This paper provides an analysis of China’s land border cultural heritage, aiming to offer guidance for its protection and development. The study compiled a comprehensive database of heritage sites and analyzed their mathematical characteristics based on quantity, type, and quality. The spatial pattern of the cultural heritage was described using kernel density analysis, spatial variability, and spatial correlation, while the driving mechanism was measured based on Geo-Detector model. The findings revealed that the land border areas of China have a total of 1227 cultural heritage, comprising two main types and 18 subtypes. National cultural heritage accounted for 22.74%, with ancient city ruins being the largest type of cultural heritage. The Southwest and Northeast regions of China exhibited high-high clusters and low-low clusters, respectively. Moreover, the study identified history of administrative succession, evolution of historical and cultural, altitude, distance from the city, population density, minority population ratio, urbanization process, and per capita GDP as significant factors impacting the spatial pattern of land border cultural heritage in China. Based on these findings, the study suggests that the government should invest more in border cultural heritage, improve transportation infrastructure in border areas, ensure dynamic inheritance and protection of cultural heritage, and promote the joint development of different types of cultural heritage. The research results will provide data support and decision-making reference for the sustainable development of cultural heritage in land border areas, and can provide a new perspective for the management of cultural heritage in similar areas.

This paper evaluated the regional tourism competitiveness of 22 county-level administrative regions in Ningxia Hui Autonomous Region, China. The study analyzed the connotation of regional tourism competitiveness from the perspectives of resources and economic and environmental benefits, and established an evaluation index system for comprehensive competitiveness. The study used entropy weight TOPSIS to measure the level of tourism competitiveness in Ningxia, and used spatial association and hot and cold analysis to describe the spatial pattern of comprehensive tourism competitiveness in Ningxia. The results indicated that Lingwu City, Xixia District, and Huinong District have high comprehensive tourism competitiveness, while Jingyuan County, Haiyuan County, and Hongsipu District exhibit the weakest comprehensive competitiveness. The study also showed that regional economic development level competitiveness had the greatest impact on the comprehensive competitiveness of county tourism in Ningxia. The northern region and the southern region of Ningxia were the regions with high-high clusters and low-low clusters of tourism competitiveness, respectively. Based on the comprehensive evaluation results, the development level of tourism of Ningxia county was classified into four types: advanced development type, marginal dependence type, improved optimization type, and backward development type. Finally, the paper put forward targeted optimization suggestions in order to provide reference for Ningxia tourism functional zoning and high-quality development.

With the in-depth development of the Western Development Strategy and Rural Revitalization, the tourism industry has become an important economic sector to drive local development in northwestern minority areas of China. This study established a database of tourism resources in Longde county and analyzed the spatial pattern of these tourism resources by using the scale index and kernel density. From the perspective of the natural and social environment, this paper quantitatively discusses the influencing factors of the spatial pattern of tourism resources in the county based on a multiple linear regression model. The results showed that: (1) There were 2049 tourism resources distributed in Longde County, and building and facility resources were the most prevalent. (2) The hierarchical structure of tourism resources in the county showed a “pyramid” distribution, where excellent tourism resources accounted for 22.60% of the total resources. (3) The spatial pattern showed an agglomeration distribution, and the spatial differences of all kinds of resources were significant. The resources were mainly distributed in Chengguan Town and Wenbao Township. (4) Topographic conditions, convenient transportation, population density and water system distribution were important factors that affected the spatial pattern of tourism resources. It was suggested that the government issue corresponding policies to optimize the spatial pattern of tourism resources; balance the relationship between the exploitation of tourism resources, tourism development and local economy, and the social and ecological environment; and promote the sustainable development of tourism in the northwestern ethnic areas in China.

The analysis of the spatial distribution of tourism resources and the identification of its influencing factors are crucial for supporting the sustainable development of regional tourism. This study established a comprehensive database of tourism resources in Ningxia Hui Autonomous Region (Ningxia) through a combination of literature review and field research. It examined the quantitative, qualitative, and categorical characteristics of tourism resources in Ningxia, and determined the spatial patterns based on kernel density and spatial association analysis. This study also comprehensively evaluated the societal, economic, and environmental factors influencing the spatial distribution of tourism resources in the entire region by employing the geographical detector model to quantify the influence of each factor. The following results were obtained. (1) There were 29218 individual tourism resources in Ningxia, comprising eight main types, 23 subtypes, and 105 fundamental types, and they exhibit a hierarchical pyramidal structure. (2) The tourism resources in Ningxia displayed characteristics of “widespread regional dispersion and limited regional agglomeration”. The spatial distribution of tourism resources was highly imbalanced, and most types of tourism resources exhibit strong positive spatial correlation. (3) The altitude, annual precipitation, population density, distance from urban centers, urbanization rate, and per capita GDP were identified as significant factors influencing the spatial distribution of tourism resources in Ningxia. Based on the results, we recommend that the government should formulate tourism development policies in Ningxia based on local conditions to effectively address the spatial imbalances, enhance the sustainability of tourism development, and continue to promote high-quality tourism development in Ningxia.