A Study on the Influencing Factors of Tourism Economy in Shanghai Based on Principal Component Analysis

This paper analyzes the value added of tourism in Shanghai, China as an explanatory variable, and analyzes it from the aspects of economy, service, tourist preference, transportation and telecommunications, and environment, and then selects 14 indicators. We used data from 2000 to 2017, using R to conduct a principal component regression analysis of the Shanghai tourism economy, and tested the residuals. Due to the autocorrelation of the residuals, we chose to use the generalized least squares method for correction. It can be seen from the analysis that the economic status of tourist destinations, the number of travel agencies, the number of tourists, the number of mobile phone users, the weighted scores of star-rated hotels, and the environmental quality of travel destinations have an effect on the tourism economy of Shanghai in 18 years, which is from strong to weak. The results show that the regression model established by the two principal components as independent variables has a good fitting effect. The model established by principal component regression analysis has certain validity and has certain value for predicting the added value of tourism.