Algorithmic Assessment of Samarkand Attractiveness and Its Strategic Role in Agritourism Development

General Background: The integration of tourism and agriculture has increased the importance of territorial attractiveness as a determinant of sustainable agrotourism development. Specific Background: Samarkand combines significant agricultural resources, cultural heritage, and expanding tourism activity, yet its agrotourism attractiveness has not been systematically quantified for strategic planning purposes. Knowledge Gap: Existing assessments rely on fragmented indicators and qualitative descriptions, limiting comparative analysis and evidence-based decision-making. Aims: This study aims to develop and apply an algorithmic framework to assess the attractiveness of the Samarkand region and to examine its strategic importance for agrotourism development. Results: The findings indicate that Samarkand’s agrotourism attractiveness emerges from the interaction of economic potential, agricultural and natural resources, tourism infrastructure, socio-cultural assets, and institutional support rather than isolated factors. Novelty: The study introduces a multidimensional, indicator-based algorithm that integrates quantitative normalization, hybrid weighting, and expert evaluation into a single composite attractiveness index. Implications: The proposed framework supports evidence-driven regional planning, investment prioritization, and policy formulation, while offering a transferable model for assessing agrotourism attractiveness in regions with similar socio-economic and agricultural characteristics.Keywords : Territorial Attractiveness, Agritourism Development, Assessment Algorithm, Samarkand Region, Strategic PlanningHighlight : Algorithm integrates five dimensions: economic, agricultural, infrastructural, socio-cultural, and institutional indicators Multicriteria evaluation combines quantitative normalization with expert-based weighting and sensitivity testing Framework enables longitudinal monitoring and comparative assessment across districts and regions

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