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      A GIS-Based Assessment of Urban Tourism Potential with a Branding Approach Utilizing Hybrid Modeling

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      https://www.riss.kr/link?id=A108504499

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      다국어 초록 (Multilingual Abstract)

      Urban tourism has developed into one of the most efficient and effective economic and recreational activities in the modern era since the late second half of the twentieth century. Although attention to urban tourism development is increasing, this paper indicates that no comprehensive study has yet combined the two subjects of tourism development and branding for spatial modeling.
      Thus, for assessing urban tourism potential (AUTP), a novel hybrid modeling approach combining K-mean, fuzzy logic, and an artificial neural network (ANN) was used.
      The findings indicate that in Tabriz metropolis, areas 3 and 7 and the southwestern portion of region 6 have the most significant potential for urban tourism. In contrast, areas 4, 8, and 10 located within the worn-out urban fabric have the least potential for tourism. Subsequently, by examining the correlation between urban tourism conditioning factors (UTCFs) and tourism maps, it was determined that factors including distance from the catering centers, distance from the fault, quality of construction materials, distance from the historical centers, distance from the health centers, maximum temperature, and distance from the parking are the most critical in terms of increasing urban tourism’s potential and branding. The analyses conducted in this study provide valuable and practical information for developing future strategies urban tourism. To this end, recommendations have been made to enhance tourism destination service delivery and management through branding.
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      Urban tourism has developed into one of the most efficient and effective economic and recreational activities in the modern era since the late second half of the twentieth century. Although attention to urban tourism development is increasing, this pa...

      Urban tourism has developed into one of the most efficient and effective economic and recreational activities in the modern era since the late second half of the twentieth century. Although attention to urban tourism development is increasing, this paper indicates that no comprehensive study has yet combined the two subjects of tourism development and branding for spatial modeling.
      Thus, for assessing urban tourism potential (AUTP), a novel hybrid modeling approach combining K-mean, fuzzy logic, and an artificial neural network (ANN) was used.
      The findings indicate that in Tabriz metropolis, areas 3 and 7 and the southwestern portion of region 6 have the most significant potential for urban tourism. In contrast, areas 4, 8, and 10 located within the worn-out urban fabric have the least potential for tourism. Subsequently, by examining the correlation between urban tourism conditioning factors (UTCFs) and tourism maps, it was determined that factors including distance from the catering centers, distance from the fault, quality of construction materials, distance from the historical centers, distance from the health centers, maximum temperature, and distance from the parking are the most critical in terms of increasing urban tourism’s potential and branding. The analyses conducted in this study provide valuable and practical information for developing future strategies urban tourism. To this end, recommendations have been made to enhance tourism destination service delivery and management through branding.

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      참고문헌 (Reference) 논문관계도

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      3 Novy, J, "Urban tourism as a bone of contention : Four explanatory hypotheses and a caveat" 5 (5): 63-74, 2019

      4 Calle-Vaquero, M. D. L, "Urban planning regulations for tourism in the context of overtourism Applications in historic centres" 13 (13): 70-, 2021

      5 Braun, E, "Towards an integrated approach for place brand management" European Regional Science Association (ERSA) 2010

      6 Al-Jabi, M, "Toward mobile arbased interactive smart parking system" 2018

      7 Atzori, R, "Tourist responses to climate change : Potential impacts and adaptation in Florida’s coastal destinations" 69 : 12-22, 2018

      8 Jenkins, C. L, "Tourism policy and planning for developing countries : Some critical issues" 40 (40): 144-156, 2015

      9 Pasquinelli, C, "Tourism in the city" Springer 1-25, 2017

      10 Blasco, D, "Tourism destination zoning in mountain regions : A consumer-based approach" 16 (16): 512-528, 2014

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      21 Gretzel, U, "Smart tourism cities : A duality of place where technology supports the convergence of touristic and residential experiences" 26 (26): 352-364, 2021

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      24 Panasiuk, A, "Policy of sustainable development of urban tourism" 27 (27): 33-37, 2020

      25 Yariyan, P, "Optimization of statistical and machine learning hybrid models for groundwater potential mapping" 1-35, 2020

      26 Ong, J. L. T, "Modelling urban tourism in historic Southeast Asian Cities" 5 (5): 38-, 2021

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      32 Pham, Q. B, "Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm" 24 (24): 18039-18056, 2020

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      35 Yariyan, P, "GIS-based seismic vulnerability mapping: a comparison of artificial neural networks hybrid models" 1-24, 2021

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      37 Yariyan, P, "Exploitation of MCDA to learn the radial base neural network (RBFNN) aim physical and social vulnerability analysis versus the earthquake (case study: Sanandaj City, Iran)" International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 2019

      38 Yariyan, P, "Earthquake vulnerability mapping using different hybrid models" 12 (12): 405-, 2020

      39 Yariyan, P, "Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS : A case study of Sanandaj in Iran" 50 : 101705-, 2020

      40 Bhavani, M. S, "Distributing 5G cell sites optimally using K-means and spectral clustering" 2019

      41 Guerra-Montenegro, J, "Computational Intelligence in the hospitality industry : A systematic literature review and a prospect of challenges" 102 : 107082-, 2021

      42 Chan, A, "City image : City branding and city identity strategies" 10 : 330-341, 2021

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      44 Tenforde, M. W, "Characteristics of adult outpatients and inpatients with COVID-19—11 academic medical centers, United States, March–May 2020" 69 (69): 841-, 2020

      45 Tapak, L, "Assessment of factors affecting tourism satisfaction using K-nearest neighborhood and random forest models" 12 (12): 1-5, 2019

      46 Moghani, B, "Assessing the Domestic Tourists’ Sense of Security and Comfort (Case Study: The Coastal City of Bandar Ganaveh)" 4 (4): 107-121, 2018

      47 Hussain, H. I, "Artificial Neural Network to model managerial timing decision : Non-linear evidence of deviation from target leverage" 12 (12): 1282-, 2019

      48 McBratney, A. B, "Application of fuzzy sets in soil science: Fuzzy logic, fuzzy measurements and fuzzy decisions" 77 (77): 85-113, 1997

      49 Ghaed Rahmati, S, "Analysis of urban tourism spatial pattern(case study : Urban tourism space of Isfahan city)" 50 (50): 945-961, 2018

      50 Pearce, D. G, "An integrative framework for urban tourism research" 28 (28): 926-946, 2001

      51 Chekalina, T, "A value co-creation perspective on customer-based brand equity modelling for tourism destinations: A case from Sweden" Mittuniversitetet 2015

      52 Li, M, "A spatial–temporal analysis of hotels in urban tourism destination" 45 : 34-43, 2015

      53 Liaghat, M, "A multi-criteria evaluation using the analytic hierarchy process technique to analyze coastal tourism sites" 5 : 479-485, 2013

      54 Alizadeh, M, "A hybrid analytic network process and artificial neural network(ANP-ANN)model for urban earthquake vulnerability assessment" 10 (10): 975-, 2018

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