Spatial Heterogeneity in the Local Clustering of Traditional Villages in Southern Zhejiang, China: Insights from Random Forest–SHAP and Multiscale Spatial Regression Models

This study develops a scale-aware and reproducible framework for examining the local clustering intensity of 612 recognized traditional villages in southern Zhejiang, China. Equal-weight Gaussian leave-one-out kernel density estimation (LOO-KDE; h = 21.01 km) excluded each focal village’s direct self-contribution, while explicitly acknowledging shared-neighbour dependence among the derived outcomes. Village-scale models used the same X1–X12 predictor set after correlation and variance-inflation screening; county socioeconomic indicators were analyzed separately for the 27 represented county-level units. A Random Forest selected from 144 prespecified candidates achieved an out-of-bag R2 of 0.353 and a county-grouped pooled R2 of 0.115. Repeated spatial holdouts yielded weak transferability: mean R2 was −0.081 for coordinate K-means and 0.082 for shifted 50 km grids without buffers, and became negative for both designs with one- or two-bandwidth buffers. SHAP ranked elevation, topographic relief, and village household count as the leading village-scale predictive contributors. OLS explained 30.5% of outcome variance, whereas SLM and SEM results depended strongly on whether structural/filtered fits or covariate-only predictions were evaluated. GWR and MGWR reached R2 = 0.9743 and 0.9984, and residual Moran’s I remained significant for all models except MGWR (I = 0.002, p = 0.429). At the county scale, rural disposable income and tertiary-industry output were negatively correlated with mean LOO-KDE, but multivariable HC3 estimates were imprecise and ecologically interpreted. The results identify associations with the clustering of recognized villages, not causes of individual-village heritage persistence.