Examining thematic evolution and epistemological shifts in the literature on artificial intelligence and machine learning in urban planning and smart city studies (2020–2025): A scientometric and interpretive analysis

Asgari, Atiyeh; Mirzajani, Mahdi; Zalibeigi, Anahita

doi:10.22034/uep.2026.583459.1901

Examining thematic evolution and epistemological shifts in the literature on artificial intelligence and machine learning in urban planning and smart city studies (2020–2025): A scientometric and interpretive analysis

Document Type : Review

Authors

Atiyeh Asgari ¹

Mahdi Mirzajani ²

Anahita Zalibeigi ²

¹ Assistant Professor, Department of Urban Planning, ST.C., Islamic Azad University, Tehran, Iran

² PhD Candidate, Department of Urban Planning, ST.C., Islamic Azad University, Tehran, Iran

10.22034/uep.2026.583459.1901

Abstract

The expansion of artificial intelligence and machine learning in urban planning and smart city studies, beyond analytical tool development, has been accompanied by changes in knowledge production, urban problem definition, and decision-making support. Despite rapid research growth, the relationship between thematic evolution and changes in the logic of urban knowledge production has not been systematically examined. This study investigates the thematic evolution and epistemological shifts in this literature during 2020–2025. Its novelty lies in integrating scientometric analysis with epistemological interpretation to explain the reconfiguration of the field’s knowledge structure. Data were extracted from Web of Science and Scopus, and following PRISMA 2020 screening, 3,578 articles were selected. Using a scientometric–interpretive approach, we analyzed supervised thematic classification, temporal trend analysis, the smoothed relative growth index (RGI), epistemological paradigms, scientific collaboration, and the geographical distribution of knowledge production. The results showed that urban sustainability, accounting for 31.9%, is the largest thematic domain; the data-driven and predictive paradigm, accounting for 77.7%, is the dominant approach to knowledge production, and generative AI approaches are gradually expanding. Knowledge production is also interdisciplinary but remains geographically concentrated. Overall, the findings indicate an intra-disciplinary reconfiguration within the literature on AI in urban planning and a gradual shift of AI from analytical tools toward becoming part of the infrastructure for knowledge production and urban decision-making support.

Keywords

Epistemological shift

machine learning

scientometric analysis

smart city

supervised thematic classification

urban artificial intelligence

urban planning

Subjects

Urban planning

Acuto, M., et al. (2023). Urban governance in the digital age. Nature Cities, 1(1), 12–19. https://doi.org/10.1038/s44284-023-00002-3
Batty, M. (2013). The new science of cities. MIT Press. https://doi.org/10.7551/mitpress/9399.001.0001
Batty, M. (2024). AI and design. Environment and Planning B: Urban Analytics and City Science, 51(4), 799–802. https://doi.org/10.1177/23998083241236619
Batty, M. (2026). The evolution of AI in city planning. Discover Cities, 3, 47. https://doi.org/10.1007/s44327-026-00224-0
Behzadfar, M., Rezaei, Sh., Torkaman, M., & Razaghi, F. (2026). Analyzing emerging trends in sustainable transportation: Designing a framework for transforming urban transportation policy and planning models. Journal of Urban Economics and Planning, 7(9), 18–40. https://doi.org/10.22034/uep.2026.577735.1853
Bibri, S. E. (2021). Data-driven smart sustainable cities of the future: Urban computing and intelligence for strategic, short-term, and joined-up planning. Springer. https://doi.org/10.1007/978-3-030-68059-1
Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006
Cugurullo, F., Caprotti, F., Cook, M., Karvonen, A., McGuirk, P., & Marvin, S. (2024). The rise of AI urbanism in post-smart cities: A critical commentary on urban artificial intelligence. Urban Studies, 61(6), 1168–1182. https://doi.org/10.1177/00420980231203386
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Faludi, A. (1973). Planning theory. Pergamon Press.
Hall, P. (2014). Cities of tomorrow: An intellectual history of urban planning and design since 1880 (4th Ed.). Wiley-Blackwell.
Healey, P. (1997). Collaborative planning: Shaping places in fragmented societies. Macmillan.
Innes, J. E., & Booher, D. E. (2010). Planning with complexity: An introduction to collaborative rationality for public policy. Routledge.
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625–636. https://doi.org/10.1080/13658816.2019.1684500
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE. https://doi.org/10.4135/9781473909472
Kitchin, R. (2016). The ethics of smart cities and urban big data. Philosophical Transactions of the Royal Society A, 374(2083), 20160115. https://doi.org/10.1098/rsta.2016.0115
Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087
Kitchin, R., & Moore-Cherry, N. (2021). Fragmented governance, the urban data ecosystem and smart city-regions: The case of Metropolitan Boston. Regional Studies, 55(12), 1913–1923. https://doi.org/10.1080/00343404.2020.1735627
Leszczynski, A. (2020). Glitchy vignettes of platform urbanism. Environment and Planning D: Society and Space, 38(2), 189–208. https://doi.org/10.1177/0263775819878721
Mai, G., Xie, Y., Jia, X., Lao, N., Rao, J., Zhu, Q., Liu, Z., Chiang, Y.-Y., & Jiao, J. (2025). Towards the next generation of geospatial artificial intelligence. International Journal of Applied Earth Observation and Geoinformation, 136, 104368. https://doi.org/10.1016/j.jag.2025.104368
OECD. (2024). Governing smart cities and artificial intelligence. OECD Publishing. https://doi.org/10.1787/g2g55432-en
Si, S., Yao, Y., & Wu, J. (2025). Artificial intelligence in urban planning: A bibliometric analysis and hotspot prediction. Land, 14(11), 2100. https://doi.org/10.3390/land14112100
Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94, 104562. https://doi.org/10.1016/j.scs.2023.104562
Steenmans, K., Robin, E., Acuto, M., Iwaszuk, E., & Ortega Garza, L. (2023). Governing the informed city: Examining local government strategies for information production, consumption, and knowledge sharing across ten cities. Urban Governance, 3(4), 243–251. https://doi.org/10.1016/j.ugj.2023.09.001
United Nations Human Settlements Programme. (2022). AI & cities: Risks, applications and governance. UN-Habitat. https://unhabitat.org/ai-cities-risks-applications-and-governance
Xu, H., Omitaomu, F., Sabri, S., Zlatanova, S., Li, X., & Song, Y. (2024). Leveraging generative AI for urban digital twins: A scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement. Urban Informatics, 3, 29. https://doi.org/10.1007/s44212-024-00060-w
Yigitcanlar, T., Agdas, D., Xia, B., Degirmenci, K., & Inkinen, T. (2024). Unlocking artificial intelligence adoption in local governments: Best practice lessons from real-world implementations. Smart Cities, 7(4), 1576–1593. https://doi.org/10.3390/smartcities7040064
Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473. https://doi.org/10.3390/en13061473
Zhang, W., Han, J., Xu, Z., Ni, H., Lyu, T., Liu, H., & Xiong, H. (2026). Towards urban general intelligence: A review and outlook of urban foundation models. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3821528