The complex types and regional differences in tourist attractions mean that the evaluation and quantification of spatial structures require inter- and trans-disciplinary methodologies. Previous studies on spatial structure have mostly focused on the law of tourist flow and the development trend of tourism, emphasising humanistic and economic methods. Currently, the main challenge of spatial structure research is the integration of natural, economic, and social factors and scientifically supporting tourism planning and management. From the perspective of geographical distance and geometric space, this study developed a quantitative method for the spatial structure of tourist attractions, which combines a grade classification index, spatial relationship function, and influence factor analysis and selects cases for implementation in a geographic information system, with the advantages of visualisation, timely data update, and convenient guidance for practice. It provided new insights for understanding the sustainable management of tourist attractions from the intersection of geography and tourism science. The research results indicate that China has the highest number of 4A and 3A level tourist attractions, accounting for 80.9% of the total. The nearest neighbor ratio of scenic areas is less than 1, showing a significant spatial distribution clustering pattern, with four major scenic area clusters located in eastern and southern China. The Natural environment determines the spatial layout of scenic areas, with 51.46% of scenic areas distributed in regions below 200 m in altitude, and 95.10% of scenic areas located in areas with a slope of less than 15 degrees. 1A and 2A level scenic areas are mainly distributed in cold and dry regions, while 5A, 4A, and 3A level scenic areas are relatively concentrated with similar climatic characteristics. 5A level scenic areas have higher GDP, population density, and growth rates. The spatial structure of scenic areas is closely related to population distribution and economic development; southeastern China accounts for more than 90% of the national population and GDP, and this region has over 60% of A-level and above scenic areas.
Zi Wang (Fri,) studied this question.