贵州省避暑旅游流网络结构特征及其影响因素

doi:10.11821/dlyj020201144

地理研究 ›› 2021, Vol. 40 ›› Issue (11): 3208-3224.doi: 10.11821/dlyj020201144

贵州省避暑旅游流网络结构特征及其影响因素

李磊¹(), 陶卓民¹(), 陆林², 孙小龙³, 刘红兰³

1.南京师范大学地理科学学院,江苏省地理信息资源开发与利用协同创新中心,南京 210023
2.安徽师范大学地理与旅游学院,芜湖 241002
3.贵州师范大学国际旅游文化学院,贵阳 550025

收稿日期:2020-11-25 接受日期:2021-02-01 出版日期:2021-11-10 发布日期:2022-01-10
通讯作者: 陶卓民（1963-）,男,江苏苏州人,博士,教授,博士生导师,研究方向为旅游地理、旅游规划与乡村旅游。E-mail: 09059@njnu.edu.cn
作者简介:李磊（1993-）,男,河南罗山人,博士研究生,研究方向为旅游地理与旅游规划。E-mail: tourismlilei@163.com
基金资助:
国家自然科学基金面上项目(41571139);国家自然科学基金重点项目(41930644);江苏省高校优势学科建设工程资助项目(164320H116)

Structural characteristics and influencing factors of summer tourism flow network in Guizhou province

LI Lei¹(), TAO Zhuomin¹(), LU Lin², SUN Xiaolong³, LIU Honglan³

1. School of Geography, Nanjing Normal University, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
2. School of Geography and Tourism, Anhui Normal University, Wuhu 241002, Anhui, China
3. School of International Tourism & Culture, Guizhou Normal University, Guiyang 550025, China

Received:2020-11-25 Accepted:2021-02-01 Published:2021-11-10 Online:2022-01-10

摘要/Abstract

摘要：

气候环境对人类行为具有重要影响,在全球变暖和城市热岛效应不断加剧的背景下,避暑旅游已经成为全球旅游者夏季出行的重要选择。贵州省作为中国国内主要的避暑旅游目的地之一,针对避暑旅游流网络特征及影响因素的研究尚未开展。本研究通过对贵州省避暑旅游现象长期的跟踪调查,获取2015—2019年间有效避暑旅游流线路数据2177条,运用复杂网络、GIS空间分析、QAP回归分析等方法,对避暑旅游流网络结构特征和影响因素进行研究。结果表明：① 避暑旅游流网络规模大、密度低,集聚特征明显,网络发育不完善,具有显著的“核心-边缘”结构,核心城市和景区分别呈“板块”和“点轴”状分布,旅游流线路具有“基营”和“环线”式结构特征。② 交通中心城市和核心景区是避暑旅游流的集散中心,贵阳在整体网络中具有突出优势和无法替代的地位,避暑旅游城市和景区在空间上相互交织,形成“贵（阳）安（顺）-荔波-凯（里）镇（远）”的“▽”空间结构。③ 避暑旅游流空间分布受气候舒适度、空间邻近性、旅游资源禀赋、接待服务能力和交通便捷程度等因素共同影响。最后,从区域旅游协调发展、旅游统计数据改革、网络空间结构优化等方面,对贵州省避暑旅游高质量发展提供了对策建议。

关键词: 避暑旅游, 旅游流网络, 气候舒适度, 贵州省, 复杂网络分析, QAP回归

Abstract:

Climate and environment have a great influence on human health, and climate comfort is an important factor in tourists' mobility behavior. Under global warming and the increasing effect of urban heat islands, travel to the cool areas has become a major choice for more and more tourists in summer holidays. Guizhou province is located on the Yunnan-Guizhou Plateau in Southwest China. It is one of the coolest areas in summer and one of China's favorite summer tourist destinations. The number of summer tourists in Guizhou has grown rapidly in the past few years, but previous studies have not been carried out on the characteristics and influencing factors of the summer tourism flow network in the province. This paper, based on long-term investigations of the summer tourism phenomena in the study area, obtained 2177 valid summer tourism flow route data from 2015 to 2019. Then, we use complex network analysis, spatial analysis (GIS), quadratic assignment procedure (QAP), and other methods to study the summer tourism flow network's structural characteristics and influencing factors. The results are as follows: (1) Summer tourism flow network presents characteristics of large scale, low density, obvious agglomeration and imperfect development, and a structure of significant "core-periphery". The core cities and scenic spots are distributed in a "plates" and "point axis" pattern, and tourism flow routes have the structural characteristics of "base camp" and "trip chaining." (2) The transportation city and core scenic spots are the centers of summer tourism flow. Guiyang is the most important node city in the network, with outstanding advantages and irreplaceable status in the overall network. In terms of spatial distribution, summer tourism cities and scenic spots form a "▽"-shaped network of Guiyang & Anshun-Libo-Kaili & Zhenyuan. (3) The spatial distribution of summer tourism flow is affected by climate comfort, spatial proximity, tourism resource endowment, reception service capacity and transportation convenience, but urban economic development has no significant impact on it. Finally, this paper provides countermeasures and suggestions for the high-quality development of summer tourism in Guizhou from aspects of coordinated regional tourism development, tourism statistical data reform, and network space structure optimization.

Key words: summer tourism, tourism flow network, summer tourism comfort, Guizhou province, complex network analysis, Quadratic Assignment Procedure (QAP)

李磊, 陶卓民, 陆林, 孙小龙, 刘红兰. 贵州省避暑旅游流网络结构特征及其影响因素[J]. 地理研究, 2021, 40(11): 3208-3224.

LI Lei, TAO Zhuomin, LU Lin, SUN Xiaolong, LIU Honglan. Structural characteristics and influencing factors of summer tourism flow network in Guizhou province[J]. GEOGRAPHICAL RESEARCH, 2021, 40(11): 3208-3224.

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	旅游流线路（条）	避暑旅游流线路（条）	占比（%）	非避暑旅游流线路（条）	占比（%）
2015年	259	96	37.07	163	62.93
2016年	422	183	43.36	239	56.64
2017年	604	285	47.19	319	52.81
2018年	1461	765	52.36	696	47.64
2019年	1703	848	49.79	855	50.21
总计	4449	2177	48.93	2272	51.07

序号	城市	加权点入度	加权点出度	介数中心度	等级排序值	序号	景点	加权点入度	加权点出度	介数中心度	等级排序值
1	贵阳	485.75	508.25	0.30	0.21	1	西江苗寨	249.00	195.50	0.18	0.12
2	安顺	346.25	344.75	0.14	0.15	2	黄果树瀑布	222.50	222.25	0.23	0.11
3	荔波	310.00	304.75	0.13	0.13	3	小七孔	212.50	228.50	0.07	0.10
4	凯里	327.25	310.00	0.08	0.13	4	镇远古城	107.50	119.25	0.05	0.05
5	镇远	160.00	161.25	0.08	0.07	5	万峰林	52.00	47.50	0.05	0.04
6	从江	75.25	75.75	0.08	0.04	6	大七孔	100.25	95.75	0.02	0.04
7	兴义	58.50	57.50	0.05	0.04	7	青岩古镇	80.75	82.00	0.06	0.04
8	织金	50.00	45.75	0.01	0.03	8	马岭河峡谷	38.50	41.25	0.02	0.03
9	江口	44.50	35.25	0.00	0.02	9	黔灵山	68.25	79.50	0.03	0.03
10	平塘	28.75	26.75	0.00	0.02	10	甲秀楼	60.00	68.75	0.02	0.03
11	遵义	22.25	22.00	0.02	0.01	11	梵净山	37.75	45.75	0.08	0.02
12	六盘水	12.00	11.25	0.02	0.01	12	肇兴侗寨	44.50	62.25	0.05	0.02
13	赤水	7.00	11.50	0.00	0.01	13	茅台镇	4.00	3.25	0.03	0.02
14	铜仁	18.00	30.00	0.00	0.01	14	丙安古镇	7.00	1.25	0.01	0.02
15	都匀	12.75	14.50	0.00	0.01	15	织金洞	27.50	32.75	0.03	0.02

指标	变量名称	含义及数据来源	预期	文献
气候舒适度	气候舒适度(S_lsg)	根据贵州省85个城市的气温、相对湿度、平均风速计算得出。S_lsg的计算方法和指标含义见公式(5)	-	[34]
	气候舒适度(S_ly)	同上。S_ly的计算方法和指标含义见公式(6)	+	[35]
空间邻近性	空间是否邻近	空间邻近的区域更容易建立联系。根据贵州省行政区划图,若两地相邻,则赋值为1,否则,赋值为0	+	[37,38]
旅游资源禀赋	5A、4A景区加权分	避暑旅游者趋向高等级旅游景区流动。据贵州省文旅厅数据,按照图2(b)中5A、4A景区平均到访量赋值为83.32、16.68,求加权值后建立差值矩阵	+	[37,38]
经济发展水平	GDP总量	避暑旅游者趋向经济水平高的区域流动。据各市、县统计公报,取2015—2019年平均值,建立差值矩阵	+	[20,38]
	三产收入	同上	+	[20]
	三产占比	同上	+	[20]
接待服务能力	饭店加权分	避暑旅游者趋向接待服务能力强的区域流动。星级饭店据贵州省文旅厅数据,其他饭店数据来源于在线预订平台。5—1星级分别赋值为9,7,5,3,1,其他饭店赋值为1,求加权值后建立差值矩阵	+	[20,37]
	旅行社数量	数据来源于贵州省文旅厅,根据数量建立差值矩阵	+	[20,37]
	国内旅游人次	同GDP总量	+	[37,38]
	国内旅游收入	同GDP总量	+	[37,38]
交通便捷程度	汽车最短旅行时间	避暑旅游者趋向在交通便捷的区域之间流动。最短时间通过百度地图查询获得	-	[38,41]
	高铁/火车最短旅行时间	中国铁路官网(12306)查询,取最短时间。若两地之间无铁路,则赋值为0	-	[27,41]

指标	变量名	QAP相关	P-Value	QAP回归	Sig.
空间邻近性	空间是否邻近	0.237***	0.000	0.119***	0.005
旅游资源禀赋	5A、4A景区加权分	0.406***	0.001	0.204**	0.046
经济发展水平	GDP总量	0.329***	0.009	-0.302	0.301
	三产收入	0.352***	0.003	0.458	0.295
	三产占比	0.028	0.377	0.008	0.435
接待服务能力	饭店加权分	0.387***	0.000	0.578*	0.073
	旅行社数量	0.350***	0.002	-0.957*	0.063
	国内旅游人次	0.380***	0.001	-0.610	0.114
	国内旅游收入	0.382***	0.001	0.984	0.100
交通便捷程度	汽车最短旅行时间	-0.368***	0.000	-0.247***	0.000
	高铁/火车最短旅行时间	0.237***	0.002	0.069*	0.086

贵州省避暑旅游流网络结构特征及其影响因素

Structural characteristics and influencing factors of summer tourism flow network in Guizhou province

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