The Spatial Network of the Tourism Economy in Iranian Provinces (Case Study: Kerman Province)

This study investigates the spatial network of the tourism economy in urban areas of Iran and analyzes the tourism network in Kerman province.

Design/methodology/approach: 

In the analysis of tourism in different regions of Iran, the gravity model of the tourism economy is employed using econometric panel data models. In the spatial network of the tourism economy in Kerman province, network indices are calculated using Ucinet software. The statistical population of this research includes the provinces of Iran and the counties of Kerman province. The statistical samples in the national and Kerman province sections are from 2011 to 2018 and 2018 to 2021, respectively.

The findings of the study reveal that the greater the distance between two tourist destinations, the stronger the economic connection in tourism becomes. This is because cities compete with each other economically to sustain their growth. In other words, the longer the distance, the more investment and higher economic connectivity a city requires to independently meet the needs of its residents and tourists. The cities of Kerman, Sirjan, Rafsanjan, Jiroft, and Bam, as core nodes, have a higher level of influential prestige and a greater impact on indicators compared to dependent nodes. They can act as core nodes and contribute to the progress and increased collaboration with neighboring cities.

Given that variables such as ecotourism, tourist attractions, and visitor numbers are central to the network, provincial managers are advised to prioritize the development of ecotourism in all areas of Kerman province. Therefore, relevant tourism organizations should create opportunities for promoting and introducing the tourist attractions of the province nationally and internationally, considering the significant impact of tourist attractions.

Originality/value: 

One of the originalities of this study is the estimation of the gravity model of tourism for urban areas in Iran and the utilization of network analysis in the spatial network of the tourism economy in Kerman province.