Tourism planning
shohreh moradpour; majid ghias
Abstract
This research leverages machine learning techniques to predict promising tourist destinations in the western region of Isfahan Province. A random forest prediction model is employed to identify the factors influencing tourist attraction in these areas and subsequently predict potential tourist hotspots. ...
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This research leverages machine learning techniques to predict promising tourist destinations in the western region of Isfahan Province. A random forest prediction model is employed to identify the factors influencing tourist attraction in these areas and subsequently predict potential tourist hotspots. The input data encompasses 34 sites, including springs, waterfalls, shrines, museums, historical structures, and more. Variables such as elevation, slope, aspect, vegetation cover, distance to rivers, distance to roads, temperature, and precipitation were selected as the most significant predictive variables based on the Variance Inflation Factor (VIF). Evaluation metrics reveal that the random forest model exhibits superior predictive performance, achieving the highest R² (0.91) and the lowest RMSE (1.06) and MAE (1.13). Slope, slope aspect, vegetation cover, and distance to rivers and roads were identified as the most critical predictors. Moreover, the spatial prediction map indicates that Chadgan and daran possess high potential for tourist reception.
Tourism planning
Naser Shafieisabet; Faranak feyz babaei cheshmeh sefidi
Abstract
In the subject of tourism, clusters, as a group of businesses and centers related to tourism, seek to increase competitiveness and development through cooperation and interaction. Given more than five decades of studies on tourism clusters and regional development and the lack of a comprehensive review ...
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In the subject of tourism, clusters, as a group of businesses and centers related to tourism, seek to increase competitiveness and development through cooperation and interaction. Given more than five decades of studies on tourism clusters and regional development and the lack of a comprehensive review of previous research, the main goal of this research is to conduct a quantitative review of these studies using bibliometric analysis tools. In this regard, articles related to this topic from 1988 to 2024 were reviewed, and knowledge mapping and performance analysis were performed on 333 articles in Vos viewer software. The findings showed that the number of published articles was the highest during the past decade, and authors such as Jackson and Murphy had the highest scientific citations. Also, word co-occurrence analysis revealed five research clusters whose core keywords include tourism, cluster analysis, regional planning, competitiveness, and tourism development.
