Structure of mobile phone data.

This study used mobile phone data to examine the spatiotemporal patterns and preferences of domestic visitors to Jeju Island, South Korea and integrated hourly floating population data to examine visitor characteristics by season, day of the week, time of the day, and inflow regions; identified hotspots at the census block level; and involved spatial autocorrelation analysis. The primary findings indicate that visitor numbers vary significantly by season, with summer and winter seasons attracting the highest number of tourists. The analysis revealed a concentration of visitors around Jeju Airport and the Jungmun Tourist Complex, suggesting that these are key hotspots. The inflow analysis underscores a dominant influx from major urban centers, particularly Seoul and Gyeonggi Province. The Global Moran’s Index confirmed a positive spatial autocorrelation across all seasons, with the strongest correlation observed in winter. The local spatial autocorrelation analysis identified significant clustering in hotspots, highlighting spatial interdependencies critical for marketing and infrastructure planning. This study uses big data analytics to advance the understanding of tourist behavior, offering critical insights for crafting sustainable tourism strategies and policies that align with demographic, temporal, and seasonal dynamics. The findings not only contribute to current empirical research but could also aid local governments and stakeholders in optimizing tourism management and enhancing visitor experiences on Jeju Island.