中国西北地区A级旅游景区时空分布格局及影响因素

doi:10.12118/j.issn.1000-6060.2023.171

摘要/Abstract

摘要：

区域旅游景区合理布局是实现旅游业高质量发展的内在要求与根本保障，探究其空间格局演变及影响因素对促进区域旅游协作、经济共享及文化融合具有重要意义。基于中国西北地区2010—2020年A级旅游景区数据，采用GIS空间技术分析景区时空分布特征，从社会、经济、环境3个方面构建影响因素指标体系，并运用地理探测器厘清景区空间格局演变与影响因素的内在机理。结果表明：（1）西北地区A级旅游景区数量变化不均衡特征显著，空间上集聚分布且集聚程度逐渐增强，呈现“西北—东南”的空间分布态势，中轴线处在“吐鲁番市—酒泉市—海北藏族自治州—兰州市—宝鸡市—西安市”一线，景区空间结构经历了“分散式点状—组团式斑块状—倒L型条带状”的演变过程。（2）常住人口、人均GDP、旅游收入、通车里程和政策力度5个因子对A级旅游景区空间格局演变的影响程度较强。（3）社会因素的影响能力上升，表现为先下降、后稳步上升2个阶段；经济因素的影响能力上升，表现为周期性的上升-下降波动；环境因素的影响能力趋向于上升，但变化幅度相对稳定。

关键词: A级旅游景区, 时空分布特征, 影响因素, 西北地区

Abstract:

Achieving high-quality tourism development relies on the rational layout of regional tourist attractions, which serves as an inherent requirement and fundamental guarantee. This study delves into the spatial pattern evolution and influencing factors of A-level scenic spots in northwestern China from 2010 to 2020. Using GIS spatial technology, we analyze the spatial and temporal distribution characteristics of scenic spots and construct an index system that encompasses social, economic, and environmental aspects. In addition, geographic detectors are employed to elucidate the internal mechanism of spatial pattern evolution and influencing factors. The findings reveal the following: (1) A noticeable imbalance exists in the number of A-level tourist attractions in northwestern China, with an increasing spatial concentration trend along a “northwest-southeast” axis. The central axis is delineated by the “Turpan City-Jiuquan City-Haibei Tibetan Autonomous Prefecture-Lanzhou City-Baoji City-Xi’an City” line. The spatial structure of scenic spots undergoes an evolution process from “scattered point” to “group-type patch” and ultimately to an “inverted L-shaped strip”. (2) Five influential factors, namely resident population, per capita GDP, tourism income, traffic mileage, and policy intensity, significantly shape the spatial pattern evolution of A-level tourist attractions. (3) The impact of social factors demonstrates a two-stage pattern of initial decline followed by steady increase, indicating rising influence. Economic factors exhibit periodic ups and downs in their influence, whereas environmental factors, although displaying a tendency to rise, experience a relatively stable range of change.

Key words: A-level tourist attractions, spatiotemporal distribution characteristics, influencing factors, northwestern China

吴志祥, 张志斌, 赵学伟, 陈龙, 马晓敏, 柴姣. 中国西北地区A级旅游景区时空分布格局及影响因素[J]. 干旱区地理, 2023, 46(12): 2061-2073.

WU Zhixiang, ZHANG Zhibin, ZHAO Xuewei, CHEN Long, MA Xiaomin, CHAI Jiao. Spatiotemporal distribution pattern and influencing factors of A-level tourist attractions in northwestern China[J]. Arid Land Geography, 2023, 46(12): 2061-2073.

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影响因素	因子选取	数据来源
社会因素	政策力度（x₁）	2010—2020年各市政府工作报告中提到“旅游景区”一词的频数
	常住人口（x₂）/10⁴人	2011—2021年《中国城市统计年鉴》
	通车里程（x₃）/km	2010—2020年各省统计年鉴的公路里程
经济因素	第三产业占比（x₄）/%	2011—2021年《中国城市统计年鉴》
	旅游收入（x₅）/10⁸元	2010—2020年各市的国民经济和社会发展统计公报
	人均GDP（x₆）/元	2011—2021年《中国城市统计年鉴》
环境因素	海拔高程（x₇）/m	地理空间数据云中的DEM数据
	年降水量（x₈）/mm	2010—2020年中国地面气候资料日值数据集V3.0处理生成
	年平均气温（x₉）/℃	2010—2020年美国国家海洋和大气管理局下设的国家环境信息中心
	河流水系（x₁₀）/km	Open Street Map地图，用ArcGIS构建0~20 km内的4级河流缓冲区

年份	多边形面积的标准差/m²	多边形面积的平均值/m²	C_v/%	分布类型
2010	3873728498.40	11429982878.96	295.06	集聚分布
2015	2122480406.42	7372495850.31	347.35	集聚分布
2020	1375504853.84	5320054291.23	386.77	集聚分布

年份	重心经度/°E	重心纬度/°N	长半轴长度/km	短半轴长度/km	转角/(°)
2010	99.20	38.50	1224.76	375.11	116.86
2015	98.36	38.53	1420.10	364.73	111.78
2020	98.81	38.38	1441.15	365.77	109.83

年份	x₁	x₂	x₃	x₄	x₅	x₆	x₇	x₈	x₉	x₁₀
2010	0.2256	0.2713	0.2413	0.1969	0.2633	0.2735	0.1805	0.2013	0.1612	0.1535
2011	0.2113	0.2808	0.2454	0.2087	0.2702	0.2809	0.1839	0.2046	0.1657	0.1547
2012	0.2244	0.2923	0.2435	0.2155	0.2738	0.2847	0.1924	0.1878	0.1724	0.1534
2013	0.2305	0.2915	0.2406	0.2074	0.2805	0.2814	0.1956	0.1965	0.1805	0.1507
2014	0.2236	0.3054	0.2472	0.2066	0.2756	0.2752	0.1908	0.2187	0.1726	0.1523
2015	0.2254	0.3112	0.2546	0.2168	0.2806	0.2741	0.1914	0.2225	0.1742	0.1506
2016	0.2283	0.3076	0.2581	0.2267	0.2867	0.2815	0.1933	0.2194	0.1728	0.1545
2017	0.2366	0.3054	0.2603	0.2200	0.2824	0.2864	0.1957	0.2146	0.1771	0.1547
2018	0.2454	0.3111	0.2641	0.2184	0.2913	0.2914	0.1967	0.2164	0.1756	0.1535
2019	0.2465	0.3176	0.2667	0.2247	0.2956	0.3036	0.1956	0.2209	0.1767	0.1547
2020	0.2423	0.3112	0.2689	0.2205	0.2912	0.3015	0.1945	0.2212	0.1772	0.1558