Determination of the Quality of Life using Hybrid BWM-TOPSIS Analysis: Case study of Tabriz (District 1,2,3 and 8), Iran

Year 2022, Volume: 4 Issue: 1, 7 - 17, 15.06.2022

Rouhollah Jafarı-sırızı Amir Oshnooeı-nooshabadı Zeinab Khabbazı-kenarı Alireza Sadeghı

https://doi.org/10.51489/tuzal.1066578

Cited By: 1

Abstract

The quality of life in cities is one of the most attractive research cases for researchers and urban planners. The Best-Worst Method (BWM) is one of the most recent multi-criteria decision-making methods (MCDM). In this study, we have tried to rank the quality of life in districts 1, 2, 3 & 8 of Tabriz city by using a combination of BWM and TOPSIS methods and Geographic Information System (GIS) capabilities. Twenty criteria have been considered for this study. At first, criteria map was prepared by using Remote Sensing processes and GIS. Then criteria weighting was determined by using the BWM method. After that, by combining the acquired weights by the TOPSIS method, the ranking of the districts was discovered. The results of BWM showed that the most effective criteria in quality of life is paying attention to the future of the city (%12.33). As a result of the quality of life analysis, it was revealed that district 2 has the best quality and district 3 has the worst quality. According to the prepared map of the quality of life, the best districts were Elahi-parast, Mirdamad 2 and Rajaea Shahr 2. On the other hand, the worst districts were Zangouleh Abad, Islam Abad 1 and Islam Abad 2.

Keywords

BWM, Qoaulity of lfe, Tabriz, TOPSIS, GIS

Hibrit BWM-TOPSIS analizi kullanılarak Yaşam Kalitesinin Belirlenmesi; Tebriz (Bölge 1-2-3-8) Iran Örneği

Abstract

Şehirlerdeki yaşam kalitesi, araştırmacılar ve şehir plancıları için en dikkat çeken araştırma konularından biridir. En iyi- En kötü yöntemi (Best Worst Method-BWM), en yeni, çok kriterli karar verme (ÇKKV) metotlarından biridir. Bu çalışmada, BWM ve TOPSIS yöntemleri ve Coğrafi Bilgi Sistemleri (CBS) bir arada kullanılarak, Tebriz şehrinin 1,2,3 ve 8. bölgelerindeki yaşam kalitesi sıralanmaya çalışılmıştır. Bu çalışma için yirmi kriter dikkate alınmıştır. Öncelikle, Uzaktan Algılama ve CBS teknikleri kullanılarak kriter haritaları hazırlanmıştır. Daha sonra BWM yöntemi kullanılarak kriter ağırlıklandırması yapılmıştır. Elde edilen ağırlıklar TOPSIS yöntemi ile analiz edilerek bölge sıralaması ortaya çıkarılmıştır. BWM sonuçları, yaşam kalitesindeki en etkili kriterin, şehrin geleceğine önem veren kriterler olduğunu ortaya koymuştur (% 12,33). Yaşam kalitesi analizleri sonucunda 2. bölgenin en iyi yaşam kalitesine sahipken, 3. bölgenin ise en kötü yaşam kalitesine sahip olduğu ortaya çıkmıştır. Hazırlanan yaşam kalitesi haritasına göre Elahi Parast, Mirdamad 2 ve Rajaea Shahr 2 semtleri en iyi yaşam kalitesine sahiptir. Buna karşın, Zangouleh Abad, İslam Abad ve İslam Abad 2 semtleri ise en kötü yaşam kalitesine sahip semtler olarak ortaya çıkmıştır.

Keywords

BWM, CBS, Tebriz, TOPSİS, Yaşam Kalitesi

References

• Akande, A., Cabral, P., Gomes, P., & Casteleyn, S. (2019). The Lisbon ranking for smart sustainable cities in Europe. Sustainable Cities and Society, 44, 475-487.
• Alberti, M., & Marzluff, J. M. (2004). Ecological resilience in urban ecosystems: linking urban patterns to human and ecological functions. Urban ecosystems, 7(3), 241-265.
• Ali Akbari, E., & Amini, M. (2010). Urban Quality of Life in Iran (1986-2006). Social Welfare Quarterly, 10(36), 121-148.
• Alizadeh, M., Alizadeh, E., Asadollahpour Kotenaee, S., Shahabi, H., Beiranvand Pour, A., Panahi, M., & Saro, L. (2018). Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran. Sustainability, 10(10), 3376.
• Baghanam, A. H., Nourani, V., Sheikhbabaei, A., & Seifi, A. J. (2019). Statistical Downscaling and Projection of Future Temperature Change for Tabriz City, Iran (No. 1813). EasyChair.
• Bilal, M., Nichol, J. E., Bleiweiss, M. P., & Dubois, D. (2013). A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces. Remote sensing of environment, 136, 135-145.
• Dameri, R. P. (2016). Smart City and ICT. Shaping urban space for better quality of life. In Information and Communication Technologies in Organizations and Society (pp. 85-98). Springer, Cham.
• Dodman, D. (2016). Environment and urbanization. International Encyclopedia of Geography: People, the Earth, 1-9.
• Douglas, O., Russell, P., & Scott, M. (2019). Positive perceptions of green and open space as predictors of neighbourhood quality of life: implications for urban planning across the city region. Journal of environmental planning and management, 62(4), 626-646.
• Estevez, E., Lopes, N., & Janowski, T. (2016). Smart sustainable cities: Reconnaissance study.
• Fox, P. D. (2019). Not Your Grandparents' Flâneur: The Afropolitan Detective in the Urban Crime Novels of Quartey and Crompton. Africa Today, 65(4), 61-82.
• Francis, R. A., Millington, J. D., & Chadwick, M. A. (Eds.). (2016). Urban landscape ecology: science, policy and practice. Routledge.
• Gavrilidis, A. A., Ciocănea, C. M., Niţă, M. R., Onose, D. A., & Năstase, I. I. (2016). Urban landscape quality index–planning tool for evaluating urban landscapes and improving the quality of life. Procedia Environmental Sciences, 32, 155-167.
• Gholi Motlagh, M., & Darvishi, F. (2021). Urban Quality of Life Survey by Localizing Eurobarometer Indexes (Case Study: the City of Qazvin). Human Geography Research, 53(2), 579-597.
• Goli, N., Jahani, M., & Behniyafar, A. (2021). The Effect of Physical and Economic Aspects of Quality of Life on Population Sustainability in Rural Settlements (Case study: Villages of Razaviyeh District, Mashhad County, Razavi Khorasan Province, Iran). Geography (Regional Planning), 11(3), 664-679.
• Gonzalez-Urango, H., Mu, E., & García-Melón, M. (2021). Stakeholder engagement and ANP best research practices in sustainable territorial and urban strategic planning. In Multiple Criteria Decision Making for Sustainable Development (pp. 93-130). Springer, Cham.
• Gou, Z., Xie, X., Lu, Y., & Khoshbakht, M. (2018). Quality of Life (QoL) survey in Hong Kong: Understanding the importance of housing environment and needs of residents from different housing sectors. International journal of environmental research and public health, 15(2), 219.
• Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. science, 319(5864), 756-760.
• Haghighi Fard, S. M., & Doratli, N. (2022). Evaluation of Resilience in Historic Urban Areas by Combining Multi-Criteria Decision-Making System and GIS, with Sustainability and Regeneration Approach: The Case Study of Tehran (IRAN). Sustainability, 14(5), 2495.
• Han, J., Meng, X., Zhou, X., Yi, B., Liu, M., & Xiang, W. N. (2017). A long-term analysis of urbanization process, landscape change, and carbon sources and sinks: A case study in China's Yangtze River Delta region. Journal of Cleaner Production, 141, 1040-1050.
• Hashemi Darebadami, S., Darvishi Boloorani, A., AlaviPanah, S. K., Maleki, M., & Bayat, R. (2019). Investigation of changes in surface urban heat-island (SUHI) in day and night using multi-temporal MODIS sensor data products (Case Study: Tehran metropolitan). Journal of Applied researches in Geographical Sciences, 19(52), 113-128.
• Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.
• Iranian Statistics Center. (2021). Iranian statistical calender. Iranian Statistics Center: Tehran, Iran.
• Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., & Cristóbal, J. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and remote sensing letters, 11(10), 1840-1843.
• Kaklauskas, A., Zavadskas, E. K., Radzeviciene, A., Ubarte, I., Podviezko, A., Podvezko, V., Kuzminske, A., Banaitis A., Binkyte, A. & Bucinskas, V. (2018). Quality of city life multiple criteria analysis. Cities, 72, 82-93.
• Klee, P. (2011). The core of GIScience: a process-based approach. Enschede, the Netherlands: ITC.
• Lafortezza, R., Carrus, G., Sanesi, G., & Davies, C. (2009). Benefits and well-being perceived by people visiting green spaces in periods of heat stress. Urban Forestry & Urban Greening, 8(2), 97-108.
• Leitner, H., Sheppard, E., Webber, S., & Colven, E. (2018). Globalizing urban resilience. Urban Geography, 39(8), 1276-1284.
• Lennon, M., & Scott, M. (2014). Delivering ecosystems services via spatial planning: reviewing the possibilities and implications of a green infrastructure approach. Town Planning Review, 85(5), 563-587.
• Liu, B. C. (1977). Quality of life indicators in US metropolitan areas, 1970: A reply. Social Indicators Research, 4(1), 107.
• Lotfi, S., Faraji, A., Hataminejad, H., & Pourahmad, A. (2011). A study of urban quality of life in a developing country. Journal of Social Sciences, 7(2), 232.
• Maleki, M., Rahmati, M., Sadidi, J., & Babaee, E. (2014, November). Landslide risk zonation using AHP method and GIS in Malaverd catchment, Kermanshah, Iran. In International Conference on Geospatial Information Research (GI Research 2014) (pp. 15-17).
• Maleki, M., Van Genderen, J. L., Tavakkoli-Sabour, S. M., Saleh, S. S., & Babaee, E. (2020). Land use/cover change in dinevar rural area of West Iran during 2000-2018 and its prediction for 2024 and 2030. Geogr. Tech, 15, 93-105. Marans, R. W., & Stimson, R. J. (Eds.). (2011). Investigating quality of urban life: Theory, methods, and empirical research (Vol. 45). Springer Science & Business Media.
• Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group decision making model. Omega, 102075.
• Murgaš, F., & Klobučník, M. (2018). Quality of life in the city, quality of urban life or well-being in the city: Conceptualization and case study. Ekológia (Bratislava), 37(2), 183-200.
• Nowak, P. (2018). Regional variety in quality of life in Poland. Oeconomia Copernicana, 9(3), 381-401.
• Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
• Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588.
• Seto, K. C., & Shepherd, J. M. (2009). Global urban land-use trends and climate impacts. Current Opinion in Environmental Sustainability, 1(1), 89-95.
• Soares Rossi Cordeiro, C. (2020). The impacts of renewal projects on urban quality of life: the case of Savassi, Belo Horizonte, Brazil. Master's thesis, University of Twente, Holland. 75p.
• Srinivasan, V. P., Palani, P. K., Dhayananthan, S., Gopi, S., Balamurugan, S., & Venkatesh, S. M. (2020). A Multi Criteria Decision Making (MCDM) based on TOPSIS and RSM for process improvement in electrical discharge machining of silicon nitride–titanium nitride ceramic composites. Materials Today: Proceedings, 45(2).
• The Whoqol Group (1998). The World Health Organization quality of life assessment (WHOQOL): development and general psychometric properties. Social science & medicine, 46(12), 1569-1585.
• Ülengin, B., Ülengin, F., & Güvenç, Ü. (2001). A multidimensional approach to urban quality of life: The case of Istanbul. European Journal of Operational Research, 130(2), 361-374.
• United Nations. (2014). World urbanization prospects: the 2014 revision, Highlights (ST/ESA/SER. A/352). New York, United, 32.
• Wang, W. M., & Peng, H. H. (2020). A fuzzy multi-criteria evaluation framework for urban sustainable development. Mathematics, 8(3), 330.
• Węglarczyk, S. (2018). Kernel density estimation and its application. In ITM Web of Conferences (Vol. 23, p. 00037). EDP Sciences.
• Woszczyk, D., & Spanakis, G. (2018, September). MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 118-133). Springer, Cham.
• Wu, F. Y., & Chuang, C. C. (2013). The optimal relationship between buyer and seller obtained using TOPSIS method. Journal of Advanced Management Science, 1(1), 133-135.
• Xu, Z., & Li, Q. (2014). Integrating the empirical models of benchmark land price and GIS technology for sustainability analysis of urban residential development. Habitat International, 44, 79-92.