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ARTIFICIALINTELLIGENCEINURBAN PLANNINGANDDESIGN
ARTIFICIAL INTELLIGENCE INURBANPLANNING ANDDESIGN
Technologies,Implementation, andImpacts
Editedby
IMDAT AS
FacultyofArchitecture,IstanbulTechnicalUniversity,Istanbul,Turkey
PRITHWISH BASU
NetworkandCyberTechnologies,RaytheonBBN,Cambridge,MA,UnitedStates
PRATAP TALWAR ThompsonDesignGroup,Boston,MA,UnitedStates
Elsevier
Radarweg29,POBox211,1000AEAmsterdam,Netherlands
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ThisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythePublisher (otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperiencebroadenour understanding,changesinresearchmethods,professionalpractices,ormedicaltreatmentmaybecome necessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingandusing anyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuchinformationormethods theyshouldbemindfuloftheirownsafetyandthesafetyofothers,includingpartiesforwhomtheyhavea professionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assumeanyliability foranyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability,negligenceorotherwise,or fromanyuseoroperationofanymethods,products,instructions,orideascontainedinthematerialherein.
ISBN:978-0-12-823941-4
ForinformationonallElsevierpublications visitourwebsiteat https://www.elsevier.com/books-and-journals
Publisher: JosephP.Hayton
AcquisitionsEditor: KathrynEryilmaz
EditorialProjectManager: SaraValentino
ProductionProjectManager: MariaBernard
CoverDesigner: MarkRogers
TypesetbySTRAIVE,India
Contents
Contributorsix
Prefacexi
Acknowledgmentsxv
1
Theoreticalfoundations
1.AnewagendaforAI-basedurban designandplanning
MarkBurry
EmbracingAItorecalibratethemasterplan3
Thefutureofwork:AIandthedisruption ofplanningandurbandesignpractice4
Unpackingartificialintelligenceforplannersand urbandesigners5
FundamentalAIcomponentsfordisrupting planningandurbandesignpractice6
AIenhancingtraditionalplanningandurban designservicesworkflow15
AIandthechallengestoexpertise18
Acknowledgment19
References19
2.AIandthelimitsofhumancreativity inurbanplanninganddesign
NeilLeach
Introduction21
Architecturallessons25
Thelimitsofhumancreativity32
References36
3.Complexityscienceforurbansolutions
AnjanaaDeviSinthalapadiSrikanth,BennyChinWeiChien, RolandBouffanais,andThomasSchroepfer
Introduction39
Artificialintelligence(AI)inthebuilt environment40
Complexityscienceandurbansystems42
Keyaspectsofspatialnetworkanalysis43
ComputationalsocialscienceanditsAI applications52
Summary55
Acknowledgments55
References55
2
AItoolsandtechniques
4.ClassesofAItools,techniques, andmethods
GeoffKimm
Introduction61
AworkingdefinitionofAIinurbanplanning anddesign62
Tools:Algorithmiccladesinurbanplanning anddesign64
Techniques:Amachine’s-eyeviewofthecity66
Methods:Asnapshotfromthepractitioner’s desktop71
Conclusions80
Acknowledgments81 References81
5.Urbanformanalysisthrough morphometryandmachinelearning
JinmoRhee
Urbanform—Abasicdefinition86
Urbanmorphometry88
Context-richurbananalysisandgenerationusing machinelearning89
Urbanmorphometrywithadvancedstatistics96 References99
6.AI-drivenBIMonthecloud
WanyuHe,JackieYongLeongShong,andChuyuWang
Introduction101
Background101
AI-drivenbuildinginformationmodel onthecloud105
Casestudies109
Conclusions116 References116
3
AIinurbanscaleresearch
7.Deeplearninginurbananalysis forhealth
DavidWilliamNewton
Introduction121
Urbanmorphologyandhealth123
Deeplearninginurbananalysisforhealth123
Applicationsofdiscriminativedeeplearning inurbanhealthanalysis124
Applicationsofgenerativedeeplearningforurban healthanalysis131
Challenges,opportunities,andnextsteps135 References137
8.Spatialdesignofenergyself-sufficient communities
MinaRahimian,LisaIulo,andJosePintoDuarte
Citiesandenergyresiliency139
Designingforenergyself-sufficienturban settlements141
Urbanformandenergyconsumptionin communities143
Interpretingtheblackbox153 Closure158
Acknowledgments160 References160
Furtherreading162
9.Theimageofthecitythroughtheeyes ofmachinereasoning
ElcinSari,CengizErbas,andImdatAs
Introduction163 Background164 Methods,tools,andtechniques166 Casestudy168 Conclusions176 Acknowledgment178 References178
10.Optimizingurbangridlayouts usingproximitymetrics
FernandoLima,NathanC.Brown,andJosePintoDuarte
Introduction181 Materialsandmethods183 Casestudy187 Results191 Discussion198 Acknowledgments199 References199
4 Casestudiesinurbandesign andplanning
11.Imageanalyticsforurbanplanning: ThecaseoftheBarcelona Superblock
AldoSollazzo
Theurgencyforanewurbanism203 Novelmethodsforimageanalytics207
Conclusions213
Acknowledgment214 References214
12.Complexityscience-basedspatial performanceanalysesofUNStudio/DP
Architects’SUTDCampusand WOHA’sKampungAdmiralty
AnjanaaDeviSinthalapadiSrikanth,BennyChinWeiChien, RolandBouffanais,andThomasSchroepfer
Introduction217
Analysesoftwoverticallyintegratedspatial networks218
Methodologyandresearchphases219
Conclusions242
Acknowledgment243 References243
13.Understandingurbanleisurewalking behavior:Correlationsbetween neighborhoodfeaturesandfitness trackingdata
€ OzgunBalaban
Introduction245 Leisurewalk247
Methodologyanddata248 Results252 Destinations255
Conclusionandfuturework258 References260
14.Spacemaker.Ai:UsingAIin developingurbanblockvariations
JeffreyLandes
Generativedesign263 ToolsatSpacemaker266 Casestudy269 Conclusion286
15.M€ obiusevolver:Competitive explorationofurbanmassingstrategies
PatrickJanssen,TungDoPhuongBui,andLikaiWang
Introduction293 Competitiveevolutionarydesignexploration295
Demonstration302
Discussion315
Conclusions318
Acknowledgment319 References319
16.Adaptivemasterplans:Flexible modulardesignstrategies
MartinBielik,ReinhardKoenig,andSvenSchneider
Introduction323
Background324 Methods324 Applications330
Conclusions335 Notes335 References336
17.SASAKI:Fillingthedesigngap—Urban impressionswithAI
ThiyagarajanAdiRaman,JustinKollar,andScottPenman
Introduction339
Background341
Identifyinga“goodenough”tool342 UsingGANstogenerateurban impressions345 Keytakeaways353 Towardasketchtoolprototype359 Conclusions361 References362
18.KPF:Aretrospectiveviewonurban planningAIfor2020
SnoweriaZhang,KateRingo,RichardChou,BrandonPachuca, EricPietraszkiewicz,andLucWilson
Preamble363 Crisesandinventions364 Evolutionoftools365 Conclusions379 References380
Index381
Contributors
ImdatAs TUBITAKInternationalFellow, IstanbulTechnicalUniversity,Istanbul,Turkey
€ Ozg € unBalaban ChairofDesignInformatics, FacultyofArchitectureandtheBuilt Environment,DelftUniversityofTechnology, Delft,TheNetherlands
MartinBielik Bauhaus-UniversityWeimar, ChairofComputerScienceinArchitecture andSpatialPlanning,Weimar,Germany
RolandBouffanais UniversityofOttawa, Ottawa,ON,Canada
NathanC.Brown DepartmentofArchitectural Engineering,CollegeofEngineering,ThePennsylvaniaStateUniversity,UniversityPark,PA, UnitedStates
TungDoPhuongBui Departmentof Architecture,NationalUniversityof Singapore,Singapore
MarkBurry SwinburneUniversityof Technology,Melbourne,VIC,Australia
BennyChinWeiChien SingaporeUniversityof TechnologyandDesign,Singapore,Singapore
RichardChou KohnPedersenFox(KPF), NewYork,NY,UnitedStates
JosePintoDuarte StuckemanSchoolofArchitectureandLandscapeArchitecture,College ofArtsandArchitecture,DepartmentofArchitecturalEngineering,SchoolofEngineering Design,TechnologyandProfessionalPrograms,CollegeofEngineering,ThePennsylvaniaStateUniversity,UniversityPark,PA, UnitedStates
CengizErbas TUBITAKInternationalFellow, HacettepeUniversity,Ankara,Turkey
WanyuHe FloridaInternationalUniversity,The CollegeofCommunication,Architecture+The Arts,Miami,FL,UnitedStates;ShenzhenXkool TechnologyCo.,Ltd.,Shenzhen,China
LisaIulo StuckemanSchoolofArchitecture andLandscapeArchitecture,CollegeofArts andArchitecture,DepartmentofArchitectural Engineering,SchoolofEngineeringDesign, TechnologyandProfessionalPrograms, CollegeofEngineering,ThePennsylvania StateUniversity,UniversityPark,PA, UnitedStates
PatrickJanssen DepartmentofArchitecture, NationalUniversityofSingapore,Singapore
GeoffKimm SwinburneUniversityof Technology,ResearchFellow,SmartCities ResearchInstitute,Melbourne,VIC,Australia
ReinhardKoenig AITAustrianInstituteof Technology,DigitalandResilientCities, Vienna,Austria;Bauhaus-UniversityWeimar, ProfessorshipforComputationalArchitecture, Weimar,Germany
JustinKollar DepartmentofUrbanStudiesand Planning,MassachusettsInstituteof Technology,MA,UnitedStates
JeffreyLandes AutodeskSpacemaker.ai, Cambridge,MA,UnitedStates
NeilLeach FloridaInternationalUniversity, Miami,FL,UnitedStates;TongjiUniversity, Shanghai,China;TheEuropeanGraduate School,Visp,Switzerland
FernandoLima DepartmentofArchitecture, StuckemanSchoolofArchitectureandLandscapeArchitecture,CollegeofArtsandArchitecture,ThePennsylvaniaStateUniversity, UniversityPark,PA,UnitedStates
DavidWilliamNewton CollegeofArchitecture, UniversityofNebraska-Lincoln,Lincoln,NE, UnitedStates
BrandonPachuca KohnPedersenFox(KPF), NewYork,NY,UnitedStates
ScottPenman Sasaki,Boston,MA,UnitedStates
Contributors
EricPietraszkiewicz KohnPedersenFox(KPF), NewYork,NY,UnitedStates
MinaRahimian StuckemanSchoolofArchitectureandLandscapeArchitecture,CollegeofArts andArchitecture,DepartmentofArchitectural Engineering,SchoolofEngineeringDesign, TechnologyandProfessionalPrograms,College ofEngineering,ThePennsylvaniaStateUniversity,UniversityPark,PA,UnitedStates
ThiyagarajanAdiRaman Sasaki,Boston,MA, UnitedStates
JinmoRhee CarnegieMellonUniversity, Codelab,SchoolofArchitecture,Pittsburgh, PA,UnitedStates
KateRingo KohnPedersenFox(KPF), NewYork,NY,UnitedStates
ElcinSari MiddleEastTechnicalUniversity, DepartmentofCityandRegionalPlanning, Ankara,Turkey
SvenSchneider Bauhaus-UniversityWeimar, ChairofComputerScienceinArchitecture andSpatialPlanning,Weimar,Germany
ThomasSchroepfer SingaporeUniversityof TechnologyandDesign,Singapore,Singapore
JackieYongLeongShong FloridaInternational University,TheCollegeofCommunication, Architecture+TheArts,Miami,FL,United States;ShenzhenXkoolTechnologyCo.,Ltd., Shenzhen,China
AldoSollazzo IAAC,Barcelona,Spain; Noumena,Barcelona,Spain
AnjanaaDeviSinthalapadiSrikanth Singapore UniversityofTechnologyandDesign, Singapore,Singapore
ChuyuWang ShenzhenXkoolTechnologyCo., Ltd.,Shenzhen,China
LikaiWang DepartmentofArchitecture, NationalUniversityofSingapore,Singapore
LucWilson KohnPedersenFox(KPF), NewYork,NY,UnitedStates
SnoweriaZhang KohnPedersenFox(KPF), NewYork,NY,UnitedStates
Preface
Urbanplanninganddesignisacomplex fieldofstudythatcombinesawidevariety ofdisciplines.Cityplanners,urbandesigners, architects,andlandscapearchitectswork togethertosolvedemandingurbanissues. Theyhavetostudythecityatarangeofscales fromzoninglawstotransportationandinfrastructurenetworks,tobuildings,streetfurniture,andlighting.Therangeofman-made urbaninterventioncanbeclusteredintothree mainlayers:firstly,houses,shops,factories, etc.,secondly,overgroundinfrastructure, e.g.,roadnetworks,railways,lightpolls, etc.,andfinally,undergroundtechnicalinfrastructure,e.g.,sewage,waterworks,heat,gas, electricity,etc.Engineerscontributetodevelopingandmaintainingthiscomplexgridof urbanconstructionandinfrastructure.Besidestheactorswhoworkwiththephysical tissueofthecity,othersstudyintangible forces.Economists,scientists,operationsresearchers,andsociologistsstudytheorganization,growth,anddeclineofcities—the flowofgoodsandservices,aspectsofmarket forces—andtheeffectsoftheurbanenvironmentsuchasclimatechangeonhumanbehavior—evolvingsocialvalues,human interactions,andtheexodusandmigration ofindividualsandgroups.
Thus,thefieldofurbandesignandplanningreliestraditionallyheavilyonlarge amountsofdata.Plannersusespreadsheets tocomeupwithspatialanalysis,evaluate projections,assumptions,andmakeestimatesandpredictions.Inthelatterhalfof the20thcentury,geographicinformation systems(GIS)toolswereintroducedtothe
fieldthatcouldintegrategeographicinformationwithlayersofdata,andtranslate themintotables,graphs,andmaps—inorder tomakeiteasiertogather,manage,andanalyzethem.Inparallel,groundbreakingdevelopmentsinartificialintelligence(AI) revolutionizedmanyfieldsofdata-drivenresearchincomputerscienceapplications.The familiarityofutilizingurbandatatodevelop urbandesignandplanningsolutionsnaturallylentitselftotheintroductionofAIto thefield.
Overthelastdecades,thefieldofAIwent throughthreewavesofdevelopment, transitioningfromsymbolicorrule-based AI,e.g.,expertsystems,geneticalgorithms, swarmintelligence,etc.,tostatisticalreasoning,e.g.,supportvectormachines,Bayesian reasoning,andartificialneuralnetworks,to ahybridofbothapproaches,e.g.,robotics. AIresearchbroadlyconsistsofsupervised andunsupervisedlearning,generativealgorithms,andreinforcementlearning.Supervisedlearningapproachestrainonlabeled dataandperformclassificationandpredictiontasks,whereasunsupervisedlearning trainsonunlabeleddataanddetectssignificantpatterns.Ontheotherhand,generative algorithmstrainortransformagenerative modeltocreateunprecedentedsamples. Andfinally,reinforcementlearninginteracts withstochasticenvironmentstogathertrainingdataandlearnsamodelthatmakes utility-optimizingdecisions.
Inthelastdecade,AIcapturedthepublic imaginationthankstodevelopmentsindeep learning—abranchofAIthatusesartificial
neuralnetworksthatlooselymimictheinner workingsofthehumanbrain.Adeepneural network(DNN)consistsoflayersofartificial neuronsthatarestackedontopofeachother. WhenaDNNistrainedwithenoughdata samples,itcandiscoverinternalrepresentationsofobjects,e.g.,thesystemcanbe trainedwithcatimagesandcanidentifycats innewimagesthatithasnotseenbefore. Deeplearningsystemsdiscoverlatentpatternsandrelationshipsinbigdatathatareoftennotapparenttohumans.Theyarebeing usedinavarietyofeverydayapplications, fromimage,voice,andvideorecognition systems,toself-drivingcars,languagetranslation,andonlinerecommendationsystems.
Inthisbook,weexplorethepromisinguse ofAIasitrelatestourbanplanninganddesign,illustratevarioustechnologiesthathave cometofruition,showcasetheirimplementationopportunitiesandchallenges,anddiscusstheirimpactonourbuiltenvironment. Organizedintofourparts:real-worldprojects,thisbookprovidesabroadoverview: (1)theoreticalandhistoricalbackground, (2)AI-basedtools,methods,andtechnologies,(3)AIinurbanplanninganddesign research,and(4)casestudiesofAIusedin real-worldprojects.Itcontains18illustrated contributionsexaminingAI-basedurban planninganddesignworkfromaroundthe world,includingtheUnitedStates,Europe, andAsia. Content
InPart1ofthebook,wesituateAIwithin theoverallcontextofurbanplanninganddesign,anddiscusshowAIapproachesdiffer andrelatetothetraditionaltoolkitofurban plannersanddesigners.Intheopening chapter,MarkBurryoffersanewagenda forAI-basedurbanplanninganddesign.
NealLeachsewsconnectionsbetweenthe seminalmomentofDeepMind’sAI-AlphaGo softwarebeatingaprofessionalhumanGo player,withthelimitsitexposesvis-a-vishumancreativityindevelopingurbanplanning anddesignsolutions.ThomasSchroepfer, laysoutthefundamentalsofcomplexityscience,toformthebasistodealwiththenature ofcomplexandmultiobjectiveproblemsets inalmostanyurbanchallenge.
InPart2,wepresentasurveyofstate-ofthe-artAI-basedtools,methods,andtechnologies.Wehighlighttheonesthathavebeen particularlyexploredinurbanplanning anddesign—withexamplesofwhereand howtheyhavebeenused.GeoffKimmgives aclassification,overview,andevaluationof variousnovelAItools.JinmoRheeoffers analternativeapproachtourbanformanalysisthroughmorphometryandmachine learning.WanyuHeetal.,ofXKool,awellknownChinesestart-up,demonstratesa noveltool,wherebuildinginformation modeling(BIM)isaugmentedwithadditionalAI-baseddatasetsonthecloud,and illustratesitsuseinvariousurbanscale projects.
InPart3,weofferaninsightintotheapplicationofAIinurbanscaleresearch.David Newtoncorrelateshealth-relatedproblems, e.g.,variousdiseases,withurbanformbyanalyzingsatelliteimagerythroughAI.Mina Rahimianetal.usedeeplearningtounearth theintertwinedrelationshipbetweenurban formandenergydemandinSanDiego, California,toachieveenergyself-sufficient communities.ElcinSarietal,illustratea novelmachinereasoning(MR)toolthatcan discoversignificanturbancomponents(ala KevinLynch)fromvariouscitylayoutsthat arehighlyrankedinsomequality-of-lifeindexes,andFernandoLimaetal.employed anevolutionarymultiobjectiveoptimization methoddrivenbyproximitymetricstogeneratenovelurbangridlayouts.
InPart4,weshowcasetheuseofAIinvariouscapacitiesinreal-worldurbandesign andplanningprojects.Westructuredthis partintothreecategories:ThefirstsetofcontributorspresentAIasan analysis toolwhere theygivenovelinsightsintothecomplex problemsetsofurbanchallenges.Thesecond setofcontributorsexamineAIasan assistant inurbandesignandplanningprocesses,e.g., inoptimizingvariousaspectsoflanduse, orientation,climate,etc.,andthelastsetof contributorsexploreAIasa generator thatdirectlyoffersactionableideaitems,e.g.,to generateunprecedentedurbanblockswithin aboundaryconditioninafreemanner.Inthe firstsection,AldoSolazzousesimageanalyticstocollectandanalyzedatainordertounderstandhowpeoplearelivingandusing urbanspacesintherenownedBarcelona Superblocks.ThomasSchropfershowsus howtousecomplexitysciencetoanalyze UNStudio&DPArchitect’sSingaporeUniversityofTechnologyandDesign(SUTD) campusandWOHA’sKampungAdmiralty Buildings;andOzgunBalabanpresentsa casestudywhereheanalyzesUrbanSingaporethroughfitnesstracking.Inthesecond section,JeffreyLandesofSpacemaker.ai demonstrateshowonecanuseAItooptimize3Durbanlayoutsforagivensitein Istanbul.PatrickJanssenetal.demonstrate theMobiusEvolverthatusesanevolutionaryalgorithmwhereoneurbanmassingsolutioncompetesagainstanother.Reinhard Koenigetal.usedigitaladaptivemaster
plans(AMPs)todevelopcitylayoutsin EthiopiaandSingapore.AMPsareparametricallycontrolledthree-dimensionalurban designmodelsthatcanautomaticallyadapt todifferentboundaryconditionsandplanningrequirements.Inthethirdsection, ThiyagarajanAdiRamanetal.ofSASAKI usegenerativeadversarialnetworks(GANs) todevelopnovelimpressionisticaerialimagery(alsoknownasurbanimpressions)to “sketchout”earlyideasforurbandesign. Finally,SnoweriaZhangetal.ofKohn, Petersen,andFox(KPF)showcasethreecase studiesthatofferanAI-basedspeculative narrativeframeworktoanalyzeanddevelop ideasinregardstoconventionalurbandesignworkflows.
AIresearchisconstantlyevolving,andits applicationsinurbandesignandplanning willsurelymatureovertime.Wehopethat thiscollectionofarticleswillofferanexciting andinformativeintroductiontothisfascinatingtopictoplannersanddesigners,architects,AIresearchers,andengineersinvolved inurbanplanninganddesignprojects.
Anyopinions,findings,andconclusions orrecommendationsexpressedinthis materialarethoseoftheauthor(s)anddo notnecessarilyreflecttheviewsofRaytheon BBN.Thisbookdoesnotcontaintechnology ortechnicaldatacontrolledundereither theU.S.InternationalTrafficinArms RegulationortheU.S.ExportAdministrationRegulations.
Acknowledgments
Weareindebtedtomanypeopleand institutionswhogenerouslygavetheirtime, offeredencouragement,andprovidedsupport.WearegratefultoIstanbulTechnical UniversityandRaytheonBBNfortheirsupportduringtheexecutionofthisbook,which allowedustoshapeourthinkingaboutthe specifictopicsofAIinurbanplanninganddesign.WealsothanktheScientificandTechnologicalResearchCouncilofTurkey (TUBITAK)forsupportingtheformationof theCityDevelopmentthroughDesignIntelligence(CIDDI)LabatIstanbulTechnicalUniversity,whichgaveusphysicalandhuman resourcestodealwithpressingissuesonthe “futurecity.”InMay2021,weorganizeda “FutureCitySummit”togetherwith
BIM4Turkey,andcontributorsofthisbook presentedtheirworkatthisconference.The worldwideparticipationinthisevent exceededourexpectations.Weareverygratefultoallthosewhoparticipated,andtoall thosewhoorganizedthisexcitingsummit withus,especiallyMehmetSakin,Furkan Filiz,thecofoundersofBIM4Turkey,and manyoftheircolleagues,whoworkeddiligentlytoputthiseventtogether.Wealso thankourpublishers,BrianRomer,Sara Valentino,andIveeIndelibleatElsevier,for theirpatienceandattentivehelpinthese difficulttimesofthepandemic,andlastly wethankAnaBatistaforgivingherfeedback andguidanceincompletingthisbook.
PART1
Theoreticalfoundations
AnewagendaforAI-basedurban designandplanning
MarkBurry
SwinburneUniversityofTechnology,Melbourne,VIC,Australia
EmbracingAItorecalibratethemasterplan
Lookingdownfromthewindowseatinacirclingjetatthe newworld’s suburbansprawl andthe developingworld’s informaldevelopments,howconfrontingthesetraffic-engineered tractsare,bereftofanysenseofjoyfulandinspiringurbanity.Theyareuncivilexpressionsof entirelylogicalorinformallyderivedarrangementsofhousing,schools,shops,parksand sportsfields,articulatedwithcongestedhighways.Theymayormaynotmeeteconomic,social,planningandbuildingcodes,standards,andconstraintsbuttheyprovidelittleelse— certainlynotculturalenrichment.Wherearetheboulevardsandthetownsquares?Where aretheretirees,younglovers,parentswithprams,anddog-walkerssupposedtostroll? Nottomentionchildrenknockingaboutwithafootball,theneighborhoodgossips,matchmakers,andbuskers!
Entirelynewcreativeavenuesbeckonforsteeringtowardsustainableurbantransformation,strivingtoboostsocialequityandcivicamenityalongtheroute.Byplacingthecitizenat thehelmofateamofmultidisciplinaryexpertsforcodesigningresponsestourbandensification,wecouldtackleakeychallenge:howwillfuturecitiesandprecinctsbedesigned with peopleratherthan forpeople?Aworthygoalistoenablethecommunityidentifymore acutelywhatisimportanttothemtowardestablishingviablealternativepathwaystoaccommodateinevitablechange.HowcanwesubsumetheNIMBY(NotInMyBackYard)mindset withamorepositiveYIMBYoutlook—wherethe“Y”ofYIMBYstandsfor“Yes!”(Lake, 1993)?Howcanthevariousnational,state,andlocalurbandevelopmentplanning,design, construction,andmanagementsectorscometogetherinanewkindofconversation?How mightwecooptAIalongwithemergingandmaturinginformationandcommunicationtechnologies(ICT)tocentralizethecitizenvoiceawayfrom consultation closerto activeparticipation?Whataretheoptimalsocialcreative,economic,andtechnologicalcriterianeededto
constructadigitalizationframeworkforinnovativeapproachestoplanningandurban design?Howmightsuchaplatformprovidethepeoplemostaffectedbytheexpert’sdecisionsthenecessaryagencytohelpformulatepositivetransformationstobackyards:denser precinctsyetgreateramenity?
Themasterplanand,bydefinition,itstwo-dimensionalphysicalcharacterization(“the plan”)isanotherkeyissue:howdowecooptrapidlydevelopingAItechnologiestomove fromconventionalplanningtomultidimensionalmasterplanning?Afundamentalparadigm shiftbeckonsplanningandurbandesignprofessionalstoengagefullywithtoday’ssmartsto anticipatefutureurbanexigenciesmoreeffectivelyandwithgreaterconfidencethanhasbeen possibletodate.HowmightAIrenderurbansustainabilitychallengesvisibleandintelligible tothecommunitiessubjectedtothepressuresofurbangrowth?CanAItoolsbecreatedto assistplanningandurbandesignprofessionalsincodesigningimprovedcivicamenityfor ourcitiesandregionswiththecitizens?Whatistheevolvingroleforplanningandurban designprofessionalsinthenewdigitalecosystemofdatacollection,dataanalytics,data visualization,artificial,augmentedreality(AR)andvirtualreality(VR),gamechangersto thewaywemasterplan,design,build,andmanageourcities?Whowillspecifyandbuild theurbanfuturesdigitalworkbenchtoserveasasharedresearchplatformandcollaborative designframework,capturingandcementingthepublic’scontributionstohelpshapesustainableurbangrowthandacceptingincreasedpopulationdensity?
Thefutureofwork:AIandthedisruptionofplanningandurbandesignpractice
Perpetuallyunfinishedbusiness
Ascitieschange,sodoessociety,convulsingwiththeimplementationofeverytechnologicalshiftasitoccurs,disruptingurbandesign,construction,andcitymanagementsystems andservicesinitswake(Burry,2020).In"TheRiseandFallofAmericanGrowth"socialeconomistRobertJGordonnotesthatsincethe1850scivilsocietyhasevolvedfromanalmost universalconditionofhavingnoaccesstoanyofthefacilitiesandmodernconveniences thatwetakeforgrantedtodayregardlessoftheirpositioninsociety(Gordon,2017).These includenothavingindoorsanitation,centralheatingandcooling,gettingaroundthecity otherthanbyfootorhorse,artificiallightingbeyondcandlesandlamps,electricitytopower domesticappliances,andnowidercommunicationwiththeworldatlargeotherthanbyletter.Nevertheless,thespeedofchangeanditsensuingcomplexityhavebeendemonstrablyat apacebeyondourhumancapabilitytostayastepahead:wehavebecomeusedtoadaptingto human-initiatedchangeandacceptingtheunintendednegativeconsequencesasinevitable, throughbeingunabletokeepupwiththepace.
Whatissodifferenttodaythanadecadeago,forinstance?
Alwayscomplexsystems,citieshavebecomecomplexadaptivesystems(Karakiewicz, 2020).Rapiddigitalizationduringthelastthreedecadeshasseenatechnologicalshiftpermeatingalmosteveryaspectofurbanlife.Thedigitalizationofthecityfabricandassociated systemsyieldsthe smartcity. Thesmartcitycombinesthe InternetofThings (IoT)—thedata collectingsensorsconnectedtoelectronicdevicesenabledby informationcommunicationtechnology (ICT)tohelpurbandesigners,constructioncompanies,andcitysystemsandservices managersdomoreforthecitizenwithless(MoraandDeakin,2019).Theglobalsmartcity
movementispredicatedonICTbeingthegreatestsustainabilitychangeagentattheexperts’ disposal.Butthesameblossomingpersonaltechnologyisalsoincreasinglyinthehandsofthe nonexperturbandwellers:the“smartcitizen,”withaccesstoradicallydifferentfacilitiesto influenceplanningandurbandesigndecision-making,suchasAustralia’s NationalUrbanResearch&DevelopmentPlatform (iHUB),anurbanobservatorydesignedtogazedeepintopossibleurbanfuturesanddescribedlaterinthechapter.
Aperennialproblemforalldesignersistheclientbeinglimitednaturallyto whattheyknow, withalimitedappetitetoexperimentonsomethingwhollyunfamiliartothem,howeverpotentiallyenrichingitmightbe.ExploitingthecombinationofArtificialIntelligencewithgames technologies,forexample,couldhelpendusersascertaintheirfundamentalneedsandresponsibilitiesforthemselves.IndeferencetoCarlFreyandMichaelHammer,AIcanbeharnessedto augmentextanthumancreativeability,andnotnecessarilysupplantit(Hammer,1990; Frey, 2019).Frey,however,inhis2019bookalsowarnsofare-emergenceoftheeponymous “technologytrap”thus:
Onereasoneconomicgrowthwasstagnantformillenniaisthattheworldwascaughtinatechnologytrap, inwhichlabor-replacingtechnologywasconsistentlyandvigorouslyresistedforfearofitsdestabilizingforce. CouldcountriesintheindustrialWestexperienceareturnofthetechnologytrapinthetwenty-firstcentury?
Proposalstotaxrobotsinordertoslowdownthepaceofautomationnowfeatureinthepublicdebateonboth sidesoftheAtlantic.AndunlikethesituationinthedaysoftheIndustrialRevolution,workersinthedevelopedworldtodayhavemorepoliticalpowerthantheLudditesdid.InAmerica,whereAndrewYang[2020 USAPresidentialhopeful]isalreadytappingintogrowinganxietyaboutautomation,anoverwhelmingmajoritynowfavorpoliciestorestrictit.Thedisruptiveforceoftechnology,Yangfears,couldcauseanotherwave ofLudditeuprisings:“Allyouneedisself-drivingcarstodestabilizesociety. [W]e’regoingtohaveamillion truckdriversoutofworkwhoare94percentmale,withanaveragelevelofeducationofhighschooloroneyear ofcollege.Thatoneinnovationwillbeenoughtocreateriotsinthestreet.Andwe’reabouttodothesamething toretailworkers,callcenterworkers,fast-foodworkers,insurancecompanies,accountingfirms.”
Unpackingartificialintelligenceforplannersandurbandesigners
Forplannersandurbandesignerswhoareawareofartificialintelligence’spolepositionin digitallydisruptingtheirpracticesbutwhollyunfamiliarwithitscomponents,thissection peeksunderthebonnet.Thisisalooseandinformaltaxonomygroupedincategoriesthatspan between“fundamental”and“littleimmediaterelevance.”Itisbynomeanscomprehensive, andnaturallytherewillbedisagreementaroundbothmyplainEnglishdefinitionsandassessmentofrelativeutility.Theintentionisnottoprovideatextbookapproachhere;the26AIcomponentsdescribedbelow,someofwhichareonlysubtlydifferentfromeachother,togetherlay outthefieldandthepotentialforAItoenhanceplanningandurbandesignpracticevery significantly—ifnotpreventedfromdoingsobytheNeo-LudditesFreywarnsusof(Fig.1.1). Intermsofthe futureofwork,itseemsthatweareatacrossroadswherethosepractitioners disinclinedtotakeuptheopportunitiesAIoffersthemcouldbeleftintheslowlane,as ThomasSiebelalertsusto:
Thecomingtwodecadeswillbringmoreinformationtechnologyinnovationthanthatofthepasthalfcentury.Theintersectionofartificialintelligenceandtheinternetofthingschangeseverything.Thisrepresentsan entirereplacementmarketforallenterpriseapplicationandconsumersoftware.Newbusinessmodelswill
1.AnewagendaforAI-basedurbandesignandplanning
FIG.1.1 126componentsofartificialintelligencethatcurrentlyimpactplanningandurbandesignpractice.They arerankedbetween“fundamental”(identifiedinred,grayinprintversion)clockwiseto“currentlyleastrelevant” (identifiedinpalegray). NoPermissionRequired.
emerge.Productsandservicesunimaginabletodaywillbeubiquitous.Newopportunitieswillabound.Butthe greatmajorityofcorporationsandinstitutionsthatfailtoseizethismomentwillbecomefootnotesinhistory. (Siebel,2019)
Inthefastlane—fastbecauseattheveryleastAIaffordsmorespeedandefficiency—will bepractitionersskilledinmakingAIcomponentsworkforthemand,regrettably,newly mintedsubprofessionalsenfranchisedthroughAItooffernewandmanifestlyvaluableservicessuchasurbananalytics,applicationsofsmarttechnology,andvisualization.Thedanger comesfromtheelevationofanindividualwithsplinterskillsgifted,say,incomputer graphicsbutinexperiencedinassayingthesignificanceofwhattheyareshowingandhow itisbeingpresented—thetalentedignoramus.ThebriefdiscussiononAIandexpertiseat theconclusionofthischapterwillconsiderthisoutlookalittlemorefully.
FundamentalAIcomponentsfordisruptingplanningandurbandesignpractice
1.Machinelearning(ML) usesartificialintelligencetoenablesystemstoself-learn,adapt, andimprovefromexperiencewithouthavingbeenexplicitlyprogrammedthroughprior experience.ComputerprogramswithMLaccessdataandsiftthroughitinordertolearn
1.Theoreticalfoundations
fromtheinsightsthatemergeintheprocess.Rawdata,observation,directexperience,or instructioninitiatesthelearningprocessenablingpatternstoemergefromthedata,thereby facilitatingmoreeffectivedecision-making.MLfacilitatescomputers’automaticlearning withouthumaninputenablingvastpoolsofdatatobeprocessedwithfargreaterspeed andprecision.
ClassicMLalgorithmsconsideredtextasasequenceofkeywordsmigratingtosemantic analysismimickinghumanabilitytocomprehendthetext’smeaning.
PlannersandurbandesignersdisposedtoproducingdesignsoftwareMLcouldtrackthe designers’decisionsandbegintoautomateroutinedecisions.Softwareproducedforclients couldbeenabledtoinnatelytracktheirchoicesandlearnfromthem,guidingtheclient towardanimprovedunderstandingofwhatisatstake.
2.NeuralNetworks(NNs) arepredicatedonmachinelearningandareatthecoreofdeep learningalgorithms(seeSection3below).Theirnameisderivedfromthestructureofthe humanbrain,inthesenseofmatchingtheconceptderivedfromourunderstandingofthe waythebrain’sneuronsintercommunicate.Artificialneuralnetworks(ANNs)consistof nodelayers,oneormorelayersbelow,andanoutputlayer.Eachnodeisconceivedasan artificialneuronconnectingtoaneighborwithanassociatedweightandthreshold.Only whenanodehasanoutputthatexceedsagiventhresholdvaluewillitactivateandtransmit datatothenextnetworklayerorelseitsimplyremainsinactiveanddoesnottransmit.
Generativeadversarialnetworks(GANs)areaclassofmachinelearninginwhichtwo competingNeuralNetworks—onethegeneratorandtheotherthediscriminatorarepitted againsteachotherinacooperativezero-sumgame:oneside’sgainistheother’sloss,from whichtolearn.Effectively,GANscreatetheirowntrainingdatasets.Thegenerator’srole istoartificiallyproduceoutputsthatcouldbemistakenforrealdata.Thediscriminator’sgoal istoidentifywhichoftheoutputsitreceiveshavebeencreatedartificially.TheGANlearnsto generatenewdatawiththesamestatisticsasthetrainingset.TrainingaGANusingarangeof photographs,forexample,newphotographscanbegeneratedthatlookatleastsuperficially authentictothehumaneyehavingabsorbedmanykeycharacteristicsextractedfromthe trainingset.
Neuralnetworksareusedextensivelytoproblemsolveandseekoptions.Theirextended useoffersdesignersandplannersunprecedentedopportunitiestominedataforthepurposes ofattainingdeeperinsightsandsituationalawarenessasinputstodecision-makinganddesign.ForthoseseekingtoaugmenthumancreativeskillsetswithAIGANsofferatreasuretroveofpossibilities.Asetofimagesfromarenownedarchitect,forinstancewillleadto creativebutdissimilatingapparentlyauthenticoutcomes.
3.Deeplearning isasubsetofmachinelearningbeinganeuralnetworkwithatleastthree layers.Neuralnetworksaredesignedtomimicbutnotimitatethehumanbrainintermsof learningfromthebigdatasourcesittrawlsthrough.Asingleneuralnetworkcansignificantly zeroinonandmakepredictions,whiletheadditionalsublayerscanassistwithoptimization, refinement,andultimatelyaccuracy.
Deeplearningisfundamentaltoanyartificialintelligenceapplicationaimedatimproving automationandautonomousanalyticalorpracticaltasksindependentofdirecthuman involvement.Thetechnologysupportingdeeplearningcanbefoundtodayinfamiliarproductsandservicesincludingdigitalassistantsandchatbots,financialfrauddetection,voicecontrolledpersonalassistantssuchasApple’sSiriandAmazon’sAlexis,anddriverless vehicles.
WithMachinelearningembeddedintodesignsoftwareaswellassoftwareintendedto helptheclientproduceabetter-informedbriefbydrawingouttheclient’slessobvious priorities,deeplearningoffersrichdividends.
4.Autonomoussystems canchangetheirbehaviorduringoperationinresponsetounanticipatedinputs.Inbuilt“intelligence”liesatthecoreofsuchsystems.Theirintegration enablesthesystemtoperceive,process,recall,learn,anddecideonappropriatecoursesof actionautonomously.Examplesincludecomputationthatcanimprovehumanperformance atgamessuchasChessandGo,facilitatingdronesandrobotstoadapttheirflightpathsand tasksaccordingtoinformationreceivedwhileinaction,self-drivingvehicles,andadvanced manufacturing.
Fortheurbandesignerandplanner,theopportunitiesarenotimmediatelyobvious. Designsoftwarecouldbegintolearnfromthedesigner’sdecisionsandmakesuggestions. Softwareforclientscouldtracktheirpredilectionsand,workinginconjunctionwiththe designer’sinbuiltconstraints,beguidedtowardtheiroptimaloption.
5.Patternrecognition comesfromcomputeralgorithmsusingmachinelearningtodetect patternsotherwiseinvisiblewithindatasets.Inrepresentingpatternsasknowledgeorstatisticalinformation,thedatacanbeclassified.
Patternrecognitionsystemsaretrainedusinglabeledtrainingdata.Lookingfor unknown knowns isachievedfromlabelsattachedtospecificinputvaluesleadingtoapattern-based output.Withoutlabeleddatabeingavailable, unknownunknowns aresoughtandmoresophisticatedcomputeralgorithmsaredeployed,therebytakingtheartbeyondthatwhichispracticallypossibleusingthehumanbrainunaided.
AHolyGrailfordesignersandplannersisaccessingtheunknownunknownsthatpotentiallyleadtodifferentsetsofdecisionsandultimatelyoutcomesthanwouldbemadebased ontraditionaldataanalysis.PatternRecognitionhasthepotentialtorevealtheseunknowns, butdesignersneverthelessrequirenewskillstoseevalueinterritoryunfamiliartothem.
6.Simulationmodeling enablesresearchthatrequiresavirtualenvironmenttosimulate physicalsystemsinoperationfromwhichusefulinsightscanbedrawn.SimulationModeling typicallylooksatsystemsinoperationsuchaspopulationdynamics,airports,cargofleets, andtrafficsystems.
Simulationmodelingisaprototypingenvironmentwherechangestoasystemcanbesafely testedandassessed,idealformulticriteriainputs,decisionsupport,andriskmitigation.
Thethreeprincipalframeworkstosimulationmodelingarediscreteeventsimulation (DES),systemdynamics(SD),andagent-basedmodeling(ABM).
Simulationmodelingisfundamentalandinprideofplaceforbothurbandesignersand plannerswhenenhancedbyvariouscategoriesofAI.AlongwithARandVRleapsinfunctionality,theopportunitiestosimulateandtestfuturescenariosareanextraordinaryasset.At thetimeofthewriting,“digitaltwins”arecenterstage;themoretheycanbeenrichedthrough AI,thegreatercapabilitywewillhavetobemoreaccurateinpredictingthefuture,andplanninganddesigningabetterone.AI-enhancedsimulationwillhelpplannersandurbandesignersanticipateandavoidwhatmighthavebeenunanticipatedconsequencesfrompoor decision-making.
7.Socialnetworkanalysis unpacksthebehaviorofindividualsatthemicrolevel,thenetworkstructurefromthepatternofrelationshipsatthemacrolevel,andhowthetwointeract. Socialnetworksbothformandconstrainopportunitiesforindividualchoicewhile
individualscansimultaneouslyinitiate,build,sustain,anddismantlerelationshipsdeterminingthenetwork’sglobalstructurealongtheway.
Theinstrumentalvalueoftherelationshipsunderinvestigationdetermineswhichnetwork structuresandpositionsgeneraterobustopportunitiesor,conversely,sturdyconstraints. Socialrelationshipscreatesocialcapitalasanopportunitystructure.Manymeasuresfor characterizingandcomparingnetworkstructuresandpositionswithinnetworkscanbe derivedthroughsocialnetworkanalysis.
Whensocialnetworkanalysisisdirectlyharnessedbytheplannersandurbandesignersas partoftheirdigitalworkbench,professionalpracticewillfundamentallychangeinresponse. Notably,thesocialcapitalbehindplanninganddesigndecisionswillhaveafarhigherlevelof participationandthereforeamoreinfluentialrole.
8.TheInternetofthings(IoT) describesanetworkofconnectabledevicesincluding computers,sensors,digitalandmechanical,andICT-enabledobjects.Withattachedunique identifiers(UIDs),animalsandpeoplecanbepartofthenetworkcapableofdatatransferover anelectronicnetworkindependentfromdirecthuman-to-humanorhuman-to-computer communication.
AnyIoTnetworkcanbeconceivedofasanecosystemofInternet-enabledsmartdevices withembeddedsystemswhichincorporateprocessors,sensors,andcommunicationhardware.Thenetworkcancollectdatafromtheirenvironments,processit,andtransmititback toanIoTgatewayeitheranalyzedlocallyorsenttothecloudtobeanalyzedremotely.IoT devicescancommunicatewitheachotherandactaccordinglyontheinformationtheyreceive andprocess,mostlywithouttheinterventionofhumansbeyondsettingthemupandprovidingthemwithinstructionsandaccessingthedata.
WhileplannersandurbandesignersarenotdirectlyinvolvedwithIoT,theimpactithas hadalreadyinthesmartcity—evenatthetriviallevelofsmartcarparking—willincreasingly influencethewayourcitiesoperate.Ascensorsproliferateandvastdatasetsbecomeever vaster,theprofessionswillhaveafardeeperinsightintohowcitiesoperateinstrumentally, andhowhumanswork,recreate,anddwell.Accessingthedataanddrawingfreshinsights fromitwillincreasinglyneedtoinvolvetheplanneranddesignerdirectlylestothersmore agiletochangestepin(andon)theirshoes.
9.Imageanalytics,alsoknownas computervision or imagerecognition,canpullinformation automaticallyfromasingleoravastcollectionofimages.AIisincorporatedasalgorithms thatcanautomaticallyextractspecifiedorunspecifieddatafromanimageorsetofimages andprocessit.
Logicalanalysisisthecoretoimageanalyticsfacilitatingtheinterpretationofinformation fromnontextualmaterialincludingdiagramsandgraphicsandisnotlimitedtophotographs. Significanttimewillbesavedasplannersandurbandesignersembraceimageanalytics.It isnotsimplyamatterofavoidingtediouswork;justasdermatologistsnowhavemoresuccessinspottingskincancersthroughAIandimagerecognition,sotoowillurbanprofessionalsbeabletospototherwiseimperceptabledifferences.Imagerecognitionappliedto satelliteimagesfromdifferenttimeperiodswillspotchangesandprovidemeasurements moreaccuratelyandinlesstimethanhumansarecapableof,evenwithanabundanceoftime thattheysimplydonothave.
10.Graphanalytics isappliedtostructured,unstructured,numeric,orvisualdatato derivedecisioninfluencinginsights.ItisarelativelynewapplicationofAIandisusedto
