Meta-analysis: The Role of the Algorithmic City in Sustainable and Smart City Development

Subject Areas : Urbanism

Narges Nonejad ¹ , morteza najafi ²

1 - Department of Urban Planning, North Tehran Branch, Islamic Azad University, Tehran, Iran.
2 -

Received: 2025-03-06 Accepted : 2025-05-21 Published : 2024-09-20

Keywords: Algorithmic City, Smart Development, Sustainable Development, PRISMA, Artificial Intelligence,

Abstract :

The algorithmic city, as an innovative approach to urban management, introduces the use of advanced technologies and big data to improve the quality of life for citizens and enhance the efficiency of urban systems. With the rapid growth of urban populations and various challenges such as pollution, traffic, and resource scarcity, the need for smart and sustainable urban development is increasingly felt. This research examines the concept of the algorithmic city and its role in achieving smart and sustainable development goals, aiming to provide a theoretical and practical framework for implementing this approach. The pioneering research is a systematic review with a meta-analytic approach based on study documents and articles from the last three years, focusing on algorithms and algorithmic planning. In this regard, using meta-analysis and a systematic review method based on the PRISMA model, the theoretical status of algorithmic and smart cities is examined. In the initial search, articles focusing on the algorithmic and smart city from 2020 to 2024 were considered, resulting in 48 articles selected for inclusion in the meta-analysis using the CMA software. The results of the research indicate how algorithmic urbanism can contribute to urban growth and development within the framework of artificial intelligence. Additionally, the findings show that the algorithmic city, through its quantitative structure, can lead to optimization in the fields of energy and transportation. However, the use of the algorithmic city necessitates attention to big data and big data analysis.

References:

1. Alomari, E., Katib, I., Albeshri, A., Yigitcanlar, T., & Mehmood, R. (2021b). Iktishaf+: A big data tool with automatic labelling for road traffic social sensing and event detection using distributed machine learning. Sensors, 21(9), 2993. https://doi.org/10.3390/s21092993
2. Andrews, C., Cooke, K., Gomez, A., Hurtado, P., Sanchez, T., Shah, S. et al. (2022). AI in planning opportunities and challenges and how to prepare. https://www.planning.org/publications/document/9255930.
3.
Anyoha, R. (2017). The history of artificial intelligence. https://sitn.hms.harvard.edu/fla sh/2017/history-artificial-intelligence. 4. Berawi, M. (2019). The role of industry 4.0 in achieving sustainable development goals.International Journal of Technology, 10(4), 644–647. https://ijtech.eng.ui.ac.id/article/view/3341.
5. Barns, Sarah. (2020). Platform Urbanism: Negotiating Platform Ecosystems in Connected Cities. 10.1007/978-981-32-9725-8.
6. Batty, M. (2013). The New Science of Cities. MIT Press.
7. Frankenfield, J. (2022). What is artificial intelligence (AI).https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp.
8. Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Van Bemmelen, J., Colangeli, G., et al. (2020). Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation. International Journal of Applied Earth Observation and Geoinformation, 88, Article 102068. https://doi.org/10.1016/j.jag.2020.102068
9. Gulshad, K., Wang, Y., Li, N., Wang, J., & Yu, Q. (2022). Likelihood of transformation to green infrastructure using ensemble machine learning techniques in Jinan, China. Land, 11(3), 317. https://doi.org/10.3390/land11030317
10. Goldsmith, S., & Crawford, S. (2014). The Responsive City: Engaging Communities Through Data-Smart Governance. Jossey-Bass.
11. Greenfield, A. (2013). Against the Smart City. Do Projects.
12. Huang, Z. (2021). AI in urban planning: 3 ways it will strengthen how we plan for the future. https://www.ura.gov.sg/Corporate/Resources/Ideas-and-Trends/AI-in-Urban-Planning
13. Ibrahim, M., Haworth, J., & Cheng, T. (2021). URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision. Environment and Planning B, 48(1), 76–93. https://doi.org/10.1177/2399808319846517
14. Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal 79, 1–14 (2014). https://doi.org/10.1007/s10708-013-9516-8
15. Janbi, N., Katib, I., Albeshri, A., & Mehmood, R. (2020). Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments. Sensors, 20 (20), 5796. https://doi.org/10.3390/s20205796
16. Legg, S., & Hutter, M. (2007). A collection of definitions of intelligence. Advances in artificial general intelligence (p. 17.24). Amsterdam: IOS Press.
17. Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., et al. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512–530. https://doi.org/10.1080/00045608.2015.1018773
18. Luque, Andres & Marvin, Simon. (2020). Urban Operating Systems: Producing the Computational City. 10.7551/mitpress/10869.001.0001.
19. Milojevic-Dupont, N., & Creutzig, F. (2021). Machine learning for geographically differentiated climate change mitigation in urban areas. Sustainable Cities and Society, 64, Article 102526. doi:10/gnbkq3.
20. Mattern, S. (2021). A City Is Not a Computer: Other Urban Intelligences. Princeton University Press.
21. Perveen, S., Kamruzzaman, M., & Yigitcanlar, T. (2017). Developing policy scenarios for sustainable urban growth management: A Delphi approach. Sustainability, 9(10). https://doi.org/10.3390/su9101787
22. Townsend, A. M. (2013). Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. W. W. Norton & Company.
23. Press, G. (2016). A very short history of artificial intelligence (AI) https://www.forbes.com/sites/gilpress/2016/12/30/a-very-short-history-of-artificial-intelligence-ai.
24. Sanchez, T., Shumway, H., Gordner, T., & Lim, T. (2022). The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2022.2102538
25. Tekouabou, S., Diop, E., Azmi, R., & Chenal, J. (2022). Artificial intelligence based methods for smart and sustainable urban planning: A systematic survey. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-022-09844-2
26. Testi, I. (2021). Computer vision for urban regeneration. https://medium.com/urban-ai/computer-vision-for-urban-regeneration-dca4594f6317.
27. Vanolo, A. (2014). Smartmentality: The Smart City as Disciplinary Strategy. Urban Studies, 51(5), 883-898.
28. Yigitcanlar, T., & Teriman, S. (2015). Rethinking sustainable urban development: Towards an integrated planning and development process. International Journal of Environmental Science and Technology, 12(1), 341–352. https://doi.org/10.1007/s13762-013-0491-x
29. Zhou, M., Ma, Y., Tu, J., & Wang, M. (2022). SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion. Environmental Science and Pollution Research, 29(48), 72797–72818. https://doi.org/10.1007/s11356-022-20904-9.