As a carrier of civilization, cultural heritage reflects the dynamic relationship between humans and their environment within specific historical contexts. During the Ming and Qing Dynasties (1368–1912 CE), the Yangtze River Basin was one of the most prominent regions for economic and cultural activities in ancient China. The cultural heritage of this period was characterized by its dense distribution and continuous evolution. Considering the applicability bias of modern data in historical interpretation, this study selected four characteristic variables: population density, agricultural productivity, technological level, and temperature anomaly. A hierarchical Bayesian model was constructed and change points were detected to quantitatively analyze the driving mechanisms behind the spatiotemporal distribution of cultural heritage. The results show the following: (1) The distribution of cultural heritage exhibited a multipolar trend by the mid-period in both Dynasties, with high-density areas contracting in the later period. (2) Agricultural productivity consistently had a significant positive impact, while population density also had a significant positive impact, except during the mid-Ming period. (3) The cultural calibration terms, which account for observational differences resulting from the interaction between cultural systems and environmental variables, exhibited slight variations. (4) The change point for population density was 364.83 people/km², and for agricultural productivity it was 2.86 × 10⁹ kJ/km². This study confirms that the differentiation in the spatiotemporal distribution of cultural heritage is driven by the synergistic effects of population and resources. This provides a new perspective for researching human–land relations in a cross-cultural context.

Cultural heritage systems play a crucial role in decoding human–environment interactions and social evolution. This study aims to reveal the spatial coupling characteristics of tangible and intangible cultural heritage in China, as well as the heterogeneity of their driving mechanisms. After quantifying heritage coupling at three geographic scales, we integrated a hierarchical Bayesian model with a hybrid causal inference framework to identify the correlations, causal effects, and heterogeneity of the driving factors. The empirical results indicate the following: (1) The coupling patterns exhibit scale dependence. The proportion of strongly coupled areas decreases from the prefecture-level scale to the provincial scale but increases at the cultural–geographical unit scale. This suggests that China’s cultural system has a cohesive effect that transcends administrative boundaries. (2) The hierarchical Bayesian model identifies the significant effects of mean annual temperature, population density, GDP–population interaction, transport–hydrological network interaction, and industrial structure. Effect strengths generally peak at the prefecture-level scale and decrease at the provincial scale. (3) Causal inference estimates the causal effects of mean annual temperature, transport–hydrological network interaction, mean annual precipitation, and water network density on coupling. (4) Heterogeneity tests reveal that the positive causal effect of transport–hydrological network interaction and the negative causal effect of mean annual precipitation are significant only in low-temperature regions. By integrating hierarchical modeling with causal verification, this study elucidates the mechanisms underlying heritage coupling. This provides a scientific basis for understanding the spatial patterns of cultural heritage systems and formulating differentiated conservation policies.

Urban greening studies have traditionally emphasized vegetation quantity, while the perceptual implications of horticultural composition and spatial configuration remain less clearly understood. This study investigated how horticultural configurations were associated with multidimensional landscape perception across 10 urban parks in Chengdu, China. A total of 500 standardized landscape images, comprising 50 images per park, were assessed by 150 participants across 17 perceptual dimensions. Vegetation composition, spatial configuration, and conventional greenness indicators were quantified from the images and park-level spatial data. Principal component analysis and PERMANOVA were used to characterize structural gradients among parks, while exploratory factor analysis was used to identify integrated perceptual domains. XGBoost models with five-fold cross-validation were subsequently developed to predict individual perceptual outcomes, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions and nonlinear response patterns. Multi-objective optimization was further used to explore hypothetical configuration scenarios involving trade-offs among perceptual objectives. The first two structural principal components explained 65.60% of the variation in horticultural composition and spatial organization, and park configurations differed significantly (PERMANOVA: F = 166.66, p = 0.001). Horticultural typologies differed across 15 of the 17 perceptual dimensions (all p < 0.001), with the largest differentiation observed for nature (ε² = 0.270), species richness (ε² = 0.259), openness (ε² = 0.251), being away (ε² = 0.245), serenity (ε² = 0.236), and enclosure (ε² = 0.234). The XGBoost models showed consistently strong predictive performance, with test-set R² values of 0.692–0.812 and cross-validated R² values of 0.756–0.796. SHAP analyses indicated differentiated and nonlinear associations: vegetation continuity and enclosure were primarily related to restorative immersion, open configurations to spatial accessibility, and ornamental and compositional complexity to affective richness. The optimization results further identified continuous trade-offs among restorative immersion, spatial openness, and affective richness, rather than a single universally optimal configuration. These findings show that landscape perception is associated with the spatial organization and composition of horticultural elements through multidimensional and nonlinear pathways. The study therefore extends vegetation-quantity assessments by providing a configuration-based analytical framework for exploring perceptually balanced urban horticultural environments.