyissubjecttothewell-known“ModifiableAreaUnitProblem”andisunabletomeasurethegeographicorganisationofthefootprintsofthebuildings.TheapplicationofthemethodsontheBrusselsMetropolitanAreaallowstoconfirmthatmorphologicallyBrusselsprawlsoutofitsadministrativeboundaryandthateachmethodleadstoadifferentdelineation.Wealreadyknowthatthedelineationoffunctionalcitiesdeeplydependsonthedata/methodusedandthatmorphologicaldelineationsaredifferentfromanyfunctionalones(Thomasetal.,2012).Thispaperfurtherproofsandremindsthatmethodmatterformorphologicaldelineationsaswellandthatoneisnotbetterthantheother;theyaresimplymeasuringotherthings.Indeed,citiescanbeseenas(1)“hotspots”ofbuildings(NaturalCities),(2)fractalpatternsthatdifferfromtheirsurrounding(Morpholim)or(3)areashavingahigh-densityoccupationofthespace(DensityIndex).2. PavelP.EmIsthereanylinkbetweenfractaldimensionandservicecompanies’entropyinMoscowandSeoul?

MoscowandSeoularetheeconomicheartsoftheirrespectivecountries.However,duetotheirvasturbansprawl,thelevelofeconomicdevelopmentandthelifestylesoftheresidentslivinginthevariouspartsofthesecitiesdiffergreatly.Thisstudyisanattempttoestimatetherelationbetweenthedegreeof

uniformity,thedensitywithinthebuild-upareas,andthedistributionofservicecompanies.Afractaldimensionwasusedtoquantifythelevelofuniformitywithinthebuilt-upareas.SpecialmapsweremadebyusingthecontoursofallthebuildingsavailablefromGoogleMaps,andthenanalyzedusingFractalysesoftware.Theentropyindexwasusedforthesamepurpose,butinthecaseofserviceenterprises.Allofthemweremarkedaspointsonamapindifferentcoloursrepresentingtheirfunction.ThelocationinformationwasbasedonYandexMapsforMoscowandDaumMapsinthecaseofSeoul.Havingmarkedalloftheserviceenterprisesaspointsonthemap,thestudiedareawassubdividedintosquareswithalinelengthof0.5km.Thetotalsaturationofallofthesquareswasthencalculated.Only0.4%ofthesquaresencapsulatedmorethan70companies,whilealmosthalfofthemencompassednomorethan10.Metrostationsattractservicecompanies,butmostofthemcannotaffordtolocatethemselveswithinthesamesquareasthemetrostationduetothehighrentalprice.Notwithstanding,themainpartofthemaresituatedinthesquaresneighbouringthosewiththestation.Thefractaldimensionandtheentropyindexvarydramaticallywithindifferentdistrictsofthestudiedcities.Theuniformitylevelinbuilt-upareasinthecentralpartofMoscowislessthanintherestofthecityduetotheprevalenceofbuildingsnomorethan3-stories;thusMoscowdevelopedoutwardlyinconcentricringsandthemajorityofallofthebuildingswithintheGardenRingRoadwereconstructedbeforetheRevolutionof1917.Theuniformityofservicecompanylocationishigherinthenewly-developedareasofSeoulincomparisonwiththedistrictsbuiltjustaftertheconclusionoftheKoreanWar.Thelevelofuniformityofservicecompanydistributionishigherinthewesternandnorthernpartsofbothcities.Theexceptionisthelocationofsportclubs,stadiums,carservices,shoppingandconsumerservicecenterswhichalmostinallcasesarelocatedclosetothemetrostations.Furthermore,accommodationservicepointsarealwayslocatedclosetotherailwaystations.Thedistributionsofpostalservicesandbeautysalonsdemonstratethehighestdegreeofuniformly.Toquantifythedegreeoftherelationshipbetweenthefractaldimensionforbuilt-upareasandtheentropyindexforthelocationofserviceenterprises,acorrelationindexwascalculated.Thisvaluevariesbetweenthetwocitiesat0.52inSeouland0.57inMoscow,whichsignifiesthepresenceofamoderatedegreeofarelationshipbetweentheseindices.ThereportedstudywasfundedbytheRFBRaccordingtoresearchproject№15-03-00740.3. GiovanniFusco;AlessandroAraldiMultipleFabricAssessment:ExploringtheFormsofaMetropolitanArea.

Quantitativeprotocolsfortheanalysisofurbanformthroughgeoprocessinghavethrivedduringthelastthirtyyears.ThispaperpresentsanewprotocolfortheanalysisoftheformsofurbanfabricsanditsapplicationtothecasestudyofthemetropolitanareaoftheFrenchRiviera(France).Urbanfabricsaremesoscalepatternsofurbanformdefinedbythespatialrelationsbetweenbuildings,streets,parcelsandsite(Conzen1960,CaniggiaandMaffei1979).Urbanmorphologyhasproposedquantitativeandqualitativeanalysisofurbanfabrics,bothinthestudyofmorphologicalprocessesandinurbandesign(BorieandDenieul1984).Geoprocessingprotocolshavedifferentlyimplementedtheapproachesofurbanmorphology,accordingtothefocusandtheultimategoalsoftheanalysis(Marshall2005,LongandKergomard2005,PinhoandOlveira2009,Berghauser-PontandHaupt2010,Giletal.2012,Hamainaetal.2013).Theidentificationofconsistentspatialpatternshasoftenbeenoverlooked,leavingthequestionofmorphologicalregionsoutsidetheresearchagenda.MultipleFabricAssessment(MFA)proposesanewwayofdefiningobservableurbanfabricsfromthepedestrianpointofview.Accordingly,itusesstreetsegmentstocreatenewbasicunitsofanalysis.MFAcouplesmorphologicalindicatorsaddressingthemultidimensionalityofurbanfabricswithgeostatisticalanalysisofsignificantspatialpatternsandBayesianidentificationofurbanfabrictypes.Usingasinputs

usualdigitalmaps,MFAisconceivedfortheanalysisofmetropolitan-wideareas.Thegoalistoidentifyurbanfabrictypesandmorphologicalregionsandproduceanewanalysisoftheorganizationofthecontemporarymetropolis,basedontheobservableformsofthephysicalcity.ThecasestudyoftheFrenchRivierashowshowtraditionalurbanfabricsarecomplementedbynewsuburbanandconnectivefabrics,confirmingtheresultsofmorequalitativestudies.Aboveall,theanalysishighlightsthecomplexityoftheemergingspatialorganizationofthemetropolitanarea.AfirstsourceofcomplexityallowedbytheBayesianprobabilisticapproachofMFAisthedifferentiationbetweencoreandperipheralareasforeachurbanfabrictype:archetypicalcorescoexistwithzonesofoverlappingcharacteristics,bothinvariablespaceandingeographicspace.Asecondsourceofcomplexityisthejuxtapositionofmorphologicalregionsofrelativelyhomogeneousurbanlandscapeswithotherscharacterizedbyafinemixtureofdifferenturbanfabrics.Athirdsourceofcomplexityisthemodularorganizationofthepolycentricmetropolitanarea,coupledwithtrans-scalarself-similarityinthespatialarrangementsofmorphologicalregions.Theseresultscontributetorenewingthewell-establishedframeworksoftraditionalurbanmorphology.MFAisclearlysusceptibleoffurtherimprovementsinthedescriptionofobservableurbanforms.Directionsofmethodologicaldevelopmentwillthusbehighlighted.Fromanurbangeographicalperspective,comparativeanalysisofmetropolitanareasandhistoricalanalysisofmorphologicalprocesseswithinagivenmetropolitanareaseemtwoimportantdirectionsoffurtherresearch.MFAalsohasthepotentialofcontributingtothecross-analysisofurbanformwithsocioeconomicphenomenalinkedtothehumanperceptionofphysicalforms.4. BranoGlumac;MarcosHerreraGomez;JulienLicheronAresidentiallandpriceindexforLuxembourg:Dealingwiththespatialdimension

Thisarticleintroducesaresidential(andmixed-use)landpriceindex,basedonoriginaldataallowingtomergetransactionpricesoflandplotsreportedbynotarydeedsalongwithcadastraldataandgeo-spatialcharacteristicsforalltransactionrecordedinLuxembourgfrom2010onwards.Thisdatabasemakesitpossibletoidentifyandlocateallsalesofavailable(i.e.non-constructed)landplotslocatedinareasmadeforresidentialpurpose(asindicatedwithintheurbanplanningdocuments).Luxembourghasbeencharacterizedforyearsbysteadilyincreasinghousingprices,stemmingfromanincreasinggapbetweenhousingneeds(steeredbytheeconomicanddemographicgrowth)andnewconstruction.Thescarcityofavailablelandand,probablymoreimportantly,thedifficultiesformobilizingtheavailablelandsuggestthataspecificindexforassessingtheevolutionofconstructiblelandiscrucialforthedesignofurbanplanning,housingandtaxpolicies.Afterperformingspatialdependencetests,anindexbasedonaspatialhedonicmodelisconstructedinordertocopewithseveralidentifiedspatialeffects.TheproposedindexisahedonicdoubleimputedFisherindex,relyingonaspatialDurbinerrormodel.Thishedonicmodelincludesaratherlargerangeofexplanatoryvariablescoveringthephysicalcharacteristicsoflandplots,accessibilitytoemploymentandtransportation,proximitytoamenities,aswellasenvironmental,legalandeconomicfactors.Theproposedindexiscomparedtoresultsreachedusingalternativemethodologies,withregardsbothtothetypeofhedonicmodellingand/ortheconstructionoftheindex.Finally,severalrobustnesschecksareconductedinordertoassesstheperformanceoftheunderlyinghedonicmodelandtheassociatedindexregarding:i)qualityofin-sampleandout-of-samplepredictionfromtheunderlyinghedonicmodel;ii)modelcoefficientstabilityovertime;andiii)sensitivitytotheprocedurefordetectingandeliminatingoutliers.5. DaniBroitman;EricKoomenLivinginhistoriccities:Intensificationandincreasingdensitygradients

Urbaneconomicmodelspredictthatastransportcostsdecreaseandpopulationgrowsmonocentriccitiesareexpectedtospreadaroundtheirterritoryandtheirdensityprofileswillflattengradually.Thispredictionisempiricallysupportedbyaplethoraofstudiesabouturbandensitiesdevelopmentformallovertheworld.Thereareexamplesofexceptionstothisrule,buttheycomefromcountrieswithextremelystricturbanregulationsorhistoric-politicalreasons.InthispaperweanalyzetheurbandensitydevelopmentofDutchcitiesduring2000and2012.Theurbandensitytrendsobservedinthecountryareincontradictionwiththeobservationsinseveralpartsoftheworld:MosturbanareasintheNetherlandsareexperiencinganincreasingresidentialdensity,and,toalesserextent,increasingpopulationdensity.InordertoevaluatethestrengthandtherelevanceoftheseincreasingurbandensitytrendswefirstassessthemonocentricityofthemainDutchcities.Restrictingtheurbanareastoacircularringwitharadiusof5kmwedemonstratedthatregardingresidentialandpopulationgradients,themajorityofthecitiescanbeclassifiedasmonocentricusingdatafrom2012.Asasecondstep,weperformedthesamemonocentricassessmentwithdatafrom2000andcomparetheresultswith2012.ForthemajorityoftheDutchcities,bothresidentialandpopulationdensitiesgradientsarebecomingsteeperovertime,contrarytotheworldwideurbandensityflatteningexperience.Inthefewcaseswherethisphenomenaisnotobservedthiscanbeattributedtogovernmentalplanningdecisions,whetherregardinglarge-scaledevelopmentsorbythefoundationsofnewtownsinpreviousdecades.Therefore,asageneralconclusionwecanclaimthatDutchcitiescentersaredenserasawholeandtheirdensitygradienthasincreasedin2012,comparedwiththeirstructureat2000.Althoughitisatoddswithmostoftheurbangrowthliterature,thisfindingisinlinewithsomerecenturbansociologicalresearch.Theexistenceofhistoricalmonumentsandculturalheritagesitesispositivelyrelatedtoincreasingdensities.ThisrelationisobservedfortheDutchurbanstructureasawholeand,forindividualcities,andissignificantforthelargestones.

6B-Accessibility

1. CyrilleMedarddeChardonDetectingoptimalcut-throughlocationstoincreasepedestrianandcyclingmodalshares

Westernsociety’stransportationdesignofprioritizingtheprivatecar,overothermodes,hascreatedcomplexhealth,environmentalandsafetychallenges(Cox,2010).Sprawlingcul-de-sacdevelopmentpatterns,especiallyintheUKandUSA,sincethe1970’shaveledtoless-safe,lessvaluedandlowerdensitycities(MarshallandGarrick,2010).Theresultingroadnetworksdecreaseproximitytoservicesandentrenchdependenceonpersonalmotorizedtransport.Lowerdensitiesandgreaterdistancestopossibledestinationsunderminealternativemodes,suchasbus,rail,walkingandcycling.Suchurbanstructurescreatevulnerabilityandsocialisolationfortheyoungandoldwhohavelessaccesstoprivatevehicles(Martin,2007).Socially,thisexistingurbantransportationlanduseisexclusive,benefitingthewealthier(Martens,2006;Mercier,2009).Thereorientingofurbantransportcanhavehealth,social,justice,securityandquality-of-lifebenefitswhilebeingpreventiveandincreasingresiliencetoenvironmentalandeconomicinstability(Cox,2010).Whilealong-termapproachescanchangedevelopmentpatterns,solutionsarerequiredtoretrofitvulnerableurbandesigns.Accessibilityisbasedonmobility,theabilitytoaccessadequatetransport,andtheproximityofdestinations(Cervero,2005).Whereeitherislacking,suchasproximityinconventionalsuburbancul-de-sacdevelopments,alternativetransportisweakened.Thispaperpresentsamethodologydeterminingoptimallocationsofcut-throughs,pedestrianandcyclingpathswhichexcludemotorizedvehicles,toshortendistancestoservices,suchasshopsandschools,inordertoincreaseaccessibility.Thispresentationwillcoverthemethodology,resultsandcontextforapplicationinsaferoutestoschoolsandotherservices.Themethodologyhasthreesteps.Themeasuringofeuclideanandnetworkdistances

betweenneighbouringpointswithinagrid.Theidentificationofpedestrianandcyclingbarriers.Theselectionofoptimalbarrierzonestoprovidecut-throughsandincreaseaccessibilitythemostefficiently.Notallbarriersareharmful,someprovidesoundandvisualprotectionagainstnegativeexternalities(e.g.,busyroads,trainsandparticularlanduses)orhavenodestinationsbeyondthem.Finally,ademonstrationofthetoolcreatedforpublicandpoliticaloutreachwillbepresented.Thisplatformallowstheinteractivevisualizationofcut-throughcreationintermsofmultiplemodelledmodaltravelsandtheoverallestimatedimpactsonmodalshares.ReferencesCervero,Robert(2005).“AccessibleCitiesandRegions:AFrameworkforSustainableTransportandUrbanisminthe21stCentury”.UCBWorkingPaper,UCBerkeleyCenterforFutureUrbanTransport.Cox,Peter(2010).Movingpeople:Sustainabletransportdevelopment.LondonandNewYork:ZedBooks.Marshall,WesleyEandNormanWGarrick(2010).“Streetnetworktypesandroadsafety:Astudyof24Californiacities”.In:UrbanDesignInternational15.3,pp.133–147.Martens,Karel(2006).“BasingTransportPlanningonPrinciplesofSocialJustice”.In:BerkeleyPlanningJournal19.1,pp.1–17.Martin,George(2007).“Globalmotorization,socialecologyandChina”.In:Area39.1,pp.66–73.Mercier,Jean(2009).“Equity,SocialJustice,andSustainableUrbanTransportationintheTwenty-FirstCentury”.In:AdministrativeTheory&Praxis31.2,pp.145–163.2. OlivierBonin;PierreFrankhauserAccessibility,AmenitiesandNeeds-aconceptualframeworkforexploringhouseholds’satisfaction

Thenotionofaccessibilityisveryfrequentlydiscussedinthecontextofplanning.ItcomesalsointoplayinLUTImodels.Ingeneral,accessibilitysynthetisesthegeneralisedcoststoaccessdifferentkindsofresourcesfromagivenplace.However,thisconceptremainssomewhatambiguousandrathernormative.Thecostfunctioncannotbeconstructedasasimpletransformationofdistancesincecognitiveaspectscomeintoplay.Generally,placesthataretakenintoaccount(jobs,shoppingfacilities,leisureareas,etc.)canbededucedfromsurveys.However,thisapproachbyactivitiesremainsdescriptiveanddoesnotseekforthedeeperreasonsoffrequentingcertainplaces.Thisholdstoowhenconsideringtheinfluenceofamenitiesinresidentialchoicewithoutreferringtoindividuals’needs.Thispaperenrichestheconceptofaccessibilitybyseekingtheoreticalgroundingstothereasonswhyindividualsfrequentdifferentplaces.Wemakeuseoftheconceptofhumanneeds(Maslow,1954;Max-Neef,1991;RyffandKeyes,1995;RyanandDeci,2000amongothers)thatalsoallowsestablishingalinktosatisfaction.InvestigationsconductedbyTayandDienerin2011focusedonexploringempiricallythisconcept.Thisrecentstudyshowedthatuniversalneedsactasgoodpredictorstoassesssubjectivewell-being.Needsandwell-beingseemtobelinkedinacomparablewayonaworld-widelevel,evenifculturalreferencesmaychangethehierarchyofneeds.Thetheoreticalapproachofhumanneeddoesnotexplicithowneedscanbesatisfiedinagivenculturalcontext,e.g.whatkindofamenitiescorrespondtodifferentneeds.Itsaysevenlessofwheretheseamenitiesshouldbelocalisedandhowtheycouldbeaccessed,whicharecrucialquestionsforurbanandlandplanners.However,thetheoryofneedsprovidespreciousinformationontheimportanceoftheseneedsandtheirlinktosatisfaction.Thus,needtheorycanhelpselectingamenitiesandevaluatingbothdistancesandtherankoftheamenityinanaccessibilityfunction.

ThisleadsusconsideringthearticulationbetweenthetheoryofneedsandamenitiesforthegreatermetropolitanregionofParis.Weconstructamatrixlinkingneedsandsatisfiers,thelattercorrespondingtoamenitiesthatareallgeolocalised.Thenourevaluationfunctioncomputesscoresforeachlocationbyassessingaccessibilitytoamenitiesaccordingtotheirdistanceandrareness,enablingustocomputeanindexsummarisingtheeasinesstosatisfyhumanneedsforhouseholdslocatedhere.Maslow,A.(1954).MotivationandPersonality.Harper&Row,New-York,ISBN9780060419875.Max-Neef,M.A.(1991).Humanscaledevelopment.TheApexPress,New-YorkandLondon,ISBN0-945257-35-XRyan,R.M.,&Deci,E.L.(2000).Self-determinationtheoryandthefacilitationofintrinsicmotivation,socialdevelopment,andwell-being.AmericanPsychologist,55,68–78.doi:10.1037/0003-066X.55.1.68Ryff,C.D.,&Keyes,C.L.M.(1995).Thestructureofpsychologicalwell-beingrevisited.JournalofPersonalityandSocialPsychology,69,719–727.doi:10.1037/0022-3514.69.4.719Tay,L.,Diener,E.(2011).Needsandsubjectivewell-beingaroundtheworld.JournalofPersonalityandSocialPsychology101,354–365.doi:10.1037/a00237793. JuliaH.TabbitaHousinginformalityintheBuenosAiresMetropolitanRegion:Aquantitative-spatialassessmentforregularizationandlandusepolicies

Housinginformalityisagrowingandlong-standingphenomenoninmostLatinAmericancities.Overthelastthirtyyears,despitethewidespreadimplementationofregularizationpolicies,lowincomehouseholdsstillaccessurbanlandandhousingprimarilythroughinformalmechanisms.Accordingto2010Censusresults,inArgentinamorethanafifthofhouseholdsintheBuenosAiresMetropolitanRegion(BAMR)liveininadequatehousingconditions.Althoughinformalsettlementslocatedininner-cityareasdifferfromperipheralrecentlyformedones,regularizationprogramshavenotadequatelyconsideredthespecificphysicalandspatialfeaturesofdifferenttypesofsettlementsatametropolitanscale.Norhaveevaluationsbeenmadeofthefeasibilityofupgradinginformalareaswithnoaccesstotheexistentinfrastructureandtransportnetwork.Theseunaddressedissueshaveledtoadoptingsimilarregularizationstrategiesinhighlydifferentcontextswithinefficientresults.ThepurposeofthisstudyistoidentifyandmapdifferenttypesofinformalsettlementsintheBAMRusingmultivariateanalysisandadvancedquantitativespatialmethods.Theinformalareasaccessibilitytotheinfrastructureandpublictransportnetworkwillalsobeassessed.TheresultsareaimedatimprovingregularizationpoliciestoredressinformalareasatametropolitanscaleintheBAMR.4. JusteRaimbault;SoleneBaffiStructuralSegregation:AssessingtheimpactofSouthAfricanApartheidonUnderlyingDynamicsofInteractionsbetweenNetworksandTerritories

NetworksandTerritoriesTransportationNetworkscanbeleveragedasapowerfulsocio-economiccontroltool,withevenmoresignificantoutcomeswhenitpercolatestotheirinteractionwithterritories.ThecaseofSouthAfricaisanaccurateillustration,asBaffi(2016)showsthatduringapartheidrailwaynetworkplanningwasusedasaracialsegregationtoolbyshapingstronglyconstrainedmobilityandaccessibilitypatterns.Weproposetoinvestigatethepotentialstructuralpropertiesofthishistoricalprocess,byfocusingondynamicalpatternsofinteractionsbetweentherailwaynetworkandcitygrowth.Moreprecisely,wetrytoestablishifthesegregativeplanningpoliciesdidactuallymodifythetrajectoryofthecoupledsystem,whatwouldcorrespondtodeeperandwiderimpacts.NetworkMeasuresWeuseacomprehensivedatabasecoveringthefullSouthAfricanrailwaynetworkfrom1880to2000with

openingandclosingdatesforeachstationandlink,togetherwithacitydatabasespanningfrom1911to1991forwhichconsistentontologiesforurbanareashavebeenensured.First,adynamicalstudyofnetworkmeasuresseemtoconfirmthehypothesis:atrendruptureinclosenesscentrality(definedforanodeastheaveragetraveltimetoothernodes)ataroughlyconstantnetworksizeevolution,atadatecorrespondingtothebeginningofofficialsegregativepolicies,suggeststhattheplanningprocessafterthisdatehadinthebestcasenoglobaleffectonnetworkperformance,andintheworstcasehadintendednegativeeffectsonaccessibilitywiththeaimtophysicallysegregatemore.CausalitypatternsWethenturntodynamicalinteractionsbetweentherailwaynetworkandcitygrowth.Forthat,westudyGrangercausalities,inthelargesenseofcorrelationsbetweenlaggedvariables,estimatedbetweencitiesgrowthratesandaccessibilitydifferentialsduetonetworkgrowth.Wetestbothtravel-timeandpopulationweightedaccessibilities,forvaryingvaluesofdistancedecayparameter.Wefindthatresultsaresignificantwithtravel-timeaccessibilityonly,autocorrelationdominatingwithweightedaccessibility.Atime-windowof30yearsappearstobeagoodcompromisebetweenthenumberofsignificantcorrelations(p<0.1foraFishertest)andtheabsolutecorrelationlevelacrossalllagsanddistancedecays,whatshouldcorrespondroughlytothetime-stationarityscaleofthesystem.Weobtaintherethroughclearcausalitypatterns,namelyaninversionoftheGrangercausality(laggedcorrelationupto0.5forseveralvaluesofdistancedecay),fromaccessibilitycausingpopulationgrowthwithalagof10-20yearsbeforetheapartheid(1948),totheoppositeaftertheapartheid(lag20years).WeinterprettheseasStructuralsegregation,i.e.asignificantimpactofplanningpoliciesondynamicsofinteractionsbetweennetworksandterritories.Indeed,thefirstregimecorrespondstodirecteffectoftransportationonmigrationsinafreecontextinoppositiontothesecondone.Furtherworkshouldconsistinsimilarstudywithmoreprecisesocio-economicvariables,forexamplequantifyingdirectlysegregationpatterns.ReferencesBaffi,S.(2016).RailwaysandcityinterritorializationprocessesinSouthAfrica:fromseparationtointegration?Theses,UniversitéParis1-PanthéonSorbonne.5. EusebioOdiari,MarkBirkin,SusanGrant-MullerandNickMallesonTheuseofbigdatainspatialmicro-simulationofrailwaypassengers

Intheabsenceofanattribute-rich,comprehensiveandrepresentativepopulation,asimulatedpopulationisnecessaryfortheanalysisofcomplexmobilityontherailways.Novelconsumerdatasetscalled‘bigdata’tendtohavecomprehensivespecificcoverage,butarenotrepresentativeoftheentirepopulation.Thedetailedheterogeneityinthesedatasetscanbebetterharnessedbyintegratingwithotherrelevantdatasetsfrommeasuredstatedsurveyswhicharedesignedtoberandomsamplesrepresentativeofthepopulation.Inthispaperbothdeterministicandstochasticspatialmicro-simulationstrategiesarediscussedforcombiningvariousdatasets,creatingarepresentativemicro-mobilitypopulationofrailwaypassengersembeddedinthewiderpopulationwithinageographicregion(WestYorkshire,UK).Adeterministicmethodologyparticularlysuitedtoadjustskewedrail-mobilityconsumerbigdataisdeveloped,combininginformationonallrailticketssoldintheUK,withthe2011CensuscommutetoworkdataandaNationalRailTravelSurvey(NRTS),yieldingarepresentativemicro-levelpopulation.Thedeterministicstrategyusingmulti-dimensionaliterativeproportionalfittingispresentedinaconciseandaccessibleway,highlightingnuances,precautionsinpracticalapplication,andassociatedadvantagesofthemethodology.Thesimulatedpopulationcreatedincludesweightswhichrepresenttheprobabilitydensityforrailcommuters,suchthatasampleofthesyntheticpopulationaccordingtothedensityyieldsrepresentativerailpassengers,whilstauniformsampleyieldsthewiderpopulationsample.SuchasyntheticpopulationcanbefedthroughalogisticGISrailnetworktoestablishfurtherpassengerbehaviour,creatingarichcomprehensivedatasetforsubsequentanalysis.

7A-Demographics

1. DavidBurgRecurringPatternsinUrbanGrowth

Thelargecitiesoftheworldexhibitrecurringmacroscopicpatternsofgrowthwithpopulationsexpandingrapidlythoughtherateofgrowthdecreaseswithtimeastheeffectsofconstraintsonthesystembecomemoresignificant.Theaimofthisresearchistorevisitandrefinethisapproach.Adetaileddynamicalanalysiswasperformedonpopulationdataofcitiesusingthelogisticmodel:

P(t)=K/[1+e^{-(ln(81)/a)(t-tm)}]

where'a'isthecharacteristictimefortheprocesstogrowfrom10%to90%and'tm'istheinflectionpointbifurcatingexponentialandlogarithmicgrowthphases.'K'istheupperpopulationlimit;however,nonlinearfittingmethodstendtounderestimatethisvalue.Toalleviatethis,wederiveareparameterizationaccountingforthecouplingbetweena,Kandthemaximumgrowthrate,s.Theadvantageofthissubstitutionisfittingthefunctiontothelinearphaseofthelogisticcurvegivesbetterestimatesfortheupperboundonpopulationsizewhentheprocesshasnotyetsaturated.Thepatternsdemonstratedforlargecities,asdefinedbyadministrativeboundaries,MetropolitanAreasor'naturalcities',confirmthehypothesisthattheyexhibitrecurringgrowthcurvesconsistentwiththelogisticmodel.Eachcityhasuniqueparametervalues,evenso,thenarrowdistributionofvaluesforthegrowthrateconstantsuggestssimilaritiesofurbanizationratesglobally.Further,thedatashowdecreasinggrowthratesinmanycitiesthroughoutthelastfewdecades.Takentogether,theseresultsmaygiveinsightintothedynamicsofcitygrowthforcivicandnationalpolicyandconceivablypredictoutcomesofmodernurbanization,ingeneral.

2. FrancoisBavaud;TheophileEmmanouilidis;GuillaumeGuexHandlingsocialsegregationingravitymodelling:acomputablescheme

Spatialflows(migrants,commuters,journeytoschools,goodstransportation,financialtransfers,communications...)defineamatrixofcountsbetweennoriginsOandmdestinationsD.Relativespatialflowsspecifiesanempiricalbivariatedistribution,anddefineinturnaweightedbipartitenetwork,oraweightedorientednetworkwhenoriginsanddestinationscoincide.Spatialflowsaretraditionallymodelledbythefamilyofgravitymodels(seeSiminietal.2012forarecent“radiationmodelling”alternative),whereoriginand/ordestinationcountsmaybyfixed,ornot.Inabsenceofexogenouspredictors(distanceortransportationcostexcepted),gravitymodelsforODconstrainedflowscanbederivedfromdiffering,yetequivalentformalschemes:(a)Jaynes-likemaximumentropyprinciplewithgivenaveragetransportationcost(Wilson1967)(b)model-basediterativefitting,followingtheinformation-theoreticalternatingminimizationframework(Csiszár1975),ortheEM-algorithm(c)regularizedoptimaltransportationproblem,involvinganadditionalorigin-destinationmutualinformationtermI(O:D),makingthesolutionuniqueanddetectingboundariesbetweensoftODclusters(e.g.Guexetal.2016).Weproposeaformallytractableextensionofthegravitymodellingbyconsideringinadditiona“social”categoricalvariableAwithptypes(suchasnationality,ethnicity,socialclass,schoolperformance,orgender),attachedattheunitsattheoriginO.SocialtypesAanddestinationsDareconditionallyindependentgiventheoriginsO,andthemutualinformationI(A:D)definesameasureofsocialsegregation.

Minimizingthe“freeenergy”U+cI(A:D)+TI(O:D),whereUisthetransportationcostU,andcandTaresuitableadjustablepositivecoefficients,turnsouttoyieldacomputableiterativescheme,yieldinganuniqueoptimalsolution.Inparticular,thedistanceversussegregationtrade-offcanbemadeexplicit-animportantprerequisiteforinformedplanningdecisions.Also,thesocialmixingparametercanbeextendedtonegativevaluesc<0,asinsegregativeplanning,aimingtoconstructsociallyhomogeneousdestinations,suchasschoolspreferentiallyprovidedtopupilsofthesamesocialcategory.Convexitybreaksdownforc<-1/T,pavingthewayforlocallyoptimaldistinctODflows.Quantitative“social”variablesA(suchaspersonalwealth,orschoolperformance)canalsobehandledbytheabovescheme,undertwovariants:(a)adjustingODflowssoasobtain(forc>0)destinationsmadeofaggregatessimilarintheaverageandthedispersionofA(b)onlyrequiringacomparableaveragelevelofAamongunitsatthesamedestinations.Simulatedexamples,aswellasrealcasestudies(segregationinprimaryschoolassignmentinLausanne,Switzerland;Emmanouilidisetal.2016)illustratethetheory.References:Simini,F.,González,M.C.,Maritan,A.,Barabási,A.L.(2012).Auniversalmodelformobilityandmigrationpatterns,Nature,484(7392),96-100.Wilson,A.G.(1967).Astatisticaltheoryofspatialdistributionmodels.Transportationresearch,1(3),253-269.Csiszár,I.(1975).I-divergencegeometryofprobabilitydistributionsandminimizationproblems.TheAnnalsofProbability,146-158.Guex,G.,Emmanouilidis,E.,Bavaud,F.(2016).Transportationclustering:aregularizedversionoftheoptimaltransportationproblem,submittedforpublication.Emmanouilidis,E.,Guex,G.,Bavaud,F.(2016).Thetransformedoptimaltransportationproblem:sensitivityandsegregationofthechildren-to-schoolconstrainedassignmentinLausanne,ProceedingsofSpatialAccuracy2016,333-340.3. DoignonYoannWhichspatialconvergenceofdemographicageinginMediterranean?

TheoreticalframeworkConvergenceisatemporalprocesswherebythedisparitiesbetweenindividualsdecreaseovertime.Theconceptofconvergenceisappropriateforpopulationstudies.Typically,demographictransitionisexplicitlyaprocessofconvergence(Wilson,2001).Thepopulationisconvergingfromayoungstructuretoanolderone.Thisworkaimstostudytheconvergenceofdemographicageing,byexplicitlyintegratingthespatialdimensionandbyusingoriginalmethods,littleusedindemography.Tothisend,wechosetheMediterranean,characterisedbyadichotomybetweenthesouthernandnorthernshore,withhigherfertilityandmortalityratesontheformer,andanolderpopulationonthelatter.Inyearstocome,thiswillcauseaconvergenceofdemographicageingfromthesoutherntowardsthenorthernshore.Todosowillrequireustocarryoutpopulationprojections.Finally,inlightoftheregionaldisparities,asub-national

scalewillbeusedforthisstudy.EmpiricalapproachTostudytheconvergenceofdemographicageingonansub-nationalscaleintheMediterranean,wehadtofaceseveralmethodologicalchallenges.First,wecreatedasub-nationalgridfortheentireMediterranean.Eachcountrypossessesnumerousadministrativelevels.However,forthepurposesofasub-nationalstudy,itwasappropriatetoselectonlyonepercountry.Tolimittheeffectsofscale,weconstructedasub-nationalgridharmonisedbysurfacearea,thatis,bychoosingadministrativelevelsofasimilaraveragesize.Next,forpopulationprojections,wechosethecomponentmethodovera50-yearperiod(2015-2065).Thisrequiredacertainamountofdata.However,thereisnocentraldataofficefortheMediterraneanarea.Wethereforecollectedthisdatafromeachnationalstatisticalofficebutalsofromspecificsurveysandinternationalorganisations.Thiscollectionprocessyieldedahighlyheterogeneoussetofdata,assomecountrieshavehighlydetaileddataavailable,whereasinothers,itisimperfectorsummary.Wethenuseddemographicmethodsofindirectestimationtoovercometheseproblems.Wethenmadeprojectionsusingthecomponentmethod,optingforaclosedpopulation.Forthefuturedevelopmentoffertilityandmortality,weemployedprojectionsfromtheUnitedNations'WorldPopulationProspects2015.WeadaptedtheUN'svariousdevelopmentvariantstoourdata(sub-nationalterritoriesfollowthesametrendasthecountrytowhichtheybelong)toconstructfourprospectivescenariosoveraperiodof50years(2015-2065).Usingthisdata,themethodsofeconometricsandspatialeconometricswereappliedtomeasuretheconvergenceofdemographicageing(σ-convergence,localσ-convergence,Markovchains,spatialMarkovchains).ResultsFirst,themeasureofσ-convergenceshowsanoveralldecreaseindisparityovertime.Wehighlightconvergencespatialclubs.Territorieswiththesametrajectoryarenotrandomlydistributedinspace.Spacethusplaysakeyroleintheconvergenceofdemographicageing.Therefore,localσ-convergenceshowsareasofspatialconvergence,butalsoofspatialdivergence.Theoppositionbetweenthenorthernandsouthernshoresbecomesmuchlessmarkedin2065.Finally,weprovethattheconvergenceprocessisthusheavilyinfluencedbythespatialdimension.4. KhneyzerChadiLebanonbetweenanalogyandspecificityofterritorialdevelopmentprocesses:ThenecessaryoriginalityofacredibledevelopmentstrategyfortheAkkar

Sincetheendofthewar(thatbeganin1991),Lebanon,asmallMediterraneancountry,haswitnessedperiodsofsignificanteconomicgrowth,aidedbythereconstructionprocessandsupportedbythepublicpoliciesofthetime.Nevertheless,thisgrowthwasmainlyquantitative,focusingonthecapitalBeirut,neglectingtoalargeextenttheneedsofregionaldevelopment.Amorecomprehensivevisionofintegrateddevelopmenthasnotbeenrealizedatnationallevel.DevelopmentwasevenlessincertainregionssuchasAkkarinnorthernLebanon.However,simultaneously,Lebanonisfoundtohavewitnessedafairlyuniquephenomenonformorethanadecade,whichcanbedescribedasa"counter-exodus"(reversedexodus)movement.Infact,asthenumberofcitiesandmetropolitanareasincreases,andtheirinfluencesbecomemorepronouncedinlifestylesandmobilities,moreandmoreinhabitantsofthecityarereturningtosettleandliveinareasconsideredtoberuralorsemi-ruralareas.TheterritorialeconomyinLebanonisrandomandhasnotheoreticalbasisinitsconception.WebelievethatthisgapneedstobefilledandthataterritorialeconomicpolicymustbecreatedinLebanonto

managetheprocessesandrebalancethedevelopmentdynamics.Onthebasisofthisobservation,wewishtoexaminethereasonsbehindthis"counter-exodus"(reversedexodus)and,inparticular,toanswerthefollowingquestions:Howcanwerevealthefactorswhichsupportthedevelopmentprocessthatallowsarebalancingatthenationallevel?WhatarethefactorsofterritorialattractivenessinLebaneseruralareasthatarestrongenoughtoexplainthisinverse(intheoppositedirection)mobility(movementorflow)?Basedonthepreceptsoftheterritorialeconomyandthesocialandsolidarityeconomy,weaimtostudytheopportunitiesforcreatingworkopportunitiesintheagriculturalandagri-foodsectorsthatwouldresolveallsocial,economic,andfinancialimbalancethroughtheconductionofancomprehensiveterritorialstudy.Inordertoprovideanswerstothisproblem,severalquestionsmustbeaskedfirst:isthereadevelopmentmodelimplementedinthemoredynamicLebaneseregions,whichcanbetransposed(similarlyimplemented)toAkkar?Ifnot,isAkkarsomarkedwithirreducibleparticularismsthataspecificstrategyshouldbeconsidered?ThisstudyneedstosituateourreasoninginthecurrentcontextofLebanonandthereforetoaddressthefollowingpoints:-Whatkindofplanningisneededtomeettheneedsofdifferenttypesofterritorialmanagement?-Istherearelay(relationshipandcommunication)betweenthestateandthelocalauthoritiesatthelocallevel?-WhattradepoliciesshouldbechosentoimprovesecurityandfoodgovernanceinLebanon?-WhatcanbethefutureofagricultureinLebanon(commercialorproximity,irrigatedorrainfed,ecologicalorintensive.)?Oncethesequestionsareexaminedandanswered,itwillbepossibletocreateanddrawaneffectivepolicyframeworkattheregionallevelsothattheattractivenessofLebaneseruralareasissustainableandconducivetolocaldevelopment.5. AnnaDmowska;TomaszStepinskiEvolutionofRacialDiversityinNewlyBuiltAmericanHousingSubdivisions

TheUnitedStatesisraciallydiverseasawholebutsegregatedatthelocallevel.Thelocalsegregationhasbeenpreviouslystudiedatthelevelofmetropolitanareasusingdemographicdataaggregatedtocensustracts.Limitationsinspatialresolutionandyear-to-yearcompatibilityofcensustractsmakeitimpossibletostudyevolutionofracialdiversityatthefundamentallocallevelofindividualneighborhoods.Usingrecentlydeveloped(http://sil.uc.edu/webapps/socscape_usa/)30mresolution,year-to-yearcompatibledemographicgridsfor1990,2000,and2010wetracked2000-2010evolutionofracialdiversityinhousingsubdivisionsbuiltbetween1990and2000.First,weidentifiedall3,382“new”subdivisionsacrosstheentireU.S.asurbanizedareaspresentinthe2000gridbutabsentinthe1990grid.Foreachnewsubdivisionwealsoidentifiedits“neighborhood”–asetofpre-1990subdivisionsintheradiusof4kmtoserveasacontrolsample;altogethertherewere48,976suchcontrolsubdivisions.Allsubdivisions(newandcontrol)areclassifiedintooneofthreecategoriesofracialdiversity:low(L),medium(M),andhigh(H)basedontheschemedevelopedinourpreviousworkandusingdatafromthe2000grid.Bycomparingdiversitycategoryofnewlybuiltsubdivisionswithprevailingcategoryoftheirpre-1990neighborswefoundthat24%ofnewlybuiltsubdivisionsstartasmorediversethantheiroldersurroundings,11%arelessdiverse,and65%conformtoadiversityleveloftheirsurroundings.Nextwere-evaluatedracialdiversityof1990-2000-builtsubdivisionsandtheiroldersurroundingsafter10yearsbyreclassifyingthemintodiversitycategoriesusingthe2010grid.Wefoundthatthemajority(78%)of1990-2000-builtsubdivisionsmaintainedtheirdiversitycategoryafter10years,19%increasedtheirdiversity,while3%decreasedtheirdiversity.Overthesametime68%oftheiroldersurroundingsinthecontrolsetalsomaintainedtheirdiversitycategory,while27%increasedand5%decreasedtheirdiversity.Moreindepthlookrevealsthatthegreatestincreaseindiversitywithinasetofcontrolsubdivisionsisassociatedwiththose1990-2000-builtsubdivisionswhichincreasedtheirdiversityinthe2000-2010

period.Controlsubdivisionsassociatedwith1990-2000-buildsubdivisionswhichremainedsteadyordecreasedtheirdiversityexperiencessmallerincreases.Resultsofthiscomprehensivestudyareinterpretedasfollows.Whenanewhousingsubdivisionisbuiltitsresidentstendtobestatisticallymoreraciallydiversethanthoseinneighboringsubdivisions.Thisislikelyduetoshiftingsocialattitudes,changingincomestratification,increasingminoritypopulation,andexistenceofanti-discriminatorylaws.Overthe10yearsperiodthediversitygapbetweenthenewlybuiltsubdivisionanditsolderneighborsnarrows,especiallyintheareasofthestrongestoveralldiversityincrease.Ifthistrendcontinue,Americansuburbia(ifnotitsinnercities)willbecomelocallydiverseinthenextfewdecades.

7B-Bigdata

1. TomasCrols;NicolasMallesonQuantifyingtheAmbientPopulationusingBigDataandAgent-BasedModelling

Understandingandquantifyingthenumberofpeoplewhoareoutsideinurbanareas(commonlytermedthe'ambient'population)isanimportantresearchareawithrelevanceacrossseveraldisciplines.Theseincludecrimescience(understandingthepopulationatriskofcrime)[1-3],environmentalscience(quantifyingexposuretoairpollution)[4],healthandtransportsciences(measuringthehealthimpactsofdiversetravelbehaviours)[5],etc.Althoughabundantdataexisttoquantifytheresidentialpopulation,therehastraditionallybeenaseverelackofreliableinformationregardingtheambientpopulation.Thishasbeguntochangeinrecentyearsasthe'bigdatarevolution'hasledtotheemergenceofvastnewdatasetsthatdescribehumanmovement[e.g.6]athighdegreesofspatialandtemporalaccuracy.Examplesofsuchdataincludevehicletrafficcounters,mobilephoneusage,socialmediacontributions,publictransportsmartcarduse,etc.Thesedatasetsareextremelydiverse,however,andtherehasbeenverylimitedworktoreconcilethesedisparatedataandcreateasingle,robustmeasureoftheambientpopulation.Thispresentationwillfocusonongoingworkthatisattemptingtoleveragetraditionaland‘Big’datasetstoquantifytheambientpopulation.Theapproachtakenhereisnovel;theresearchprogrammeproposestouseagent-basedmodellingasameansofsimulatingtheambientpopulationatanindividuallevel.‘Big’datastreamswillbeusedtocalibrateandvalidatethemodel,ultimatelyinrealtimeastheyemerge.Inthismanner,themodelwillbeabletoincorporatedatafromadiverserangeofsources,creatinganup-to-date,realisticrepresentationofthebehaviourofthepopulationasittravelsaroundanurbanarea.Specifically,thepaperwillpresentthemostrecentresultsfromasmallcasestudyinatowninYorkshire,UK.Itwilldemonstratehowarelativelysimplemodelcanproduceestimatesoftheambientpopulation,andwillvalidatetheseagainstestimatesofthepopulationthathavebeencapturedusingWi-Fisensorstodetectthenumberofmobilephonespassingcertainpointsinthetown.References[1]Andresen,M.A.,Jenion,G.W.,&Reid,A.A.(2012).Anevaluationofambientpopulationestimatesforuseincrimeanalysis.CrimeMapping:AJournalofResearchandPractice,4(1),7–30.[2]Boivin,R.,&Felson,M.(2017).CrimesbyVisitorsVersusCrimesbyResidents:TheInfluenceofVisitorInflows.JournalofQuantitativeCriminology(onlinefirst).[3]Malleson,N.,&Andresen,M.A.(2016).ExploringtheimpactofambientpopulationmeasuresonLondoncrimehotspots.JournalofCriminalJustice,46,52–63.[4]Park,Y.M.,&Kwan,M.-P.(2017).IndividualExposureEstimatesMayBeErroneousWhen

SpatiotemporalVariabilityofAirPollutionandHumanMobilityAreIgnored.Health&Place,43,85–94[5]Flint,E.,Webb,E.,&Cummins,S.(2016).Changeincommutemodeandbody-massindex:prospective,longitudinalevidencefromUKBiobank.TheLancetPublicHealth,1(2),46–55[6]Kitchin,R.(2014).TheReal-TimeCity?BigDataandSmartUrbanism.GeoJournal,79(1),1–14.

2. ArnaudAdam;IsabelleThomasRevisitingtheinternalstructureofBrusselswithmobilephonedata:theoreticalrevolutionordataevolution?

WiththeadventofBig-data,newmathematicalmethodologiesarecurrentlyusedanddevelopedtodecomposeadelugeofdataintosimpleinformation.Appliedtospatialdatasets,theyopennewavenuesforurbananalysesbydepictingtheinternalorganizationofcitiesandbymeasuringtheirspatio-temporalvariations.WehereusemobilephonecallstodelineateinteractionpatternswithinthemetropolitanareaofBrussels;theLouvainMethodisappliedonalmost20millionmobilephonecallscollectedinApril–May2015.Usingtheoriginandthedestinationofthecalls,wedetectcommunitiesofantennasthataretightlylinkedintermsofnumberofcalls,duringworkandnon-workinghoursand,withtheintroductionofaparameterintheLouvainmethod,thesespatialgroupsaredecomposedintosub-communitiesandfurtheranalyzed.Therobustnessoftheresultsintermsofdataandthemethodsarein-depthdiscussed.Moreover,theantennasareindividuallycharacterizedintermsofthesocio-economicconditionsandthetimedimensionofthecallsarehenceconsidered.Communitiesofmobilephonecallsarecomparedandfurtherdiscussed.Doesthesocio-economicconditionsofplacesinfluencetheorganizationofcommunities?Aresomeareasmoreactivethanothers?Resultsshowthat(1)surprisingly,thecommunitiesofcallsfollowquitewelltheexpectedcompositionoftheBrusselsCapitalRegionand,furthermorecanbeeasilyrelatedtothetransportnetworkorganization,(2)thesocio-economicconditionsaroundtheantennashelptocharacterizetheinteractionpatternswithinthemetropolitanareaofBrussels.ThisworkshowsthatcombiningnewandclassicalanalysesforinterpretingBig-Dataallowtodepicttheurbancomplexityandopensnewavenuesforfutureresearch.

3. RobinCuraMakinglargespatio-temporaldataanalysiseasier:Illustratedpleaforusing(geo)VisualAnalytics

In1998,PeterFisheradvocatedforanimprovementofgeographicdataexploration,consideringthat“softwaretoolsneedtobedevelopedtotransformdataintointelligibleviewsandexploratorytools.[...]Itmustbemademucheasier”(Fisher,1998).ForAndrienkoetal.(2014),thisissue“remainssharplypertinentnearly20yearslater”.Factis,despitebeingabletorelyonsomeveryusefulandpraisedtoolslikeGeoDa,mostofthegeographicaldataisstillexploredandanalyzedwithintheboundsoftraditionalGISsoftware,especiallywithintheTheoreticalandQuantitativeGeographycommunity.AlanMacEachren(2017)calledforareneweduseof(Geo)VisualAnalytics.HeespeciallyinsistsontheopportunitiesthatBigDataoffers,pleading“fora‘human-in-the-loop’(geo)VisualAnalyticsapproachtoleveragingbig(geo)data”.Isuggestthatthisapproachcanalsobeappliedtoregularspatio-temporallargedatasets.Togetherwiththeemergenceofthe“geodatascience”andthegrowingcomplexityofanalysismethods,mostofthe

recentresearchonthisfieldfocusondevelopingnewcomplexmethods.Withtoday’scomputationpowerincreaseandmoreemployabletechnologies,TQGcommunitycouldmakeuseofthesimplestvisualexplorationmethodsthatcouldnotbeappliedonlargedatasetuntilrecently.Amongthese,Iconsidertraditionalmethodssuchassmallmultiples,linkedviewsandfilters,temporalaswellasspatialandthematic.Inthistalk,I’llpresentsuchauseonthreelargeandinherentlyspatialandtemporaldatasets.(1)ThefirstcomesfromtheGoogleLocationHistoryofananonymoususer.Itconsistsofafrequentrecording(every3minutes)oftheuser’ssmartphonelocation.Albeitthelackofcontextualdata,wewillshowhowthismassofdatacanallowtogatherverypersonalinformationsabouttheuser.(2)WealsopresentanexplorationoftheCitiBikedataset.OriginatingfromNew-YorkCitybikesharingsystem,itconsistsofarecordforeachtrip.Togetherwithspatio-temporalinformations,thematicdatalikeageandgenderofuserscanhelpfigureoutdifferentusesofthebikesystemthroughtimeandspace.(3)Thelastdatasetisabouttransit:formultipleroadsegments,road-censorsinParisregistersthecartrafficeveryhour.Gatheredoverayear,thisverylargedatasetcanhelpdetectingpatternsandspecialeventsinthecarflow.Thosedatasetsandquestionswereaddressedbycreatingspecificwebapplicationsdedicatedtotheirvisualexploration,andIwilltrytoexposethegeneralityofsuchanapproach.Aparticularattentionwillbededicatedtoshowinghoweasyitcanbetobuildsuchad-hocexploratorytools,and,moreimportantly,howsimpleitcanbetouse.AndrienkoG.,FabrikantS.,GriffinA.,DykesJ.,SchieweJ.(2014).Geoviz:Interactivemapsthathelppeoplethink.InternationalJournalofGeographicalInformationScience,28(10)FisherP.(1998).IsGISHideboundbytheLegacyofCartography?TheCartographicJournal,35(1)MacEachrenA.(2017).LeveragingBig(Geo)Datawith(Geo)VisualAnalytics:PlaceastheNextFrontier.InSpatialDataHandlinginBigDataEra

4. HoracioSamaniego;MauricioFranco;BorisSotomayorTheTopologyofCommunicatingAcrossCitiesofIncreasingSizes,ortheComplexTaskof“ReachingOut”inLarger

Citiesarequintessentiallycomplexandadeeperunderstandingofurbandynamicswillgreatlybenefitfromrecognizingthesharedpropertiestheyhavewithabroaderclassofsystems.Particularly,withcomplexadaptivesystemswhosharecoupledhuman-environmentfeaturesorganizedaroundanecologyofsocial,economic,andstructuralcomponents.Insuchcontext,citieshavealsobeencomparedtosocialreactorsconstrainedbythecommunicationandcoordinationpossibilitiesofferedanurbanenvironmentthathasonlygrownsincetheadventofindustrialage.WehereattempttoprovideafirstdescriptionofhumaninteractionsintheurbanenvironmentusingCallDetailedRecords(CDR)ofthemajormobilephonecommunicationnetworkoperatorinChile.Webuildcommunicationnetworksfor145Chileancitiestodescribeandcharacterizethecommunicationbehaviorofurbandwellers.Wecenterouranalysisinobservedindicatorsofsocialactivity,suchasthenumberofcontacts,numberofcallsandtotalcommunicationtimeineachcityandevaluatetheirscalingrelationshipwiththenumberofmobileassignedtoeachcityasanapproximationofcitysize.Weattempttounveildifferencesincellphoneusagepatternsamongleisureandworkingtimesbyseparatingoutanalysisintotwoperiods:workingtime(08:00-13:00&14:00-20:00hrs.)andnighthours(20:00-08:00hrs&13:00-14:00hrs.).Surprisingly,thetopologicaldifferencesofnetworkbetweenthetwoperiodsaremarginal.Interestingly,thevaluesofscalingexponentscloselymatchrecentexplanationsproposedbyBettencourt

(2013).Thetopologiesofcellphonenetworksusageamongcitiesofincreasingsizesareslightlyassortative,albeitassertiveness’decreaseswithsize.Additionally,theyshowsmallaveragepathlengthrelativetotheirsizes,atypicalfeatureofsmall-worldnetworks.However,theydecreaseinsteadofgrowingwhensizeistakenintoaccount,unlikeothercomplexnetworks.Differenttransitivityindicesshowmixedresults.AverageWatts-Strogatzclusteringcoefficientincreasesinlargercitiesmuchlargerthanexpectedbypurechanceasithasbeenshowninothersocialnetworks.Ontheotherhand,thefactthatclassictransitivityindexdecreasesseemtoexhibitaregimechangewithadecreasingrelationwithsizeandanunexpectedgrowthinlargercities.Bothtransitivityindices,asawhole,coulddescribeamongthosewhoaremakingnewinteractionsasthecitygrows.Alltheseresultsindicatethatwhiletightlyknithumancommunitiesseemtolosecohesionastheygrow,suchcommunitypropertiesmayprogressivelydisappearamongthethreetofourlargesturbancentersinChilewherethecoordinationofcomplexfunctionsrequireseachcitydwellertoreachouttoalargernetworkofpeopleandspeakforlongerperiodsoftimeascomparedtosmallercities.

5. MarinaToger;ItzhakBenenson;SaritWeisburdThedeterrenceeffectrevisited:spatialanalysisoftheimpactofpolicepresenceonprobabilityofcrime.

Withgrowingurbanisationaswellasupsurgeinhumanpopulation,increasingattentionisbeingpaidtoso-calledsmartpolicing,wherelimitedresourcesaredeployedinanoptimisedfashiontoachievemaximumvisibility,shortestresponsetimesand,finally,minimisethecrime.Attheheartofsmartpolicing,thereisaneedtoanalysecorrelationbetweenspatiotemporalpatternsofthepresenceofpolice,andtheoccurrencesofcrime.Whilesomeliteratureshowedthatrelocatingpoliceforcesdidnotsignificantlyimpactcrimeoccurrence,otherresearchersshowedthatconcentratedpolicepresencedecreasesprobabilityofcrimeintheseareas.Recentlyavailablehigh-resolutionspatialdataonpolicepresenceandemergencycallstopoliceenablerevisitingthisquestionusingthenewbig-dataanalysistechniques.Inthisproject,weanalysedetailedGPSrecordsofpolicevehiclestogetherwithcrimelocationstime-stampedusing911callsrecordsinDallas,TexasUSA.Wedevelopastatisticalmodelofspatiotemporaldependencebetweenthepolicevehiclesandemergencycallsanddetectbothspatialandtemporalthresholdsoverwhichpresenceoflawenforcementaffectstheprobabilityofmisconduct.Thecriticalissueofsignificanceoftherevealeddependencyisstudiedwiththehelpofstatisticalsimulation,basedontherandomiseddatasets:Voidofanyinteractioneffectbetweenpoliceandcrimeandseveralkindsofpossibledeterrence.Inthiswayweinvestigatethedependencyofthefrequencyofcrime,bythetypesoftheincidentssuchasviolentcrimes,burglaries,publicdisturbances,etc.Ourapproachcapturesthedatainfinegrainwithexacttimeandlocationinordertodetecteffectsthatarelostinaggregation.Insteadofgroupingincidentsbyarea,whichcloaksdifferentialeffectsinspecificplaceswithinthatunit,theaggregationofpolice-crimeinteractionsbasedonrecurrentsequentialpatternsandeffectspaintsanuancedpictureofthecompleximpactsandspecificpossiblestrategiesforoptimisations.

8A-PoliticsandDemographics

1. CelioSierra-PaychaHowNationalUrbanSystemsshapeInternationalMigrationSystems:thecaseofColombia

Thankstonationalstatisticaldatabasedoncensusesandacenturylongtraditionofresearches(Berry1964,Pred1977,Pumainetal.2015),thestructuresofnationalurbansystemsarewellknown.Onthecontrary,thetopicofinternationalmigrationfields(thedistributionofapopulationborninaspecificcountryacrosstheworldasdefinedbyHägerstrandin1957)isbarelystudied.Onereasonforthisdiscrepancyisthelackofhomogenousstatisticaldatacoveringtheentirepopulation.Exceptforsomecountrieswhosepopulationregisterscanserveassupportforsuchanalyses,veryfewpapersstudythespatialdistributionofinternationalmigration.

WewanttousetheColombiancasetoopenthisresearchfield.Ontheonehand,Colombiaoffersaveryinterestingstudycasesinceitsnationalurbansystemdevelopedina“quadricephalic”wayaroundfourcitiesthatconcentratemorethanamillionofinhabitantsineachcitysincethenineties:Bogota,Medellin,Cali,Barranquilla.OnlyinthepasttwentyyearsdidBogota,thecapital,growfasterthantheotherthreecities.Ontheotherhand,Colombianinternationalmigrationischaracterizedbyanimportantout-migrationdistributedinthreemajorcountries:Venezuelasincethesixties,theUnitedStatessincetheseventiesandSpainsincethenineties.Nowadays,thethreenamedcountriesstillconcentratemorethan300000peopleborninColombiawhereasthecountriesthatfollowinthisranking(Ecuador,GreatBritain,France,Italy)eachcompriselessthan50000Colombianresidents.Onthegroundsofamulti-sitedquantitativedatabaseusingthe2005ColombiancensusandSpanishdemographicsurveys,thispaperquestionstheshapeoftheColombianinternationalmigrationfieldfromtheperspectiveoftheinternalorganizationoftheurbansystem.WhichpartsoftheircountryofbirthdoColombianslivinginSpain,VenezuelaandtheU.S.comefrom?Ourresultsshowthateachoftheinternationalout-migrationdestinationmatcheswithaspecificareaofColombiathatplayedanimportantroleinstructuringthegrowthofthelocalbigcity:forexamplemigrantsmovingtoSpaincomemorespecificallyfromCaliandsurroundings,ColombianslivinginVenezuela,morespecificallyfromBarranquillaandnearbyarea,ColombianresidentsintheU.SmostlycomefromMedellinandnearbycitieswhereasthedestinationsofpeopleborninBogotaarehighlydiversified.Tounderstandhowmigrantsreproducedonaninternationalscalethespecificpatternsoftheirinternaldistribution,themacroanalyticalmodelssuchasthepushandpullfactorstheoryandthegravitymodelsarenotsufficient.Theanalysisonameso-levelrevealsthatpersonalnetworksandfamilyplayamuchmoredecisiverolethanmacro-economicfactorsordistance.

2. NickHood;MylesGould;JocelynEvans;PaulNormanIsyourlocalareasusceptibletotheradicalright?DifferentiationUKIPsupportusinganareabasedclassificationofelectoralwardsinEngland

AsapartyperceivedtobepartoftheRadicalRightpopulistpartyfamilyinEurope(JohnandMargetts,2009;FordandGoodwin,2014;Clarkeetal,2016),considerableattentionhasbeengiventothespecificdriversofelectoralsuccessfortheUnitedKingdomIndependenceParty(UKIP).Polling26.6percentofthevoteinthe2014EuropeanElection,and12.6percentinthe2015GeneralElection,thepartyhasbeendescribedasappealingto‘leftbehindBritain’(GoodwinandMilazzo,2015).Thisgrouphasgenerallybeenseenascomprisingdualelectorates,combiningdiscontentedEuroscepticConservativesinthesouthofthecountrywithworking-classvotersdisenchantedwiththeLabourPartyinthenorth.RecentelectionsresultshavealsoshownaprevalenceofsupportalongmuchoftheEastcoastofthecountry.MuchoftherecentliteraturehasfocusedonthepartyoriginsofsupportforUKIP,anddebateditslikelypoolsofsupport(EvansandMellon,2016;FordandGoodwin,2016;MellonandEvans,2016).However,muchofthisliteraturefocusesonindividualleveleffectsofdifferentvariablesonUKIPsupportoruseslargespatialunits(constituency/localauthority)andinouropinionundervaluestheecologicalcompositionoflocalareas(inthiscaseelectoralward).Thispaperfocusesonthesocio-demographicconditionsinEnglishwardsatthe2015localelectionsandusesarangeofindicatorsfromthe2011Censusandotherpublicdatasourcestocharacterisethesocio-economiccontextofwardsand,usingthedatareductiontechniqueofk-meansclustering,classifiesareasbyantecedentsofsupportforthemajorpoliticalpartiesinEngland.ThisfinerspatialanalysisallowsustounderstandhowdifferinglocalcontextsmaydriveUKIPsupport(asafunctionofwardclasses)andwe

subsequentlyrankthefavourabilityofelectoralwardstoUKIPsupport.Wethenlookatthevotingprofilesoftheseclasses,forallparties,butfocusspecificallyonUKIPshareofthevotetoidentifyhowthepartyperformsacrosswardswithdifferentecologicalcharacteristics.Lastly,weusethe2017GeneralElectionconstituenciesinwhichUKIPfieldedcandidatestoconsidertheextenttowhichthepartieschoseareas,inanelectionbroadlyexpectedtoseeacollapseofitssupport,closetothefavourableclassesidentifiedbyourmodel.TheclassificationfindsspecificspatialcontextsinwhichUKIPstruggledforvotesatits2015peakbutfindsamorecomplexpictureofthetypesofareainwhichthepartyachievedgreaterlevelsofsupport.Moreover,theclassificationisreassuringinitsconfirmationoftheeffectsofanumberofvariablesonUKIPsupport(particularlylevelofeducation)whilstitprovidesadditionalinsightsintotheeffectofothervariablesonradicalrightsupport(notablyage)whicharefoundtobeheavilyinfluencedbyspecificspatialcontextsattheelectoralwardlevelinEngland.3. MylesGould;NicholasHood;JocelynEvans;PaulNormanIntroducinganEuropeancomparativemultilevelstudyof'radicalright'support

Electoralsupportforthe'radicalright'partiessuchastheFrenchFN,theGermanAfD,theDutchPVVandUKIPinBritainhavereceivedconsiderableattentioninpoliticalscience,particularlyforthoseelectionswheretheyhavebeensuccessful(e.g.forUKIPpriortothe2016ReferendumonleavingtheEU).Previousresearchhasshownthattheprincipalmotivationsforsupportingsuchradicalrightpartieshashistoricallybeenrelatedtoanti-immigrantsentimentsandanti-Muslimattitudes.Suchfactorsbecomeimportantinpeoplesvotingdecisionsinlocalandnationalcontexts.Comparativeresearchonradicalrightpartiesinpoliticalsciencehasfocusedmainlyonnational-levelindicatorsandindividualmotivationswhenexplainingvariationsinsupportfortheseparties.Whilstsomeresearchhasusedlocaldatatolookathowsupportvarieswithinindividualcountries,therehasbeenanotableabsenceofcomparativeresearchthatformallyandsimultaneouslyexamines:theregionaland'neighborhood'contexts,andindividualdeterminantsofradicalrightsupport.TheinterdisciplinarySCoRE(Sub-NationalContextandRadicalRightSupportinEurope)projectisbuildingamultilevelanalysisforfourcountries:France,Germany,theNetherlandsandtheUK.Inthispaperwereportonthecollationofavailablesecondarypolitical,demographicandsocio-economicdatafromofficialsourcesattheregionalandlocallevels.Wesummariesanumberofchallengesthathavehadtobeovercomeinharvestingandcombiningacommonsetofharmonizedindicatorsforthedifferentcountrieswithdifferentapproachesandtraditionsforthecollectionanddisseminationofofficialstatistics.Wealsoconsiderthedecisionstakeninselectingacommonfinestspatialscaleanddefinitionofneighborhoodunitsformakingcross-nationalcomparisons.Wewillalsoreportonthesecondphaseoftheprojectwhichhasinvolvedthedesignandadministrationofastandardizedsocialsurveyofpartysupportandpublicopinionacrossthefourcountries.Thishasjustresultedinthecollectionofanup-to-datesetofdemographic,attitudinalandbehavioralinformationforindividual'svotersacrossthefourcountriesandhasbeentimedtofittheirelectioncycles.FortheUK,wewillprovidesomeinitialdescriptiveanalysisofthesurveyconductedjustafterthe2017'snapgeneralelection'.Wewillalsobrieflyoutlinefutureworkinvolvingmultilevelcomparativemodellingofradicalrightsupport,toidentifytherelativeroleofcontextualandindividual/compositionaldeterminantsoffactorsofpartysupport.

4. StephenClark;MichelleMorris;NikLomax

EstimatingtheUKsreferendumonEUmembershipusinge-petitiondataandmachinelearningalgorithms

TheUnitedKingdom’s(UK)2016referendumonmembershipoftheEuropeanUnion(EU)isperhapsoneofthemostimportantelectoraloutcomesintheUKforoverhalfacentury,withtheimpactbeingfeltbothdomesticallyandinternationally.Thepoliticalsentimentembodiedinthisoutcomehaschallengedpoliticians,mediacommentatorsandacademicsalike.InparticularmanybelievethatitisimportantthatlocalMembersofParliamenttakeaccountofthissentimentindebatesconcernedwiththeUKexitingfromtheEU(BREXIT).UnfortunatelyformanyareasoftheUKthisoutcomeisnotknownforWestminsterParliamentaryConstituencies(WPC),ratheritisknownforthecoarsergeographyoflocalauthorities/countingareas(CA).Thisstudycomplementsexistingresearchthatattemptstoestimatetheleave/remainoutcomefortheseWPCs.Theresearchusesnovele-petitiondatacollectedbytheUKParliament’sPetitionsCommitteeandmachinelearningalgorithmstopredicttheoutcomeforeachWPC.Theperformanceofeachalgorithmiscomparedandevaluatedagainstbothknownoutcomesandalternativeestimates.Thealgorithmthatisseentoperformbestwithtestingdataandproduceresultswhichcorrelatewelltootherestimatesisaruleandinstance-basedregressionmodellingalgorithm.

8B-Landuse

1. RemiLemoy;GeoffreyCarusoEvidencefortheHomotheticScalingofUrbanForms

InthisworkweanalysetheprofileoflanduseandpopulationdensitywithrespecttothedistancetothecitycentreforlargeEuropeancities.Inadditiontoprovidingtheradialpopulationdensityandsoil-sealingprofilesforalargesetofcities,wedemonstratearemarkableconstancyoftheprofilesacrosscitysize.Ouranalysiscombinesthe5mresolutionGMES/CopernicusUrbanAtlas2006landusedatabasefor300largeEuropeancities(populationgreaterthan100.000inhabitants)withthe1kmresolutionGeostatpopulationgrid.PopulationisallocatedproportionallytosurfaceandweightedbysoilsealinganddensityclassesoftheUrbanAtlas.Weanalysetheprofileofeachartificiallanduseandpopulationwithdistancetothetownhall.Inlinewithearlierliterature,weconfirmthestrongmonocentricityofEuropeancitiesandthenegativeexponentialcurveforpopulationdensity.Moreover,wefindthatlanduseprofiles,inparticulartheshareofhousingandroads,scalealongthetwohorizontaldimensionswiththesquarerootofcitypopulation,whilepopulationdensityprofilesscaleinthreedimensionswiththecuberootofcitypopulation.Inshort,Europeancitiesofvaryingsizesarehomotheticintermsoflanduseandpopulationdensity,andthesetwophenomenascalerespectivelyasasurfaceandavolume.Whileearlierliteraturedocumentedthescalingofaveragedensities(totalsurfaceandpopulation)withcitysize,wedocumentthescalingofthewholeradialdistanceprofilewithcitysize,thusliaisingintra-urbanradialanalysisandsystemsofcities.InadditiontoprovidinganewempiricalviewoflargeEuropeancities,ourscalingoffersasetofpracticalandcoherentdefinitionsofacity,independentofitspopulation,fromwhichwecanre-questionurbanscalinglawsandZipf’slawforcities.Inparticular,wefindthatthedistributionofEuropeancities’sizesdoesnotfollowZipf’slaw,whichcorrespondstoanexponent−1forthecounter-cumulativedistributionofsizes.Wefindratheranexponentof−3/2,whichcorrespondstooldersystemsofcities.2. AlexandreOrnonAspatialmultiscalarmodelisationofurbanisationintheSouth-EastofFrance

Spatialcontext:OverthelastdecadestheregionProvenceAlpesCôted’AzurhasbeensubjectedtothemostimportantrateofurbangrowthinFranceonaccountofthemetropolitanpolarisationofMarseilleandNice.Asaccessibilityincreasesatamacro-scale,thisentailsalengtheningofwork-homefluxes,andaccordingly,agrowthofsub-urbanisation.ThesemechanismsmainlyconcernthedepartmentofVar,locatedbetweenMarseilleandNice.Purpose:Theintentofthisresearchistograspurbangrowthfactorsviaspatialmodeling.Theaimistoexplorethesemechanismsinactionbytestingsimulationsoncellularautomata(Voiron-Canicioandal.2014)andfinallytosubmitatdifferentscalesaprospectiveinsightfor2030.Themainissueistodesignamethodforinterlockingdifferentmodelsproducedatdifferentscales.Thismodelingproposalisbuiltonthearticulationoffourdifferentsub-modelsstructuredbyscalarinteractions.Combiningthemallowstotestandtocomprehendwithmoreaccuracythedifferencesbetweeneachscalarprocessofurbangrowth.Method:Thefirstsub-modelsimulatesurbangrowthatamacro-scaleinsidetheregionProvenceAlpesCôted’Azur.Theresultingsimulationsenabletopinpointthemainchangingareas(i.e.theVar)inordertofocusonanewmodelextentforasecondphase.Thesecondmodelincludesmoreparameterssuchasanindicatorofurbanpressureandmorphologicalpotentialsofdevelopment.Aftersimulatingurbandevelopmentfor2030atahigherresolution,themodelextentisredefinedaccordingly.Regardingthelocalscale,themodelingrulesareidenticalexceptforanextrainputofaprotectedarealayeraswellasamorelanduse-orientedapproach.Thescopeappliestothecity-countyandaimsatsimulatingresidentialbuildingdevelopment.Finally,theresultsfromthelocalscalemodelpermitthedeterminationofthelastsub-model’sextent,atamicro-scale(i.e.cityscale).Centeredonacity,therulesandtheurbandevelopmentprocessesarebasedoncityplanningschemes.Foreachstepofmodeling,aspatialanalysisofspatialtrendsisundertakenbeforetheirtranslationsintomodelingrules.Thecalibrationiscarriedoutbyusinggeostatistics(DauphinéandVoiron-Canicio1988)andmathematicalmorphology(Voiron-Canicio1995).Inathirdphase,athousandsimulationsareperformedonthemodelbykeepingthesameparameterstoobtainthemainchangingzones.Conclusion:Withthismodelingapproach,mechanismsoftheurbangrowtharedistinguishedontheonehandbythescale/resolutionandontheotherhandbytheincreasingofcomplexityofthesub-modelrules.Asaresult,thelocationofthemainchangingspacesismoreandmoreaccurate,zoominginfromquarter-scaledareastobuildings.References:Dauphiné,André,etChristineVoiron-Canicio.1988.Variogrammesetstructuresspatiales.Reclusmodesd’emploi12.Reclus.Voiron-Canicio,Christine.1995.Analysespatialeetanalysed’imagesparlamorphologiemathématique.CollectionEspacesmodesd’emploi.GIPReclus.Voiron-Canicio,Christine,FabriceDecoupigny,SophieLiziard,AlexandreOrnon,DelphineRoussel,etRomainSery.2014.«PLUMES :PressionLittoraleUrbaine :Modélisation,Environnement,Simulation».OHMLittoralMediterranéen,LabexDRIIHM.

3. EmilieLerond;OlivierKlein;Jean-PhilippeAntoniDatatransformationforland-use/transportinteractionsexploration

IntroductionModelsingeographyhavebeendevelopedwiththeimprovementofhardwareandsoftwares.Theseallowthecomputationofmultidimensional,disaggregatedandvoluminousdataasmobilitygeosimulationresults,accordinglyanimprovementformobilitybehavioranalysis.Tomakethiskindofanalysisbetter,disaggregateddatalikeagent-basedandmobilitygeosimulationaremoreandmoreused.Neverthelesstheirquantityandstructurescanbehardtohandleandexploringthesedataisrequiredtounderstandmobilityanditsinteractionswithland-use.Thispaperfocusesondatastructurationtocomparespatialdistributions.DataThedatasetisgeneratedbyMobiSim,anagent-basedmobilitysimulationmodel(Antonietal.,2016).Itdescribesthedailymobilityof180,000individualsintheGreatBesançon(France)during24hours,i.e15.5billionofspatio-temporalpositions.MethodsSpatialdatasetsareclassicallyconsideredaspoints(mobilitytraces),lines(transportationnetwork)orpolygons(buldings)inGeographicalInformationSystems.Inordertomakecomparablethesethreedifferenttypesofspatialdata,therearetransformed.First,dataareaggregatedinseparategridsofsameformandsameresolution:pointsandpolygonsarecountedinaggregationunits,linearesnappedtounitcentroidsandnumberoflinesarecounted.Soeachtypeofspatialdataisstructuredinseparate,butequivalent,grids.Then,elementsingridsaretransformedinscatteredpointspatternaccordingtocentroidsofaggregationunits.Analysisofthesescatteredpointspatternsallowscomparisonsonspatialdistributionsandtopinpointlinksandinteractionswithinelements.Finally,fromscatteredpointspatterns,twokindsofanalysisareprocessed.Analysisofeachdatainaseparateway,tounderstanditsorganization:e.g.istherealinkbetweennumberofmovementanddistancetourbancenterofstudyarea?Secondkindofanalysisbetweendataallowstounderstandtheirinteractions:e.g.cansomeinteractionsbetweenmobilityandland-useberevealedandhighlighted,instatisticalandvisualways?ConclusionThismethodconsistsinafirststeptoexploremassivemobilitydataset,duetodataspatialtransformation.Itmakesdatacomparableandallowsastatisticalandvisualintegrationofdatasetsasland-useandmobility.Thisintegrationenablestonewanalysistoolsandnewhypothesesonland-useandmobilityinteractions.

4. NikitaA.SinitsynCellularautomataforsuburbanizationmodelling:thecaseofBelgorod

Belgorodisnotabigregionalcentre(253,000)inthesouthofRussia.Thelocalgovernorhasprovidedeffectivemigrationpolicysince1992.BelgorodisoneofthemostappealingcitiestoratherwealthypensionersfromnorthernRussianregions.Thegrowthofdemandledtohighhousingpricesinmulti-storeybuildings.Sotheshareofone-storeyedbuildingsinannualconstructionwithinBelgorodagglomerationincreasedfrom12%(1992)to77%(2016).UniquecircumstancesmakeBelgorodregionthefirstagglomerationwithlargesuburbanareainthecountry.ThiscaseisreallyagreatchancetounderstandthemainpatternsofRussiansuburbanization.SuburbanizationisamongthemostprobablevariantsofhousingmarketdevelopmentinRussia–thereisnolackofland.Theresultofsettlementsystemshrinkingisconcentrationofpopulationinthebiggest

agglomerationswithpopulationover500,000.Onceuponatime,one-storeyedhousewillbecheaper,thanthesameflatinmulti-storeybuilding.Whenthetimeisdue,themodelwillhelpurbanplannerstopredictspatialdevelopmentofsuburbanizationinRussianagglomerations.Cellularautomatasimulatesspatialdevelopmentofsuburbanareasfrom1992to2014.Eachiterationconsistsofthreesteps:сalculationoftransitionpotential,cuttingfixedcellsfrompotentialrasterandselectionofnewgenerationofcells.Sixfactorsofsuburbanappearanceweredefined.Fourquantitativefactorsarecharacterizedbythedistancesfromsomeobjects:citycentre,mainroads,sewagetreatmentplantsandmainsewerpipes,multi-storybuildingsandprevioussuburbangenerations.Twoqualitativefactorscharacterizethecontentofcertainplace:thepollutionareasofplantswithhighsmokestacksandakindofvacantcells–applegardensarelessattractivethanemptyfields.Theresultofmodellinglookslikerealsuburbanareasateveryiteration,notonlyatlast.Themainadvantagesofthismodelaretheabsenceofcalibrationandsimplicity.