Application of Quantitative Methods to Identify Analogous Cities: A Search for Relevant Experiences in the Development of Smart Cities for Implementation in Kazakhstan
Urdabayev M. Digel I. Kireyeva A.
June 2025Multidisciplinary Digital Publishing Institute (MDPI)
Smart Cities
2025#8Issue 3
Highlights: What are the main findings? A three-method quantitative approach (PCA, cluster analysis, t-SNE) reliably pinpoints the most similar smart cities to Almaty and Astana. Ottawa and Denver emerge as the closest matches, with Ankara and Phoenix also forming a second tier of comparable cities. What is the implication of the main finding? Focusing on these analogous cities can streamline the transfer of their successful smart city strategies to Almaty and Astana. The combined use of these statistical methods can serve as a replicable framework for other cities seeking evidence-based reference points for smart city development. Rapid urban growth and the spread of the concept of smart cities force an increasing need to understand how cities become “smart” and apply their experience where it will best take root. Understanding which experience will be most suitable is not a trivial task and requires labor-intensive analysis. This study aims to identify smart cities that are most similar to Almaty and Astana in terms of key indicators by applying quantitative methods. Using a sample of smart cities, this paper successively employs three methods—principal component analysis, hierarchical cluster analysis, and t-distributed stochastic neighbor embedding. The results showed that Denver and Ottawa are the closest to Almaty and Astana, followed by Ankara and Phoenix. The proposed methodology allowed us to assess the similarity of urban development conditions, with an assumption that similar development conditions determine approaches to the development of smart cities, and thus the relevance of experiences from other smart cities worldwide could be applied to Almaty and Astana. This approach is intended to contribute to the effectiveness of transferring advanced solutions of smart city development to the context of Kazakhstan. The obtained conclusions can be used to form recommendations for the development strategy of Almaty and Astana, as well as other cities facing similar challenges.
Almaty , Astana , city similarity , cluster analysis , PCA , quantitative methods , smart cities , t-SNE
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Department of Economics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Integrated Energy Systems, FB 16 Electrical Engineering/Computer Science, University of Kassel, Kassel, 34121, Germany
Department of Information and Implementation of Research Results, Institute of Economics of the Ministry of Science and Higher Education of RK, Almaty, 050010, Kazakhstan
Department of Economics
Integrated Energy Systems
Department of Information and Implementation of Research Results
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