Akıllı Şehirleri Çok Kriterli Karar Verme ile Değerlendirme ve Sıralama

Year 2023, , 2538 - 2552, 16.12.2023

Mert Ünal , Gülfem Işıklar Alptekin

https://doi.org/10.35674/kent.1287759

Abstract

Kentsel alanlar büyümeye ve gelişmeye devam ederken, akıllı şehirlerin gelişimi, kentsel planlama ve sürdürülebilirlik açısından kritik bir konu haline gelmiştir. Şehirler, vatandaşların yaşam kalitesini sürekli arttırırken, çok sayıda kentsel işlevle başa çıkmak ve ekonomik büyümeyi artırmak için mümkün olduğunca akıllı olmaya zorlanmaktadır. Şehirler, büyük miktarda veriyi yönetmek, performansı ve verimliliği artırmak için sıklıkla veriye dayalı akıllı teknolojilere yatırım yapmaktadırlar. Ancak bir şehri akıllı olarak değerlendirebilmek için, sadece teknoloji alanında değil, çeşitli boyutlardaki kriterleri karşıladığını göstermek gerekir. Bunu belirlemenin bir yolu, şehri rakipleri arasında konumlandırmaktır. Bu makale, dünya çapında 48 şehrin akıllı şehir olma yönündeki seviyesini, başlıca iki yöntemden oluşan bir metodoloji kullanarak incelemektedir: TOPSIS ve entropi ağırlık yöntemi. Önerilen metodoloji, şehirleri yedi boyuta göre nesnel şekilde karşılaştırmaktadır. Elde edilen sıralama sonuçları, yalnızca şehirlerin akıllı şehir olmadaki genel performansını değil, aynı zamanda iyileştirmek için yatırım yapılması anlamlı olan zayıf taraflarını da vurgulamaktadır. Önerilen yaklaşım, şehir planlamacılarına, politika yapıcılara ve paydaşlara, sürdürülebilir akıllı şehirlerin gelişimi için en iyi uygulamaları belirleme, ilerlemeyi değerlendirme ve yatırımları doğru alanlara yönlendirme konusunda bir karar destek aracı olarak kullanılabilir.

Keywords

Akıllı Şehir, Dijital Altyapı, Çok Kriterli Karar Verme, TOPSIS, Entropi

Supporting Institution

Galatasaray Üniversitesi

Using Multi-Criteria Decision-Making for Smart City Evaluation and Ranking

Abstract

As urban areas continue to grow and evolve, the development of smart cities has become a critical topic in urban planning and sustainability. Cities have been forced to become as smart as possible to deal with huge amounts of civic functions and boost economic growth, while constantly increasing citizens’ quality of life. They frequently invest in data-driven smart technologies for managing the vast amount of data and improving performance and efficiency. However, a city earns its smartness status when it satisfies criteria from various dimensions. Therefore, governments and local authorities need to direct their limited resources to the right domains. One way to determine it is to position the city among its rivals. The paper proposes a methodology for evaluating and ranking the smartness of 48 cities worldwide using two techniques: TOPSIS and the entropy weight method. The proposed framework examines the smartness of the cities considering seven dimensions. The results reveal a comprehensive ranking that not only accounts for the cities' overall performance in smart city development but also highlights specific strengths and weak areas for improvement. The proposed approach can be utilized as a decision support tool for city planners, policymakers, and stakeholders to identify best practices for the development of sustainable smart cities, evaluate progress, and direct investments to the right areas.

Keywords

Smart City, Digital Infrastructure, Multi-Criteria Decision-Making, TOPSIS, Entropy

References

• Adali, E., Öztaş, G.Z., Öztaş, T. and Tuş, A., 2022. Assessment of European cities from a smartness perspective: An integrated grey MCDM approach. Sustainable Cities and Society, 84, 104021.
• Aihemaiti, A. and Zaim, A.H., 2018. Ranking model of smart cities in Turkey. Anatolian Journal of Computer Sciences, 3(2), 35, 35-43.
• Ahvenniemi, H. and Huovila, A., 2021. How do cities promote urban sustainability and smartness? An evaluation of the city strategies of six largest Finnish cities. Environment, Development and Sustainability, 23, 4174-4200.
• Barett, B.F.D., DeWit, A. and Yarime, M., 2021. Chapter 5 – Japanese smart cities and communities: Integrating technological and institutional innovation for Society 5.0. Smart Cities for Technological and Social Innovation Case Studies, Current Trends, and Future Steps, 73-94.
• Cai, M., Kassens-Noor, E., Zhao, Z., and Colbry, D., 2013. Are smart cities more sustainable? An exploratory study of 103 U.S. cities. Journal of Cleaner Production, 416, 137986.
• Dall’O, G., Bruni, E., Panza, A., Sarto, L. and Khayatian, F., 2017. Evaluation of cities’ smartness by means of indicators for small and medium cities and communities: A methodology for Northern Italy. Sustainable Cities and Society, 34, 193-202.
• Fang, Y. and Shan, Z., 2022. How to promote a smart city effectively? An evaluation model and efficiency analysis of smart cities in China. Sustainability, 14(11), 6512.
• Garau, C. and Pavan, V.M., 2018. Evaluating urban quality: Indicators and assessment tools for smart sustainable cities. Sustainability, 10(3), 575.
• GPCI, 2022: https://mori-m-foundation.or.jp/english /ius2/gpci2/index.shtml
• Hajduk, S., 2021. Multi-criteria analysis of smart cities on the example of the Polish cities. Resources, 10(5), 44.
• Hwang, C.L. and Yoon, K., 1981. Multiple Attribute Decision Making - Methods and Applications, Springer-Verlag, Heidelberg.
• Işıklar, G. and Büyüközkan, G., 2007. Using a multi-criteria decision-making approach to evaluate mobile phone alternatives. Computer Standards & Interfaces, 29, 265-274.
• Koca, G., Eğilmez. Ö. ve Akçakaya, O., 2021. Evaluation of the smart city: Applying the dematel technique. Telematics and Informatics, 62, 101625.
• Mokarrari, K.R. and Torabi, S.A., 2021. Ranking cities based on their smartness level using MADM methods. Sustainable Cities and Society, 72, 103030.
• OECD, 2022: https://goingdigital.oecd.org/, (10.09.2022).
• Okubo, H., Shimoda, Y., Kitagawa, Y., Gondokusuma, M.I.C., Sawamura, A., and Deto, K., 2022. Smart communities in Japan: Requirements and simulation for determining index values. Journal of Urban Management, 11(4), 500-518.
• Özkaya, G. ve Erdin, C., 2020. Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS techniques. Heliyon, 6, e05052.
• Pandiyan, P., Saravanan, S., Usha, K., Kannadasan, R., Alsharif, M.H., and Kim, M.K., 2023. Technological advancements toward smart energy management in smart cities. Energy Reports, 10, 648-677.
• Shannon, C.E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423.
• Spicer, Z., Goodman, N., and Wolfe, D.A., 2023. How ‘smart’ are smart cities? Resident attitudes towards smart city design. Cities, 141, 104442.
• Vanlı, T. and Akan, T., 2023. Mapping synergies and trade-offs between smart city dimensions: A network analysis. Cities, 142, 104527.
• Yaşar, S., Poyraz, Z., Yumuşak, R. ve Eren, T., 2022. Smart city analysis with ANP and PROMETHEE methods: A case in Ankara. Gazi Journal of Engineering Sciences, 8(1), 15-28.
• Ye, F, Chen, Y., Li, L., Li, Y. and Yin, Y., 2022. Multi-criteria decision-making models for smart city rankings: Evidence from the Pearl River Delta region, China. Cities, 128, 103793.