The burgeoning resurgence of interest in artificial intelligence (AI) is transforming urban planning and design. While studies have leveraged AI for generative planning and design, the focus has been on augmenting the capabilities of planners and designers, often overlooking public participation. Addressing this gap, this study proposes a new AI-aided and data-driven (AIDD) framework that integrates design, science, and participation to support participatory generative planning and design. Demonstrated in a hypothetical design case, this framework allows users with limited expertise to generate designs that resemble favored urban forms, meet zoning requirements, and improve safety perception performance.
AguilarRosaCalistoLuisFlackeJohannesAkbarAuliaPfefferKarin. 2021. “OGITO, An Open Geospatial Interactive Tool to Support Collaborative Spatial Planning with a Maptable.” Computers, Environment and Urban Systems86:101591.
2.
AttiaShadyGratiaElisabethDe HerdeAndréHensenJan L. M.2012.“ Simulation-Based Decision Support Tool for Early Stages of Zero-Energy Building Design.” Energy and Buildings49:2–15.
3.
BallatoreAndreaMcClintockWillGoldbergGraceKuhnWerner. 2020.“ Towards a Usability Scale for Participatory GIS.” In Geospatial Technologies for Local and Regional Development: Proceedings of the 22nd AGILE Conference on Geographic Information Science, 327–48. Cham: Springer.
4.
BattyMichael. 1995. “Planning Support Systems and the New Logic of Computation.” Regional Development Dialogue16:1–17.
5.
BattyMichael. 2018a. “Artificial Intelligence and Smart Cities.” Environment and Planning B: Urban Analytics and City Science45 (1): 3–6.
6.
BattyMichael. 2018b. “Digital Twins.” Environment and Planning B: Urban Analytics and City Science45 (5): 817–820.
7.
BerkePhilip R.StevensMark R.2016. “Land Use Planning for Climate Adaptation: Theory and Practice.” Journal of Planning Education and Research36 (3): 283–89.
8.
BraunTrevorFungBenjamin C. M.IqbalFarkhundShahBabar. 2018. “Security and Privacy Challenges in Smart Cities.” Sustainable Cities and Society39:499–507.
9.
CaldarelliG.ArcauteE.BarthelemyM.BattyM.GershensonC.HelbingD.MancusoS.MorenoY.RamascoJ. J.RozenblatC.2023. “The Role of Complexity for Digital Twins of Cities.” Nature Computational Science3:374–81.
10.
CasaliYleniaAydinNazli YoncaComesTina. 2022.“ Machine Learning for Spatial Analyses in Urban Areas: A Scoping Review.” Sustainable Cities and Society85:104050.
CreswellAntoniaWhiteTomDumoulinVincentArulkumaranKaiSenguptaBiswaBharathAnil A.2018. “Generative Adversarial Networks: An Overview.” IEEE Signal Processing Magazine35 (1): 53–65.
14.
DingXinWangYongweiXuZuhengWelchWilliam J.WangZ. Jane. 2020. “CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation.” International Conference on Learning Representations. https://openreview.net/forum?id=PrzjugOsDeE.
15.
DuJiaxinYeXinyueJankowskiPiotrSanchezTomMaiGengchen. 2023. “Artificial Intelligence Enabled Participatory Planning: A Review.” International Journal of Urban Sciences28:183–210.
16.
EckhoffDavidWagnerIsabel. 2017. “Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions.” IEEE Communications Surveys & Tutorials20 (1): 489–516.
17.
EwingReidHandySusan. 2009. “Measuring the Unmeasurable: Urban Design Qualities Related to Walkability.” Journal of Urban Design14 (1): 65–84.
18.
GeroJohn S.1990. “Design Prototypes: A Knowledge Representation Schema for Design.” AI Magazine11 (4): 26–26.
19.
GoodfellowIanPouget-AbadieJeanMirzaMehdiXuBingWarde-FarleyDavidOzairSherjilCourvilleAaronBengioYoshua. 2014. “Generative Adversarial Nets.” Advances in Neural Information Processing Systems. https://dl.acm.org/doi/10.5555/2969033.2969125.
20.
HanzlMalgorzata. 2007. “Information Technology as a Tool for Public Participation in Urban Planning: A Review of Experiments and Potentials.” Design Studies28 (3): 289–307.
21.
HartmannStefanWeinmannMichaelWesselRaoulKleinReinhard. 2017. “StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks.” 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic. https://cg.cs.uni-bonn.de/backend/v1/files/publications/street_gan_2017.pdf.
22.
HeKaimingZhangXiangyuRenShaoqingSunJian. 2016. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://ieeexplore.ieee.org/document/7780459/.
23.
JacobsJane. 1961. The Death and Life of Great American Cities. New York: Random House LLC.
24.
KlostermanRichard E.1997. “Planning Support Systems: A New Perspective on Computer-Aided Planning.” Journal of Planning Education and Research17 (1): 45–54.
25.
KnightTerryStinyGeorge. 2001. “Classical and Non-Classical Computation.” ARQ: Architectural Research Quarterly5 (4): 355–72.
26.
KodaliNaveenAbernethyJacobHaysJamesKiraZsolt. 2017. “On Convergence and Stability of GANs.” arXiv preprint arXiv:1705.07215.
LinYueXiaoNingchuan. 2023. “A Computational Framework for Preserving Privacy and Maintaining Utility of Geographically Aggregated Data: A Stochastic Spatial Optimization Approach.” Annals of the American Association of Geographers113 (5): 1035–56.
29.
LongYingZhangEnjia. 2020. Data Augmented Design: Embracing New Data for Sustainable Urban Planning and Design. Cham: Springer Nature.
30.
LucicMarioKurachKarolMichalskiMarcinGellySylvainBousquetOlivier. 2018. “Are GANs Created Equal? A Large-Scale Study.” Advances in Neural Information Processing Systems. https://dl.acm.org/doi/10.5555/3326943.3327008.
MouratidisKostas. 2019. “The Impact of Urban Tree Cover on Perceived Safety.” Urban Forestry & Urban Greening44:126434.
33.
MuellerJohannesLuHangxinChirkinArtemKleinBernhardSchmittGerhard. 2018. “Citizen Design Science: A Strategy for Crowd-Creative Urban Design.” Cities72:181–88.
34.
NaikNikhilPhilipoomJadeRaskarRameshHidalgoCésar. 2014. “Streetscore-Predicting the Perceived Safety of One Million Streetscapes.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. https://ieeexplore.ieee.org/document/6910072.
35.
OxmanRivka. 2006. “Theory and Design in the First Digital Age.” Design Studies27 (3): 229–65.
36.
OxmanRivka. 2009.“ Performative Design: A Performance-Based Model of Digital Architectural Design.” Environment and Planning B-Planning & Design36 (6): 1026–37.
37.
PanHaozhiDealBrian. 2020. “Reporting on the Performance and Usability of Planning Support Systems—Towards a Common Understanding.” Applied Spatial Analysis and Policy13 (1): 137–59.
38.
PanHaozhiKwakYoonshinDealBrian. 2022. “Participatory Development of Planning Support Systems to Improve Empowerment and Localization.” Journal of Urban Technology29 (2): 33–54.
39.
PelzerPeter. 2017. “Usefulness of Planning Support Systems: A Conceptual Framework and an Empirical Illustration.” Transportation Research Part A: Policy and Practice104:84–95.
40.
PelzerPeterGeertmanStanvan der HeijdenRobRouwetteEtiënne. 2014. “The Added Value of Planning Support Systems: A practitioner’s Perspective.” Computers, Environment and Urban Systems48:16–27.
41.
PengZhong-RenLuKai-FaLiuYangheZhaiWei. 2023.“ The Pathway of Urban Planning AI: From Planning Support to Plan-Making.” Journal of Planning Education and Research44:2263–79.
42.
PoplinAlenka. 2012. “Playful Public Participation in Urban Planning: A Case Study for Online Serious Games.” Computers, Environment and Urban Systems36 (3): 195–206.
43.
PuntE. P.GeertmanStan C. M.AfroozA. E.WittePhilipp A.PettitChristopher James. 2020. “Life Is a Scene and We Are the Actors: Assessing the Usefulness of Planning Support Theatres for Smart City Planning.” Computers, Environment and Urban Systems82:101485.
44.
QuanSteven Jige. 2022. “Urban-GAN: An Artificial Intelligence-Aided Computation System for Plural Urban Design.” Environment and Planning B: Urban Analytics and City Science49 (9): 2500–15.
45.
QuanSteven JigeEconomouAthanassiosGraslThomasYangPerry Pei-Ju. 2020. “An Exploration of the Relationship between Density and Building Energy Performance.” Urban Design International25 (1): 92–112.
46.
QuanSteven JigeParkJamesEconomouAthanassiosLeeSugie. 2019. “Artificial Intelligence-Aided Design: Smart Design for Sustainable City Development.” Environment and Planning B46 (8): 1581–99.
47.
RadfordAlecMetzLukeChintalaSoumith. 2016. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2–4.
48.
RahmanMd MostafizurSzabóGyörgy. 2021. “Multi-Objective Urban Land Use Optimization Using Spatial Data: A Systematic Review.” Sustainable Cities and Society74:103214.
49.
RussoPatriziaLanzilottiRosaCostabileMaria F.PettitChristopher J.2018. “Towards Satisfying Practitioners in Using Planning Support Systems.” Computers, Environment and Urban Systems67:9–20.
50.
SanchezThomas W.ShumwayHannahGordnerTreyLimTheo. 2023. “The Prospects of Artificial Intelligence in Urban Planning.” International Journal of Urban Sciences27 (2): 179–94.
51.
SenbelM.ChurchS. P.2011. “Design Empowerment: The Limits of Accessible Visualization Media in Neighborhood Densification.” Journal of Planning Education and Research31 (4): 423–37.
52.
SteadmanPhilip.2001. “Binary Encoding of a Class of Rectangular Built Forms.” 3rd International Space Syntax Symposium, Atlanta, GA.
53.
SteinøNicolaiYıldırımMiray BaşÖzkarMine. 2013. “Parametric Design Strategies for Collaborative Urban Design. In eCAADe2013: Computation and Performance, 195–203. eCAADe.
54.
SteinitzCarl. 2012. A Framework for Geodesign: Changing Geography by Design. Redlands, CA: Esri Press.
55.
StinyGeorgeMarchLionel. 1981. “Design Machines.” Environment and Planning B: Planning and Design8 (3): 245–55.
VardouliTheodora. 2013. “Performed by and Performative for.”eCAADe18:243.
58.
VonkGuidoGeertmanStanSchotPaul. 2005. “Bottlenecks Blocking Widespread Usage of Planning Support Systems.” Environment and Planning A37 (5): 909–24.
59.
VonkGuidoGeertmanStanSchotPaul. 2007. “A SWOT Analysis of Planning Support Systems.” Environment and Planning A39 (7): 1699–714.
60.
WickiSergioSchwaabJonasPerhacJanGrêt-RegameyAdrienne. 2021. “Participatory Multi-Objective Optimization for Planning Dense and Green Cities.” Journal of Environmental Planning and Management64 (14): 2532–51.
61.
WijnandsJasper S.NiceKerry A.ThompsonJasonZhaoHaifengStevensonMark. 2019. “Streetscape Augmentation Using Generative Adversarial Networks: Insights Related to Health and Wellbeing.” Sustainable Cities and Society49:101602.
62.
WuAbraham NoahStouffsRudiBiljeckiFilip. 2022. “Generative Adversarial Networks in the Built Environment: A Comprehensive Review of the Application of GANs Across Data Types and Scales.” Building and Environment223:109477.
63.
WuJin-LongKashinathKarthikAlbertAdrianChirilaDragosXiaoHeng. 2020. “Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems.” Journal of Computational Physics406:109209.
64.
WuNingSilvaElisabete A.2010. “Artificial Intelligence Solutions for Urban Land Dynamics: A Review.” Journal of Planning Literature24 (3): 246–65.
65.
WuYihanZhanQingmingQuanSteven JigeFanYuliYangYu. 2021. “A Surrogate-Assisted Optimization Framework for Microclimate-Sensitive Urban Design Practice.” Building and Environment195:107661.
66.
YeXinyueDuJiaxinHanYuNewmanGalenRetchlessDavidZouLeiHamYoungjibCaiZhenhang. 2023. “Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda.” Journal of Planning Literature38 (2): 187–99.
67.
ZhangFanZhouBoleiLiuLiuLiuYuFungHelene H.LinHuiRattiCarlo. 2018. “Measuring Human Perceptions of a Large-Scale Urban Region Using Machine Learning.” Landscape and Urban Planning180:148–60.
68.
ZhaoBoZhangShaozengXuChunxueSunYifanDengChengbin. 2021. “Deep Fake Geography? When Geospatial Data Encounter Artificial Intelligence.” Cartography and Geographic Information Science48 (4): 338–52.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
