An Integrated Data Mining and GIS Approach for Assessing Tourism Potential in Thailand’s Less Visited Areas

Abstract

Less visited areas are destinations that do not qualify as primary tourist attractions and have yet to attract large numbers of visitors. These areas have potential for development based on their strengths in culture, agriculture, community products, tourism, and food. They also provide convenient transportation and accessibility. In Thailand, there are 55 provinces classified as Less visited areas. This study aims to classify provinces by their social and economic potential using clustering techniques and to create a GIS model that maps secondary tourist destinations by potential groups. Data was collected from social media using web scraping methods, covering 2010-2019. The study calculated average values for three economic and social factors: provincial population, the number of tourists visiting each province, and tourism revenue per province. This data was then analyzed using data mining techniques and clustering methods to classify the tourism potential of Less visited areas into three levels: high, medium, and low potential, employing the K-Means clustering algorithm. Subsequently, a map model of secondary tourism province groups was developed for each region based on potential levels using a geographic information system.

The results show that the high potential group includes provinces located in the Northeast (5 provinces) and Central regions (4 provinces), as well as one province each from the North, East, West, and South regions. The medium group consists of provinces in the Northeast (11 provinces), North (7 provinces), Central (5 provinces), and West (1 province) regions. The low potential group represents provinces in the South (8 provinces), Central (3 provinces), East (3 provinces), and Northeast (2 provinces) regions.