Spatial Distribution Pattern and Influencing Factor Analysis of Rural Tourism Sites in Inner Mongolia

Rural tourism serves as a catalyst for rural revitalization and plays an important role in poverty alleviation in Inner Mongolia. This study analyzes the spatial distribution of key villages and star-rated rural (pastoral) households in Inner Mongolia as rural tourism destinations, clarifies their distribution patterns, and contributes to the development of rural tourism in Inner Mongolia. By selecting rural tourism destinations in Inner Mongolia, importing the coordinate points obtained from Baidu Map API into Arc GIS 10.8, and using indices such as the nearest neighbor index, location quotient, kernel density analysis, the geographic concentration index, and the geographic correlation ratio for quantitative analysis, this study explores the distribution patterns and identifies the driving factors influencing the distribution of rural tourism destinations in Inner Mongolia using geographic detectors. The results are:(1) Rural tourism destinations in Inner Mongolia exhibit agglomerated distribution in space, with uneven distribution among different leagues and cities. (2) The spatial density of rural tourism destinations in Inner Mongolia shows the characteristics of “a single core center, with decreasing density toward the periphery, and dispersed secondary cores, and dispersed secondary cores.” The actual geographic concentration index G is 30.23, while the distribution index G 0 under uniform conditions is 29.14. Since G is greater than G 0, a certain degree of agglomeration exists in the spatial distribution of rural tourism destinations in Inner Mongolia, but the overall degree of agglomeration is not strong. (3) Tourism infrastructure, economic development, tourism potential, and natural factors collectively influence the spatial distribution of rural tourism destinations in Inner Mongolia. Among them, economic development, tourism potential, and tourism infrastructure have significant impacts on the distribution of key villages. In terms of subsystems, the number of A-level scenic resources has the highest explanatory power (q=0.848), followed by the number of tourists received (q=0.788), employment in the tertiary industry (q=0.687), and the resident population (q=0.603), which are the main factors influencing the spatial differentiation of rural tourism destinations.