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In recent years, tourism and leisure districts have become a pivotal aspect of China’s tourism development. Analyzing their spatial distribution characteristics and driving factors is essential for fostering comprehensive district tourism and promoting sustainable development, while also facilitating the profound integration of culture and tourism. This study undertakes a thorough investigation of the spatiotemporal patterns of national-level tourism and leisure districts in China, employing GIS spatial statistical analysis techniques, including the Average Nearest-Neighbor Index, Kernel Density Analysis, and Standard Deviation Ellipse. Additionally, this research identifies the principal driving factors affecting the spatial distribution of these districts through overlay analysis, buffer analysis, and geographic detectors. The findings reveal that (1) tourism and leisure districts exhibit a notable spatial clustering pattern, characterized by a predominance in the eastern regions and scarcity in the west, alongside a higher concentration in the south compared to the north, with a gradual decline in spatial density. (2) High-density tourism and leisure districts are predominantly located in the Yangtze River Delta and the Beijing–Tianjin–Hebei urban agglomerations, while regions of elevated density are situated in the southwest (notably in Sichuan, Chongqing, Guizhou, and Yunnan provinces). The centroids of the first to third batches of tourism and leisure districts have transitioned from southern to northern locations. (3) The population density factor exhibits the most substantial explanatory power regarding the distribution of tourism and leisure districts (q: 0.80528), followed by the added value of the tertiary industry (q: 0.53285), whereas the slope factor shows minimal influence (q: 0.00876). Furthermore, the distance to rivers of grade three and above, in conjunction with population density, constitutes the primary factor combination influencing the spatial configuration of tourism and leisure districts (q: 0.9101).
In recent years, tourism and leisure districts have become a pivotal aspect of China’s tourism development. Analyzing their spatial distribution characteristics and driving factors is essential for fostering comprehensive district tourism and promoting sustainable development, while also facilitating the profound integration of culture and tourism. This study undertakes a thorough investigation of the spatiotemporal patterns of national-level tourism and leisure districts in China, employing GIS spatial statistical analysis techniques, including the Average Nearest-Neighbor Index, Kernel Density Analysis, and Standard Deviation Ellipse. Additionally, this research identifies the principal driving factors affecting the spatial distribution of these districts through overlay analysis, buffer analysis, and geographic detectors. The findings reveal that (1) tourism and leisure districts exhibit a notable spatial clustering pattern, characterized by a predominance in the eastern regions and scarcity in the west, alongside a higher concentration in the south compared to the north, with a gradual decline in spatial density. (2) High-density tourism and leisure districts are predominantly located in the Yangtze River Delta and the Beijing–Tianjin–Hebei urban agglomerations, while regions of elevated density are situated in the southwest (notably in Sichuan, Chongqing, Guizhou, and Yunnan provinces). The centroids of the first to third batches of tourism and leisure districts have transitioned from southern to northern locations. (3) The population density factor exhibits the most substantial explanatory power regarding the distribution of tourism and leisure districts (q: 0.80528), followed by the added value of the tertiary industry (q: 0.53285), whereas the slope factor shows minimal influence (q: 0.00876). Furthermore, the distance to rivers of grade three and above, in conjunction with population density, constitutes the primary factor combination influencing the spatial configuration of tourism and leisure districts (q: 0.9101).
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