Spatial Mismatch Patterns and Nonlinear Associations Between Tourism Resources and Tourism Vitality at the County Level in Heilongjiang Province, China

The spatial mismatch between tourism resources and tourism vitality represents one of the key constraints on the high-quality development of regional tourism. Taking 77 county-level administrative units in Heilongjiang Province as the study area, this research utilizes panel data from 2019 to 2024 to evaluate tourism resource endowment and tourism vitality through the entropy-weighted TOPSIS method. A coupling coordination degree model, quadrant classification approach, and spatial autocorrelation analysis are employed to identify spatial mismatch patterns. Furthermore, a progressive analytical framework integrating XGBoost-based nonlinear modeling, SHAP interpretability analysis, and Geodetector-based spatial validation is constructed to identify the main explanatory factors and nonlinear associations related to these mismatch patterns. The results reveal that: (1) significant spatial mismatches exist between tourism resources and tourism vitality across counties in Heilongjiang Province. The overall coupling coordination degree remains at a low coordination level and exhibits a distinct core–periphery spatial structure; (2) four categories of mismatch units are identified, including high high matching, resource-leading mismatch, vitality-leading mismatch, and low low matching types. These categories display pronounced spatial clustering characteristics, with a sharp contrast between the high-value clusters in the Harbin metropolitan area and border regions and the low-value clusters in western Suihua; (3) the number of hotels shows the highest explanatory contribution in the model, showing a clear threshold-like pattern, while border ports demonstrate a sparse but important association pattern; (4) strong interaction relationships exist among explanatory variables, with nearly 30% of the model’s explanatory power originating from synergistic multi-factor interactions; and (5) Geodetector analysis further confirms the spatial explanatory power of the major explanatory factors and the significance of multivariate interaction effects. This study provides policy references for optimizing the allocation of tourism resources and enhancing tourism vitality in underdeveloped border regions.