Speaker
Description
Introduction and research problem
Planning scholarship has increasingly described a conceptual progression from the smart city, understood as a city instrumented with pervasive digital devices, to the data-driven city, in which vast troves of urban data are generated and analyzed in real time, and further to the algorithmic city, in which governance itself is delegated to software-mediated infrastructure that continuously measures, monitors, and regulates urban life (Kitchin, 2014; Bibri, 2019; Coletta & Kitchin, 2017). This progression is rarely treated as a neutral technical upgrade. Cook and Karvonen (2024) argue that data-driven and algorithmic planning knowledge is produced incrementally and recursively, reasserting technical expertise as the primary driver of urban change, while a critical strand of the literature cautions that the consequences of this shift depend heavily on the institutional and political context into which it is introduced (Vanolo, 2014; Luque-Ayala & Marvin, 2015; Datta, 2015).
This paper takes up Track 4's central question directly: how do formal ontologies and AI-based tools structure planning knowledge and influence decision processes, and what does this imply for equity and legitimacy in everyday planning practice? It does so by reading a current, still unfolding national institutional case through the smart city to algorithmic city framework, rather than by waiting for implementation outcomes that do not yet exist. The research problem is therefore twofold. First, conceptually, how does the shift from a data-driven to an algorithmic planning ontology change what counts as legitimate planning knowledge? Second, empirically, what happens at the specific moment when a national government makes such an ontology compulsory across municipalities of unequal capacity, before that mandate has an operating history to evaluate?
Conceptual or theoretical framework
The paper's framework is drawn from a three-step conceptual chain. The first step, the smart city, is characterized by Kitchin (2014) as a city rendered legible through pervasive computing, whose governance is increasingly driven by data analytics rather than by conventional planning instruments. The second step, the data-driven city, is described by Bibri (2019) as a condition in which instrumentation, datafication, and computerization pervade the fabric of the city so that data itself becomes the primary material of urban knowledge; Cook and Karvonen (2024) add that this produces a distinctive epistemology, in which planning knowledge is assembled incrementally from bits of performance data rather than derived from a long-term theory of urban development. The third step, the algorithmic city, is where governance is delegated to software-mediated infrastructure: Coletta and Kitchin (2017) describe this as automated management, in which algorithms measure, monitor, and regulate the rhythms of urban life in ways that are frequently inaccessible to public scrutiny.
Critical literature complicates this chain rather than treating it as a linear technical progression. Vanolo (2014) argues that smart city discourse functions as a disciplinary strategy, a "smartmentality" that produces compliant municipal subjects competing against a technical benchmark of the good city. Luque-Ayala and Marvin (2015) observe that research on smart urbanism remains fragmented and concentrated on single city case studies from the global north, leaving open how these dynamics play out in different institutional and political settings. Datta (2015) demonstrates empirically, through the case of a national smart city mission in postcolonial India, that such missions are provincialized through local land politics and entrepreneurial state ambitions rather than implemented as designed. Yigitcanlar et al. (2020) add a governance-focused caution, noting that AI-driven city building carries real risks of bias, opacity, and uneven benefit that are not automatically resolved by more advanced technology. Taken together, this literature suggests that the smart city to algorithmic city chain is not simply technical and that its consequences depend on the institutional and political context of adoption, a claim this paper tests against a case drawn from outside the literature's usual reference settings.
Methods
This is a theoretical paper rather than an empirical qualitative study built on fieldwork; it draws on no interviews, surveys, or ethnographic observation and does not claim to generalize beyond the material discussed. Its method is conceptual: the smart city to data-driven city to algorithmic city framework is used as an analytical lens through which to read a small set of publicly available documentary sources closely, for what they mandate, propose, or merely declare.
Two primary policy texts anchor the reading. The first is Turkey's published "2030 National Smart Cities Strategy and Action Plan" circular, structured around four strategic goals, eight targets, thirty-two actions, and two hundred ninety-seven implementation steps, which makes a semantic planning model compulsory for municipal spatial planning and requires two hundred sixty-eight municipalities to adopt geographic information system-based development plans on a fixed multiyear timeline (Guven, 2026). The second is a single reported ministerial statement naming smart city management as one shared field within a bilateral Turkey-Cyprus artificial intelligence cooperation framework, for which no published circular or implementing instrument could be identified at the time of writing (Haber Kibris, 2026). These two readings are deliberately held apart rather than merged into a single composite case, because the paper's argument depends on the asymmetry between a binding mandate and a declared intention, not on treating the two jurisdictions as equivalent instances of the same phenomenon. The paper closes with an interpretive literature positioning exercise rather than a formal systematic review, stating where this documentary reading confirms, extends, or sits alongside existing claims in the smart city, data-driven urbanism, and algorithmic governance literatures.
Results or expected results
Reading Turkey's strategy through the algorithmic city framework shows a formal planning ontology, the semantic planning model, imposed from above and made binding across municipalities of very different administrative and technical capacity, before its consequences for planning practice on the ground are known. This relocates uncertainty rather than removing it: uncertainty shifts from the technical limits of any single model into the gap between what the mandate assumes municipalities can do and what they are able to do, a gap that remains largely invisible in the strategy document itself. The early warning and disaster management mechanisms embedded in the same digital infrastructure tie the legitimacy of crisis response directly to the legitimacy of the underlying planning ontology, so that uneven implementation of the semantic model carries directly into uneven capacity to respond to environmental shocks.
The Cyprus material, read as a declared intention rather than an implemented instrument, marks an earlier moment in the same institutional process, one that existing algorithmic governance literature, built mainly on cities where automated infrastructure is already operating, has paid comparatively little attention to. This asymmetry between a binding mandate and a mere announcement of cooperative intent is itself read as analytically informative: it demonstrates that the work of framing planning priorities and legitimating a particular knowledge politics begins before an algorithmic instrument is built, at the point where its adoption is first proposed.
Discussion and conclusion
These results confirm Cook and Karvonen's (2024) argument that data-driven and algorithmic planning reassert technical expertise as the primary driver of urban change and extend Coletta and Kitchin's (2017) account of algorithmic governance to an earlier moment in the same process, the point of mandate rather than the point of operation. Read alongside Datta's (2015) account of a national smart city mission in India, the Turkish case suggests that uniformly mandated algorithmic planning models are not confined to any single region, but that the specific risk of a widening gap between mandate and municipal capacity recurs wherever a national government imposes one planning ontology on jurisdictions of unequal administrative strength, aligning this case more closely with Datta's account of provincialization than with the voluntary city branding competitions Vanolo (2014) describes. The case also gives an institutional register to Yigitcanlar et al.'s (2020) general caution about bias, opacity, and uneven benefit in AI-driven city building, suggesting these risks are not only technical or ethical design questions but can be structural features of how a national mandate is rolled out on a fixed timeline regardless of starting capacity.
For Track 4's concern with the governance of uncertainty across institutional and model-mediated domains, the paper concludes that the moment of adoption, not only the moment of implementation, should be treated as a site where visibility, prioritization, and legitimacy in planning knowledge are already being decided. Future empirical work, following municipalities through the implementation of Turkey's mandate and tracking whether the Cyprus statement of intent develops into a binding instrument, would allow this theoretical reading to be tested against implementation evidence as it becomes available.
Keywords: algorithmic governance, smart city, institutional planning ontology, artificial intelligence in planning, equity and legitimacy
References
Bibri, S. E. (2019). The anatomy of the data-driven smart sustainable city: Instrumentation, datafication, computerization and related applications. Journal of Big Data, 6, Article 59. https://doi.org/10.1186/s40537-019-0221-4
Coletta, C., & Kitchin, R. (2017). Algorhythmic governance: Regulating the 'heartbeat' of a city using the Internet of Things. Big Data & Society, 4(2), 1–16. https://doi.org/10.1177/2053951717742418
Cook, M., & Karvonen, A. (2024). Urban planning and the knowledge politics of the smart city. Urban Studies, 61(2), 370–382. https://doi.org/10.1177/00420980231177688
Datta, A. (2015). New urban utopias of postcolonial India: Entrepreneurial urbanization in Dholera smart city, Gujarat. Dialogues in Human Geography, 5(1), 3–22. https://doi.org/10.1177/2043820614565748
Guven, S. (2026, September 19). Bakan Kurum'dan "Akilli Sehirler Stratejisi ve Eylem Plani" paylasimi. Anadolu Ajansi. https://www.aa.com.tr/tr/ekonomi/bakan-kurumdan-akilli-sehirler-stratejisi-ve-eylem-plani-paylasimi/4061887
Haber Kibris. (2026, June 15). Arikli: KKTC, Turkiye'nin yapay zeka eylem planinda yerini alacak. https://haberkibris.com/arikli-kktc-turkiyenin-yapay-zek-eylem-planinda-yerini-alacak-1849-2026-06-15.html
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. https://doi.org/10.1007/s10708-013-9516-8
Luque-Ayala, A., & Marvin, S. (2015). Developing a critical understanding of smart urbanism? Urban Studies, 52(12), 2105–2116. https://doi.org/10.1177/0042098015577319
Vanolo, A. (2014). Smartmentality: The smart city as disciplinary strategy. Urban Studies, 51(5), 883–898. https://doi.org/10.1177/0042098013494427
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), Article 1473. https://doi.org/10.3390/en13061473