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

Moghaddam, Majid Dadashpour; Ahmadzadeh, Hassan; Valizadeh, Reza

doi:10.1007/s41324-022-00439-4

Cited by 12 publications

(5 citation statements)

References 43 publications

(40 reference statements)

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“…The financial quarters and the travel plans/ year are varied in this analysis. The existing TEGM + SNA [22], AUTP-GIS [27], and DPSIR [25] methods from the related works section are used alongside the proposed method. The growth rate is efficacious in this method using the fuzzy inference system in enhancing economic stability.…”

Section: Discussionmentioning

confidence: 99%

Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism

Wang

2024

Int J Comput Intell Syst

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Urban tourism promotes the economic growth of a nation around the year through direct and indirect incomes. In recent years, the digital economy has impacted the growth of urban tourism through hassle-free money transactions and expenditures. This article, therefore, introduces a Multi-Criteria Fuzzy-based Decision-Making Method (MCFDMM) for validating the impact of the digital economy impact over tourism. The study introduces a new framework, DLFDSS-RRM, that uses deep learning and fuzzy decision support systems for residence right management, enhancing resource allocation, security, and resident satisfaction in urban residential communities. The criteria such as expenses, positive response, and repeated payments are validated by the tourists across their travel plan. These conditions satisfying the tourist’s expectations are estimated based on their reviews of economic conditions are validated. The validation is performed against the growth of the country from urban tourism. The fuzzy process validates the growth of the country between two successive financial quarters based on the above conditions. In the condition analysis, the fuzzy process identifies the least derivatives contributing to minimal economic growth. This is reversed using the hiking condition that occurs in any quarter and hinders economic growth. Therefore, the process is validated using the metrics growth rate, condition satisfaction, analysis rate, analysis time, and unrelated assessment. The comparative analysis across various models reveals growth rates ranging from 0.263 to 0.4055, condition satisfaction percentages from 53.747 to 74.351, and analysis rates from 0.275 to 0.4662.

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Section: Discussionmentioning

confidence: 99%

Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism

Wang

2024

Int J Comput Intell Syst

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“…2024, 13, x FOR PEER REVIEW 4 of modeling provides valuable information for developing future strategies for urban tou ism, the paper does not consider tourism data sources such as social media platforms, an it also overlooks various tourism objectives. Mehrdad et al [35] discuss the use of uns pervised clustering methods as data-driven models for mineral prospectivity mappin (MPM). A hybrid data-driven clustering model combines the k-means clustering alg rithm with harmony search (HS) and artificial bee colony (ABC) metaheuristic optimiz tion algorithms.…”

Section: Methodsmentioning

confidence: 99%

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

Damos,

Zhu,

et al. 2024

IJGI

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Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively.

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“…Research on tourism competitiveness also uses various methods such as ANOVA, behavioral theory, linear regression, and other methods for quantitative evaluation of urban tourism competitiveness ( 13 , 14 ). Literature ( 15 ) proposed a GIS-based model for assessing urban tourism potential. The model uses a novel hybrid modeling approach to assess the city’s tourism potential.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation model of urban tourism competitiveness in the context of sustainable development

Song,

2024

Front. Public Health

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In the contemporary context marked by globalization and the growing prominence of sustainable development, assessing urban tourism competitiveness has emerged as a crucial research domain. This paper aims to develop a comprehensive model for evaluating city tourism competitiveness, grounded in the principles of sustainable development. The model incorporates factors such as city tourism resources, environmental considerations, economic aspects, and societal factors. This holistic approach seeks to offer valuable insights for the city tourism industry. The study conducts a thorough analysis of current research both domestically and internationally, highlighting gaps and articulating the objectives and significance of the research. Employing a machine learning-based empowerment method, the paper determines the significance of evaluation indices and utilizes the Topsis method for assessing urban tourism competitiveness. Distinguishing itself from traditional evaluation methods, this model integrates the principles of sustainable development throughout the evaluation process, with environmental, social, and economic sustainability serving as pivotal evaluation indicators. Empirical analysis involves the evaluation of tourism competitiveness for select cities, facilitating inter-city comparisons. Results from empirical studies demonstrate the model’s effectiveness in evaluating urban tourism competitiveness, providing targeted developmental recommendations for urban tourism.

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

Cited by 12 publications

References 43 publications

Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism

Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

Evaluation model of urban tourism competitiveness in the context of sustainable development

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