Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model

Tourism trend prediction is useful for tourism investment and tourism income estimation. Studies on tourism prediction have mostly relied on linear models and historical visitors; however, relationships between tourism trends and their influencing factors may be nonlinear. This study took internet search data as influencing factors and predicted tourism trends using a spatial-temporal framework based on the extreme gradient boosting (XGBoost) method. To incorporate the spatial characteristics, Baidu index data were divided according to locational attributes, and influencing factors were reconstructed via spatial cluster analysis and principal component analysis. Next, variables derived from dimension reduction were further processed based on the weighted moving average to reduce the lag effect between tourism internet search and actual tourism behavior. By using the above spatial-temporal method, Baidu index data can more accurately reflect changes in tourist source composition and tourist volumes. The spatial-temporal XGBoost composite model then applied to the empirical prediction of Beijing's tourism trends. A comparison of prediction results obtained using different models indicates that the spatial-temporal XGBoost composite model has excellent prediction ability. The findings also suggest that machine learning methods may not perform well if the essential characteristics of data, such as spatial autocorrelation and spatial heterogeneity, are ignored.