Evaluating development prospects of smart cities: Cluster analysis of Kazakhstan’s regions

Digel, Ivan; Mussabalina, Dinara; Urdabayev, Marat; Nurmukhametov, N. N.; Akparova, Aigul

doi:10.21511/ppm.20(4).2022.07

Cited by 6 publications

(9 citation statements)

References 29 publications

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“…Two of them emerged as expected, consisting of Almaty and Astana in the first one, and Aktobe on its own in the second one, whereas two remaining clusters have more or less even distribution of cities. In principle, it was possible to merge Aktobe and the fourth cluster, but with the results of the previous work on that matter [ 1 ], it seemed reasonable to leave the clusters as they are now, separate.…”

Section: Resultsmentioning

confidence: 98%

“…Cluster analysis to assess the potential of smart cities in Kazakhstan has been used once before by Digel et al [ 1 ]. There are similar studies for other countries, although some of them aim to cluster those cities that can be called smart to a certain degree.…”

Section: Discussionmentioning

confidence: 99%

“…Using cities instead of regions to perform cluster analysis seems to be natural extension of previous research performed [ 1 ], sinceit is the cities, not the regions, that are in focus of smart development. The problem emerged in the realm of data availability, as some cities do not have data for some of the chosen variables, thus raising the dilemma of cutting the number of variables versus cutting the list of cities to analyse.…”

Section: Discussionmentioning

confidence: 99%

“…There are five groups of factors to consider when designing a smart city. These include social, technological, economic, environmental, and political factors [ 1 , 35 ]. The data used in the analysis includes indicators such as population growth, migration, education, employment, crime rate, pollution rate, etc.…”

Section: Methods and Datamentioning

confidence: 99%

“…This paper builds on previous research, the work of Digel et al [ 1 ], which used cluster analysis to assess the potential of smart cities in different regions of Kazakhstan. However, the main shortcoming of the previous study was that it did not directly analyze cities—only the administrative regions of the country.…”

Section: Introductionmentioning

confidence: 99%

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Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

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The potential for developing smart cities in Kazakhstan is evaluated using cluster analysis. Built on previous research focused on clustering the regions of Kazakhstan, this study applies the same method to the cities of the country. The analysis uses indicators related to human capital, infrastructure, education, information technology, production, and other factors to assess the potential of each city. The clustering is performed using Single Linkage, Complete Linkage, and Ward’s methods. The results show that Almaty and Astana are the cities with the highest potential for becoming smart cities. Aktobe is identified as a city with distinctive features that may help or hinder its development as a smart city. The remaining cities are clustered into two groups, with one group having the potential to catch up and maintain the trend of developing smart cities, while the other group is less suitable for starting smart city projects and may require more investment per capita. The study highlights the deep regional inequality affecting the potential to successfully develop and manage smart cities in Kazakhstan. The analysis also reveals some limitations and challenges in the data and variables used, including the lack of data for some variables and the difficulties in "translating" some factors and indicators into quantitative variables for clustering. The study concludes that future research should address these challenges and consider clustering inside certain regions to focus on their unique features. The study recommends launching pilot projects in small cities, with the most successful practices then scaled and implemented in the core smart cities and possibly Aktobe, if it manages to use its advantages to compensate for risks. Overall, this study provides insights into the potential of smart city development in Kazakhstan and can inform policymakers in their efforts to support smart city projects in the country.

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Section: Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Urdabayev,

Kireyeva,

Vasa

et al. 2024

PLoS ONE

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Economical and statistical analysis of the basic parameters of sustainable development of leading Russian regions

Kosobutskaya,

Serdyuk,

Soltis

2023

Proceedings of Voronezh State University. Series: Economics and Management

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Subject. Sustainable development of Russian regions. Basic parameters of sustainable development of Russian regions in three spheres: economic, social, and environmental. Objectives. To determine the regions with similar social, economic, and environmental parameters reflecting their level of sustainability and to group them into virtual clusters. To develop a methodological approach to the analysis of the basic parameters of sustainable development of leading Russian regions and to determine the points of stabilisation and destabilisation for these regions. Research methods. Dialectical method, monographic method, comparative analysis, structuring of an array of information - virtual clustering method. Using the dialectical and monographic methods to assess the sustainability of Russian regions, we justified the use of 10 parameters characterising the economic, social, and environmental subsystems of Russian regions. The information array included data about 82 regions for the period between 2017 and 2021. In the study, we calculated the average values of sustainability indicators for each region over the said time period. The regions were grouped using the k-means algorithm and the Statistica software. The degree of sustainability of clusters was assessed based on the sum of mean normalised values of the analysed parameters. A comparison of the mean normalised values obtained for each cluster with mean values for each cluster and each parameter allowed us to determine the points of stabilisation and destabilisation for the leading clusters. Results and discussion. By dividing the regions into groups, we managed to form six homogeneous clusters with a high degree of reliability. They differ in their structural composition of the studied parameters reflecting the level of development of social and economic subsystems of the regions comprising the clusters. The sustainability of clusters was assessed based on the sums of normalised values of the analysed parameters. The leading clusters are A and B. They are far ahead of the medium cluster C. Clusters D, E, and F form a group of outsiders. Economic, social, and environmental parameters were used to determine the points of stabilisation and destabilisation for the leading clusters.

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Digital Economic Transformation: In-depth Analysis of Smart Economy in the Implementation of Smart Village in Ketapang Village

Amalia,

Pertiwi

2023

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Weak technology and lack of human resources caused global e-government to show that Indonesia is below the world average. It is challenging for Indonesia to have an E-Government initiative program towards Good Governance, one of which is Smart City innovation. The community economy is crucial in determining community welfare in moving towards a Smart City. With a Smart Village in Ketapang Village, Banyuwangi Regency, it is hoped that it can create community welfare through a Smart Economy. This research aims to determine the success of Smart Economy in implementing Smart Village in Ketapang Village, Banyuwangi Regency. This research uses a qualitative method with a descriptive approach. Data collection techniques were carried out using observation, interviews and documentation. If the previous research only discussed Smart Cities in general, then in this research, the author examines one of the success factors of smart cities, namely, the Smart Economy. This update discusses the Smart Economy in depth. The study was carried out based on seven Smart Economy indicators using the Giffinger approach. The research results obtained show that the Smart Economy in Ketapang Village has not been maximized due to several obstacles, such as the uneven distribution of facilities provided by the regional government, the lack of ability of the Ketapang Village community to utilize technology, and the lack of government support for technology weak responsibility towards society. However, in general, its implementation is exemplary, manifested in developing technological innovations for the community and government facilities that continue to be built.

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Evaluating development prospects of smart cities: Cluster analysis of Kazakhstan’s regions

Cited by 6 publications

References 29 publications

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Discovering smart cities’ potential in Kazakhstan: A cluster analysis

Economical and statistical analysis of the basic parameters of sustainable development of leading Russian regions

Digital Economic Transformation: In-depth Analysis of Smart Economy in the Implementation of Smart Village in Ketapang Village

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