Estimation of the spatial climate comfort distribution using tourism climate index (TCI) and inverse distance weighting (IDW) (case study: Fars Province, Iran)

This article was conducted to perform a temporal and spatial analysis in order to identify suitable climatic regions for tourism. We investigated tourism climate conditions in Fars province from 2006 to 2016 using tourism climate index (TCI). Also, modified inverse distance weighting (IDW) interpolation is applied to generate the optimal spatial pattern of the TCI distribution. The relationship between the interpolation accuracy and a critical IDW parameter, called power value (β), was evaluated for optimization. The results revealed that during four months of May, April, October, and November, 70–83% of cities in Fars province show excellent and ideal climatic comfort. In the four months of July, December, January, and March, about 45–54% of Fars province provide good and very good conditions for tourism activities. The spatial distribution of TCI also shows that the cities in the northern part generally have the most desirable conditions during the hot season, while the southern cities of Fars province are more suitable for tourism during the cold season. Also, analysis of optimization steps demonstrated that power value (β) affects interpolation accuracy. As our study suggests, using the optimal power values (β) of 1 and 2 can lead to optimal spatial interpolation of the TCI distribution. Overall, we found IDW and TCI as reliable tools for assessing bioclimatic comfort conditions, considering β-value as an influential factor that should be evaluated to achieve optimal interpolation results. © 2021, The Author(s).