Scenario-Based Assessment of Agritourism Development Using Hierarchical Principal Component Analysis: A Case Study of Iran

This dataset contains the spatial input data, processing codes, and model outputs developed for the study “Scenario-Based Assessment of Agritourism Development Using Hierarchical Principal Component Analysis: A Case Study of Iran”. It supports a national-scale, multi-criteria spatial assessment of agritourism development potential using a scenario-based Hierarchical Principal Component Analysis (HPCA) framework. The analysis integrates five main criteria: accessibility, risk and safety, environmental conditions, tourism attractions, and tourism facilities. Spatial sub-criteria layers were compiled from national geographic, climatic, topographic, and infrastructure datasets and standardized using min–max normalization. A two-level HPCA approach was applied. At Level 1, PCA was conducted separately for the sub-criteria of each main criterion to reduce dimensionality and generate criterion-level potential maps. At Level 2, PCA was applied to these criterion-level maps to determine their relative contributions and produce the final national agritourism development potential map. Model robustness was evaluated through a weight-based sensitivity analysis. In addition, scenario analysis was performed to explore the effects of alternative planning priorities under development-oriented, environment-oriented, and risk-oriented scenarios, highlighting spatial shifts in high-potential areas. The dataset is structured to ensure full reproducibility and includes both intermediate and final outputs. The dataset includes: - Standardized spatial sub-criteria layers for the five main criteria - Level 1 PCA outputs (loadings, explained variance, criterion-level maps) - Level 2 PCA outputs and the final agritourism potential map - Sensitivity analysis results and spatial stability outputs - Scenario-based agritourism potential maps (development-, environment-, and risk-oriented) - Scripts and codes for PCA, HPCA integration, sensitivity analysis, and scenario modeling This dataset provides a transferable spatial decision-support framework for agritourism planning and sustainable rural development.