Exploring the spatial pattern and its influencing mechanism of cultural heritage is of great significance for the systematic protection, inheritance and utilization of cultural heritage. In this study, the national-level cultural heritage of China as the research object, and the spatial patterns of the cultural heritage (CH), tangible cultural heritage (TCH) and intangible cultural heritage (ICH) are analyzed are summarized using the multiple geospatial analysis method, and their spatial distribution differences. Then, the optimal parameter geodetector model was further used to analyze the influencing and interaction effect of natural environment (NE) factors, socioeconomic and cultural environment (SCE) factors on the spatial distribution of CH, TCH and ICH, and the interaction mechanism was deeply discussed. The results show that high-density region of CH are mainly located in the southeastern Heihe-Tengchong Line of China, while low-density region are mostly located in the northwestern Heihe-Tengchong Line. In terms of local spatial differences, the spatial distribution of material cultural heritage is different in different river basins, different terrains and different climates. The spatial distribution of ICH mainly shows significant differences in terrain and climate, and does not show significant differences in river basins. In terms of influencing factors, sociocultural environment, education level, historical settlement environment, road traffic, climate, economy, population and terrain factors all have a significant impact on the spatial distribution of CH and TCH. Sociocultural environment, education level, historical settlement environment, road traffic, climate, economy and population have a significant impact on the spatial distribution of ICH, while topographic factors and water system factors have no significant impact on the spatial distribution of ICH. Compared with ICH, TCH is more likely to be directly affected by topographic conditions (altitude, relief of terrain) and hydrological conditions (production and living water sources and flood risk) during its formation stage. At the same time, topographic and hydrological conditions can also indirectly affect the spatial distribution of TCH by directly affecting human agricultural activities, economic activities and cultural activities.

Exploring the influencing factors and its influencing mechanism of the spatial coupling between rural human-habitat heritage (RHH) and key rural tourism villages (RTV) at county scale from the perspective of space can expand the theoretical research on the spatial coupling mechanism between RHH and RTV, and further provide theoretical reference and data support for the coordinated development and high-quality development of RHH and RTV in China. At the same time, previous studies have failed to systematically analyze the influencing factors and influencing mechanisms of the spatial coupling between RHH and RTV at the county scale, which restricted decision makers from formulating coordinated development measures between RHH and RTV at the macro level. In this study, bivariate spatial autocorrelation model and spatial coupling coordination model were used to quantitatively analyze the spatial coupling level between RHH and RTV at the county scale in China. Then, the linear regression (OLS) model, geographically weighted regression (GWR) model, and optimal parameter GeoDetector (OPGD) model were integrated to systematically analyze the linear influencing, spatial heterogeneity effect and interactive effect of natural environment and socioeconomic factors on the spatial coupling level between RHH and RTV in China, and explore the interactive influencing mechanism. The results show that the spatial coupling level of RHH and RTV in China show a significant east-west differentiation. There were 2024, 473, 293, 55 and 6 areas of severe, moderate, mild, basic and moderate coordination between RHH and RTV, respectively. Among them, severe and moderate discoordination areas are mainly distributed in Northeast China, arid and semi-arid areas in Western China, plateau areas in Southwest China, densely populated urban agglomerations and plains agricultural areas in the Middle East China. Mild discoordination areas and basic and moderate coordination areas are mainly located in transition zones in mountainous and plain areas, economically developed mountainous and hilly counties along the southeastern coast, and coastal tourist cities. Economic and population factors are the fundamental factors that affect the spatial coupling between RHH and RTV. Rural tourism facilities and rural public service facilities are important external driving forces for the coupling development of RHH and RTV, and Sociocultural environment factors are the important internal driving forces. Different surface forms, different climate conditions and different ecological environment conditions can form different natural textures and spatial organizations. Suitable climate conditions, sufficient water sources and ecological environment conditions can form more suitable rural settlement construction conditions and production and living conditions, and ultimately affect the protection and activation of rural human settlement heritage and the development and layout of key tourist villages. The spatial coupling relationship between RHH and RTV is the result of the complex interaction between the natural directivity law caused by natural environmental factors and the humanistic directivity law caused by human social and economic activities.

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

Scientific identification of the network of heritage tourism corridors is of great significance for the revitalization of regional cultural heritage and the optimization of the heritage tourism destination system. Taking the Yellow River Cultural Tourism Belt (YRCTB) of China as the research area, this study constructs a potential cultural heritage tourism corridor network and classifies it, then uses virtual tourism flow data to verify its scientificity and rationality, and proposes optimization strategies. Key findings include: The YRCTB has two belt-like clusters of cultural heritages, eight multi-core clusters and seven single-core clusters. The YRCTB has identified 40 sources of heritage tourism and 106 potential heritage tourism corridors. Based on the gravity model, 106 cultural heritage tourism corridors were classified into five levels, forming a hierarchical structure system of “9 + 14 + 28 + 28 + 27”. The comparison results show that heritage tourism corridors receive a medium to high level of response in the virtual tourism flow network.