“一带一路”沿线中国省份旅游信息流时空分布格局及驱动因素分析

doi:10.12118/j.issn.1000-6060.2022.614

摘要/Abstract

摘要：

采用空间场强、二次指派程序（QAP）、地理探测器等方法，分析“一带一路”沿线中国省份旅游信息流空间分布格局及驱动因素。结果表明：（1） 2012—2022年“一带一路”沿线中国省份旅游信息流总场强发展分为高速增长、减速增长与急速下降3个阶段。（2） 2012—2022年“一带一路”沿线中国省份旅游信息流集聚场强呈现东北、东南地区高集聚，西北、西南地区较低集聚的空间分布；扩散场强呈现以云南、西藏为中心，两地向外辐射，形成由西南向东北梯次下降的空间分布格局；总场强呈现多核心、多层级分布格局，新疆、云南、黑龙江、广东扮演西北、西南、东北、东南地区旅游信息流总场强引领角色。（3） 2012—2019年“一带一路”沿线中国省份旅游信息流受到“推力-拉力-阻力”3方面影响，电信业务总量、互联网宽带用户数、旅游业从业人数、住宿企业数量、企业法人单位数、政府对旅游业投资总额、旅游资源禀赋、空间距离是影响旅游信息流的重要因素。

关键词: 旅游信息流, “一带一路”沿线省份, 驱动因素, 中国

Abstract:

This study analyzed the spatial distribution pattern and driving factors of tourism information flow in Chinese provinces along the Belt and Road using spatial field strength, quadratic assignment procedure correlation analysis, and Geodetector. The following results were observed. (1) From 2012 to 2022, the development of the total field strength of tourism information flow in Chinese provinces along the Belt and Road is divided into three stages, such as high growth, deceleration growth, and rapid decline stage. (2) The agglomeration field strength in Chinese provinces along the Belt and Road shows a spatial distribution pattern, exhibiting high agglomeration in the northeast and southeast regions and low agglomeration in the northwest and southwest regions from 2012 to 2022. Moreover, the diffusion field strength demonstrates a spatial distribution pattern with Yunnan and Tibet as the center and the two places radiating outward, forming a gradual decrease from southwest to northeast of China. Additionally, the total field strength presents a multicore and multilevel distribution pattern with Xinjiang, Yunnan, Heilongjiang, and Guangdong as the total field strength areas of tourism information flow in the northwest, southwest, northeast, and southeast, respectively. (3) The push-pull resistance in three aspects affects the tourism information flow in Chinese provinces along the Belt and Road from 2012 to 2019. The total telecommunication services, number of internet broadband users, number of people working in the tourism industry, number of accommodation enterprises, number of business corporations in the tourism industry, number of corporate entities, total government investment in tourism, tourism resource endowment, and spatial distance are the crucial factors that affect tourism information flow.

Key words: tourism information flow, provinces along the Belt and Road, driving factors, China

杨丽,付玉慧,王姣姣. “一带一路”沿线中国省份旅游信息流时空分布格局及驱动因素分析[J]. 干旱区地理, 2023, 46(10): 1714-1722.

YANG Li,FU Yuhui,WANG Jiaojiao. Spatiotemporal distribution patterns and driving factors of tourism information flow in Chinese provinces along the Belt and Road[J]. Arid Land Geography, 2023, 46(10): 1714-1722.

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扩散地	旅游信息流集聚地
扩散地	2012—2019年	2020—2022年
内蒙古	广东、浙江、上海	一致
辽宁	吉林、黑龙江、广东	一致
吉林	辽宁、黑龙江、广东	一致
黑龙江	辽宁、吉林、广东	广东、辽宁、浙江
上海	浙江、广东、福建	广东、浙江、辽宁
浙江	上海、福建、广东	一致
福建	浙江、广东、上海	一致
广东	浙江、福建、上海	一致
广西	广东、浙江、上海	一致
海南	广东、浙江、上海	一致
重庆	广东、陕西、浙江	一致
云南	广东、浙江、陕西	辽宁、广东、黑龙江
西藏	广东、浙江、上海	一致
陕西	广东、浙江、辽宁	广东、浙江、上海
甘肃	陕西、广东、浙江	广东、浙江、上海
青海	陕西、广东、甘肃	广东、浙江、上海
宁夏	陕西、广东、内蒙古	广东、陕西、浙江
新疆	广东、浙江、上海	一致

变量名称	衡量指标	指标方向	相关系数	q值	影响强度
经济发展水平	地区生产总值（GDP）/10⁸元	正向	0.444^***	0.480^**	较强
人口因素	年末常住人口数量/10⁴人	正向	0.392^***	0.345^**	一般
	普通高等在校学生数量/10⁴人	正向	0.227^*	0.507^*	显著
交通便捷度	私人汽车拥有量/10⁴辆	正向	0.426^***	0.356^*	一般
	高速公路里程数/km	正向	0.401^***	0.098	不显著
	铁路里程数/km	正向	0.255^**	0.327	不显著
信息化水平	电信业务总量/10⁸元	正向	0.379^***	0.665^***	显著
	互联网宽带用户/10⁴户	正向	0.439^***	0.665^***	显著
旅游吸引力	旅游资源禀赋	正向	0.222^*	0.420^*	较强
旅游服务水平	旅游业从业人数/人	正向	0.329^**	0.665^***	显著
	旅行社数量/个	正向	0.426^***	0.376^**	一般
	住宿企业数量/个	正向	0.375^***	0.665^***	显著
市场活跃度	企业法人单位数/个	正向	0.401^***	0.665^***	显著
	外商投资额/10⁸ USD	正向	0.340^**	0.313^**	一般
公共服务水平	公园绿地面积/hm²	正向	0.416^***	0.409^**	较强
	医疗机构床位数/10⁴张	正向	0.389^***	0.345^**	一般
政府支持力度	政府对旅游业投资总额/10⁸元	正向	0.347^***	0.618^**	显著
距离因素	空间距离/km	负向	-0.273^***	0.456^**	较强