Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture

Abstract

Objective Dali is a city rich in tourism resources and cultural heritage, where residents’ subjective well-being (SWB) varies in response to the dynamics of local tourism culture. Few studies have examined the distribution of SWB levels and their influencing factors in areas where modern tourism economies and traditional cultures coexist. The study aims to explore the relationship between multiple variables and SWB, and rank the importance of key well-being factors. Methods This study employed a convenience sampling method to survey permanent residents of Dali City, resulting in a final dataset of 483 valid samples. Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. The model’s performance was evaluated using the area under the curve (AUC) metric. Generalized additive models (GAMs) were used in sensitivity analyses to assess potential nonlinear relationships between predictors and SWB. Results The probability of high SWB in Dali City was 48.9%. RF demonstrated the highest predictive accuracy (AUC = 0.82). By ranking the importance of variables in the best model RF, we obtain the top five predictors of SWB as: frequency of health issues affecting daily activities, family economic status, age, income, and weekly family face-to-face communication. GAMs explained 55.2% of the variance in SWB (R2 = 0.552, N = 483). Fewer health issues affecting daily life were strongly associated with higher SWB (B = 4.83–6.39, p < 0.001). Better family economic status (B = 1.37–2.24, p < 0.001) and greater trust in society (B = 0.92–1.39, p < 0.01) also predicted higher SWB. Age showed a positive association with SWB scores (EDF = 1.00, p < 0.001). Conclusions This study shifts the focus from economic outcomes to residents’ SWB in a culturally diverse tourism setting. Using machine learning and GAMs, health issues emerged as the strongest predictors of SWB. Findings support health-oriented tourism strategies and highlight the need to integrate socio-cultural factors into sustainable tourism planning.