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<p>This study evaluates smart tourism readiness in 18 coastal cities along the southern Caspian Sea in Guilan Province, Iran, using an integrated geospatial and multi-criteria decision-making framework. Guided by six smart tourism dimensions—economy, governance, infrastructure, education, people, and connectivity—seventeen indicators were measured via a Likert-scale questionnaire (<i>n</i> = 384) and validated with Cronbach’s alpha (0.761–0.884) and Exploratory Factor Analysis. Objective indicator weights were computed using the CRITIC method, and cities were ranked with the COCOSO model. GIS-based spatial analyses (Global Moran’s I, LISA, Getis-Ord Gi*) identified clustering patterns and north–south disparities in readiness. Sensitivity analysis (Spearman’s ρ = 0.957, <i>p</i> &lt; 0.001) confirmed ranking robustness. Results indicate significant spatial inequalities: Talesh, Bandar Anzali, and Asalem rank highest due to strong governance, digital infrastructure, and civic awareness, while Lavandevil and Paresar rank lowest, reflecting institutional and technological constraints. Findings highlight non-random spatial clustering and a performance gradient across the study area. By combining perception-based indicators, MCDM weighting, and spatial analysis, this study provides a theoretically informed and empirically robust framework for evidence-based planning and sustainable development of smart tourism in environmentally sensitive coastal regions.</p>

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Smart tourism potential mapping using geospatial and MCDM in the southern Caspian Sea region of Iran

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月21日

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  • Zahra BidarighMehr,
  • Mehrdad Mehrjou

摘要

This study evaluates smart tourism readiness in 18 coastal cities along the southern Caspian Sea in Guilan Province, Iran, using an integrated geospatial and multi-criteria decision-making framework. Guided by six smart tourism dimensions—economy, governance, infrastructure, education, people, and connectivity—seventeen indicators were measured via a Likert-scale questionnaire (n = 384) and validated with Cronbach’s alpha (0.761–0.884) and Exploratory Factor Analysis. Objective indicator weights were computed using the CRITIC method, and cities were ranked with the COCOSO model. GIS-based spatial analyses (Global Moran’s I, LISA, Getis-Ord Gi*) identified clustering patterns and north–south disparities in readiness. Sensitivity analysis (Spearman’s ρ = 0.957, p < 0.001) confirmed ranking robustness. Results indicate significant spatial inequalities: Talesh, Bandar Anzali, and Asalem rank highest due to strong governance, digital infrastructure, and civic awareness, while Lavandevil and Paresar rank lowest, reflecting institutional and technological constraints. Findings highlight non-random spatial clustering and a performance gradient across the study area. By combining perception-based indicators, MCDM weighting, and spatial analysis, this study provides a theoretically informed and empirically robust framework for evidence-based planning and sustainable development of smart tourism in environmentally sensitive coastal regions.