A New Fuzzy Approach for Analyzing the Smartness of Cities: Case Study for Turkey

Year 2021, , 308 - 325, 15.04.2021

Melike Erdoğan

https://doi.org/10.16984/saufenbilder.799469

Cited By: 2

Abstract

Smart cities, developed as alternative to classical urbanism, are areas where information and communication technologies are used to make places more livable, sustainable and efficient. If a city offers solutions to problems related to governance, people, economy, mobility, environment and living issues, it can be defined as "smart city". The smartness of cities can be measured on these six basic axes. By analyzing the smartness of cities, evaluations can be made on the quality of life, health, public safety, environment and services. Hereby, appropriate measures can be taken against problems and strategies can be developed to increase the smartness of cities. This paper proposes a new decision making analysis to evaluate and compare the smartness of cities. For this aim, we considered the cities which are the candidates to be smart areas in Turkey. At this point, we applied multi-criteria decision-making (MCDM) analysis to evaluate criteria and alternatives in the decision process. We also utilized from fuzzy logic to model the uncertainty in the best way. Furthermore, we applied extended version of ordinary fuzzy sets which is named spherical fuzzy sets for the first time with QUALIFLEX method. Thus, one of the most comprehensive qualitative analyses ever made in the evaluation of smart cities is revealed and the usability of spherical fuzzy sets by MCDM methods is demonstrated. In addition, a sensitivity analysis was used to examine the robustness of the proposed method. As a result, a novel fuzzy decision-making approach has been proposed in the evaluation of smart cities.

Keywords

decision making, QUALIFLEX, smartness of cities, spherical fuzzy sets

References

• [1] B. Baykurt and C. Raetzsch, “What smartness does in the smart city: From visions to policy,” Converg. Int. J. Res. into New Media Technol., p. 135485652091340, Mar. 2020.
• [2] J. R. Gil-Garcia, T. A. Pardo, and T. Nam, “A Comprehensive View of the 21st Century City: Smartness as Technologies and Innovation in Urban Contexts,” in Public Administration and Information Technology, vol. 11, Springer, 2016, pp. 1–19.
• [3] S. Bernardino, J. Freitas Santos, and J. Cadima Ribeiro, “The legacy of European Capitals of Culture to the ‘smartness’ of cities: The case of Guimarães 2012,” J. Conv. Event Tour., vol. 19, no. 2, pp. 138–166, Mar. 2018.
• [4] B. Murgante and G. Borruso, “Cities and smartness: A critical analysis of opportunities and risks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, vol. 7973 LNCS, no. PART 3, pp. 630–642.
• [5] T. Yigitcanlar and M. Kamruzzaman, “Smart Cities and Mobility: Does the Smartness of Australian Cities Lead to Sustainable Commuting Patterns?,” J. Urban Technol., vol. 26, no. 2, pp. 21–46, Apr. 2019.
• [6] M. Calderon, G. Lopez, and G. Marin, “Smartness and technical readiness of Latin American Cities: A critical assessment,” IEEE Access, vol. 6, pp. 56839–56850, 2018.
• [7] R. Carli, M. Dotoli, R. Pellegrino, and L. Ranieri, “Measuring and managing the smartness of cities: A framework for classifying performance indicators,” in Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013, pp. 1288–1293.
• [8] S. H. Alsamhi, O. Ma, M. S. Ansari, and F. A. Almalki, “Survey on collaborative smart drones and internet of things for improving smartness of smart cities,” IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers Inc., pp. 128125–128152, 2019.
• [9] C. Kahraman, “Multi-criteria decision making methods and fuzzy sets,” in Springer Optimization and Its Applications, vol. 16, Springer International Publishing, 2008, pp. 1–18.
• [10] L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, Jun. 1965.
• [11] Z. Eslaminasab and A. Hamzehee, “Determining appropriate weight for criteria in multi criteria group decision making problems using an Lp model and similarity measure,” 2019.
• [12] İ. Kaya, M. Çolak, and F. Terzi, “A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making,” Energy Strategy Reviews, vol. 24. Elsevier Ltd, pp. 207–228, 01-Apr-2019.
• [13] E. Afful-Dadzie, Z. K. Oplatková, and L. A. Beltran Prieto, “Comparative State-of-the-Art Survey of Classical Fuzzy Set and Intuitionistic Fuzzy Sets in Multi-Criteria Decision Making,” Int. J. Fuzzy Syst., vol. 19, no. 3, pp. 726–738, Jun. 2017.
• [14] H. P. McKenna, “Human-smart environment interactions in smart cities: Exploring dimensionalities of smartness,” Futur. Internet, vol. 12, no. 5, p. 79, May 2020.
• [15] M. L. Marsal-Llacuna, “Measuring the standardized definition of ‘smart city’: A proposal on global metrics to set the terms of reference for urban ‘smartness,’” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9156, pp. 593–611, 2015.
• [16] H. Ahvenniemi and A. Huovila, “How do cities promote urban sustainability and smartness? An evaluation of the city strategies of six largest Finnish cities,” Environ. Dev. Sustain., pp. 1–27, May 2020.
• [17] S. Hajduk, “The smartness profile of selected European cities in urban management – A comparison analysis,” J. Bus. Econ. Manag., vol. 19, no. 6, pp. 797–812, Dec. 2018.
• [18] E. G. Lima et al., “Smart and Sustainable Cities: The Main Guidelines of City Statute for Increasing the Intelligence of Brazilian Cities,” Sustainability, vol. 12, no. 3, p. 1025, Jan. 2020.
• [19] K. Axelsson and M. Granath, “Stakeholders’ stake and relation to smartness in smart city development: Insights from a Swedish city planning project,” Gov. Inf. Q., vol. 35, no. 4, pp. 693–702, Oct. 2018.
• [20] S. Al-Nasrawi, A. El-Zaart, and C. Adams, “Assessing smartness of smart sustainable cities: A comparative analysis,” in 2017 Sensors Networks Smart and Emerging Technologies, SENSET 2017, 2017, vol. 2017-January, pp. 1–4.
• [21] G. Dall’O, E. Bruni, A. Panza, L. Sarto, and F. Khayatian, “Evaluation of cities’ smartness by means of indicators for small and medium cities and communities: A methodology for Northern Italy,” Sustain. Cities Soc., vol. 34, pp. 193–202, Oct. 2017.
• [22] G. A. El Khayat and N. A. Fashal, “Inter and intra cities smartness: A survey on location problems and gis tools,” in Handbook of Research on Geographic Information Systems Applications and Advancements, IGI Global, 2016, pp. 295–320.
• [23] F. Corsini, F. Rizzi, and M. Frey, “Analysing smartness in European cities: a factor analysis based on resource efficiency, transportation and ICT,” Int. J. Glob. Environ. Issues, vol. 15, no. 3, pp. 235–254, 2016.
• [24] F. K. Gündoǧdu and C. Kahraman, “Spherical fuzzy sets and spherical fuzzy TOPSIS method,” J. Intell. Fuzzy Syst., vol. 36, no. 1, pp. 337–352, Jan. 2019.
• [25] F. Kutlu Gündoğdu and C. Kahraman, “A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets,” Eng. Appl. Artif. Intell., vol. 85, pp. 307–323, Oct. 2019.
• [26] C. Kahraman, S. C. Onar, and B. Oztaysi, “Performance measurement of debt collection firms using spherical fuzzy aggregation operators,” in Advances in Intelligent Systems and Computing, 2020, vol. 1029, pp. 506–514.
• [27] E. Boltürk, “AS/RS technology selection using spherical fuzzy TOPSIS and neutrosophic TOPSIS,” in Advances in Intelligent Systems and Computing, 2020, vol. 1029, pp. 969–976.
• [28] F. Kutlu Gündoğdu and C. Kahraman, “A novel spherical fuzzy analytic hierarchy process and its renewable energy application,” Soft Comput., vol. 24, no. 6, pp. 4607–4621, Mar. 2020.
• [29] O. Barukab, S. Abdullah, S. Ashraf, M. Arif, and S. A. Khan, “A new approach to fuzzy TOPSIS method based on entropy measure under spherical fuzzy information,” Entropy, vol. 21, no. 12, Dec. 2019.
• [30] F. Kutlu Gündoǧdu and C. Kahraman, “A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection,” J. Intell. Fuzzy Syst., vol. 37, no. 1, pp. 1197–1211, 2019.
• [31] P. Liu, B. Zhu, P. Wang, and M. Shen, “An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in China,” Eng. Appl. Artif. Intell., vol. 87, Jan. 2020.
• [32] F. K. Gündoğdu and C. Kahraman, “Extension of codas with spherical fuzzy sets,” J. Mult. Log. Soft Comput., vol. 33, no. 4–5, pp. 481–505, 2019.
• [33] C. Kahraman, F. K. Gündoğdu, A. Karaşan, and E. Boltürk, “Advanced Fuzzy Sets and Multicriteria Decision Making on Product Development,” in Studies in Systems, Decision and Control, vol. 279, Springer, 2020, pp. 283–302.
• [34] F. Kutlu Gündoǧdu, “A spherical fuzzy extension of MULTIMOORA method,” J. Intell. Fuzzy Syst., vol. 38, no. 1, pp. 963–978, 2020.
• [35] Z. Yang, X. Li, H. Garg, and M. Qi, “Decision support algorithm for selecting an antivirus mask over COVID-19 pandemic under spherical normal fuzzy environment,” Int. J. Environ. Res. Public Health, vol. 17, no. 10, May 2020.
• [36] K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 20, no. 1, pp. 87–96, Aug. 1986.
• [37] R. R. Yager and A. M. Abbasov, “Pythagorean membership grades, complex numbers, and decision making,” Int. J. Intell. Syst., vol. 28, no. 5, pp. 436–452, May 2013.
• [38] F. Kutlu Gündoǧdu and C. Kahraman, “A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection,” J. Intell. Fuzzy Syst., vol. 37, no. 1, pp. 1197–1211, Jan. 2019.
• [39] T. Y. Chen, C. H. Chang, and J. F. Rachel Lu, “The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making,” Eur. J. Oper. Res., vol. 226, no. 3, pp. 615–625, May 2013.
• [40] J. H. P. Paelinck, “Qualiflex: A flexible multiple-criteria method,” Econ. Lett., vol. 1, no. 3, pp. 193–197, Jan. 1978.
• [41] T. Y. Chen, “Data construction process and qualiflex-based method for multiple-criteria group decision making with interval-valued intuitionistic fuzzy sets,” Int. J. Inf. Technol. Decis. Mak., vol. 12, no. 3, pp. 425–467, May 2013.
• [42] T. Y. Chen, “Interval-valued intuitionistic fuzzy QUALIFLEX method with a likelihood-based comparison approach for multiple criteria decision analysis,” Inf. Sci. (Ny)., vol. 261, pp. 149–169, Mar. 2014.
• [43] Z. Zhang, “Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach.”
• [44] Y. Liang, J. Qin, L. Martínez, and J. Liu, “A heterogeneous QUALIFLEX method with criteria interaction for multi-criteria group decision making,” Inf. Sci. (Ny)., vol. 512, pp. 1481–1502, Feb. 2020.
• [45] J. Li and J. qiang Wang, “An Extended QUALIFLEX Method Under Probability Hesitant Fuzzy Environment for Selecting Green Suppliers,” Int. J. Fuzzy Syst., vol. 19, no. 6, pp. 1866–1879, Dec. 2017.
• [46] M. Gul, S. Mete, F. Serin, and E. Celik, “Fine–kinney-based occupational risk assessment using interval type-2 fuzzy qualiflex,” in Studies in Fuzziness and Soft Computing, vol. 398, Springer Science and Business Media Deutschland GmbH, 2021, pp. 135–149.
• [47] A. Zare, M. Malakoutikhah, and M. Alimohammadlou, “Selecting lighting system based on workers’ cognitive performance using fuzzy best–worst method and QUALIFLEX,” Cogn. Technol. Work, vol. 22, no. 3, pp. 641–652, Aug. 2020.
• [48] C. Zhou, D. Liu, P. Zhou, J. Luo, S. Yuksel, and H. Dincer, “Hybrid predictive decision-making approach to emission reduction policies for sustainable energy industry,” Energies, vol. 13, no. 9, May 2020.
• [49] L. Liu, Z. Bin, B. Shi, and W. Cao, “Sustainable supplier selection based on regret theory and QUALIFLEX method,” Int. J. Comput. Intell. Syst., vol. 13, no. 1, pp. 1120–1133, 2020.
• [50] X. F. Ding, L. X. Zhu, M. S. Lu, Q. Wang, and Y. Q. Feng, “A Novel Linguistic Z -Number QUALIFLEX Method and Its Application to Large Group Emergency Decision Making,” Sci. Program., vol. 2020, 2020.
• [51] X. Tian, Z. Xu, X. Wang, J. Gu, and F. E. Alsaadi, “Decision Models to Find a Promising Start-Up Firm with Qualiflex under Probabilistic Linguistic Circumstance,” Int. J. Inf. Technol. Decis. Mak., vol. 18, no. 4, pp. 1379–1402, Jul. 2019.
• [52] D. Banerjee, D. Guha, and F. Kouchakinejad, “Ranking alternatives using QUALIFLEX method by computing all spanning trees from pairwise judgements,” in Advances in Intelligent Systems and Computing, 2019, vol. 816, pp. 235–247.
• [53] A. Mahmoudi, S. A. Javed, Z. Zhang, and X. Deng, “Grey Group QUALIFLEX Method: Application in Project Management,” in Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 2019, pp. 189–195.
• [54] D. Banerjee, B. Dutta, D. Guha, and L. Martínez, “SMAA-QUALIFLEX methodology to handle multicriteria decision-making problems based on q-rung fuzzy set with hierarchical structure of criteria using bipolar Choquet integral,” Int. J. Intell. Syst., vol. 35, no. 3, pp. 401–431, Mar. 2020.
• [55] W. Liang, B. Dai, G. Zhao, and H. Wu, “Assessing the performance of green mines via a hesitant fuzzy ORESTE-QUALIFLEX method,” Mathematics, vol. 7, no. 9, Sep. 2019.
• [56] X. Feng, Q. Liu, and C. Wei, “Probabilistic linguistic QUALIFLEX approach with possibility degree comparison,” J. Intell. Fuzzy Syst., vol. 36, no. 1, pp. 719–730, 2019.
• [57] H. Demirel, E. Akyuz, E. Celik, and F. Alarcin, “Ships and Offshore Structures An interval type-2 fuzzy QUALIFLEX approach to measure performance effectiveness of ballast water treatment (BWT) system on-board ship An interval type-2 fuzzy QUALIFLEX approach to measure performance effectiveness of ballast water treatment (BWT) system on-board ship,” 2018.
• [58] S. Song, H. Zhou, and W. Song, “Sustainable shelter-site selection under uncertainty: A rough QUALIFLEX method,” Comput. Ind. Eng., vol. 128, pp. 371–386, Feb. 2019.
• [59] A. Alinezhad and N. Esfandiari, “Sensitivity Analysis in the QUALIFLEX and VIKOR Methods,” J. Optim. Ind. Eng., vol. 10, pp. 29–34, 2012.
• [60] Turkish Republic Ministry of Environment and Urbanization, “2020-2023 National Smart Cities Strategy and Action Plan,” 2019.