山西省国家A级旅游景区空间分布影响因素研究

doi:10.11821/dlxb202004015

摘要/Abstract

摘要：

基于GIS空间分析技术与地理探测器（Geodetector）方法,使用旅游政务网、地理空间数据云、政府报告和统计年鉴相关数据,对2001—2017年间影响山西省国家A级旅游景区空间分布的影响因素进行了分析,包含社会政治因素（相关政策、常驻人口、交通状况）、经济产业因素（三产占比、旅游收入、生产总值）和自然环境因素（海拔高度、河流水系、平均气温）。结果表明：① 社会政治因素和经济产业因素变化较大,属于动态影响因素,自然环境因素变化较小,属于静态影响因素;② 相关政策、常住人口、交通状况、旅游收入、生产总值5个三级动态影响因素的影响能力值从2001—2017年间不断加强,而三产占比三级动态影响因素呈现波动下降,海拔高度、河流水系、平均气温3个三级静态影响因素的影响能力值变化较平稳;③ 从演化过程来看,2001—2017年间社会政治因素影响能力值逐年增加,是影响程度最高的二级影响因素,经济产业因素发展为影响程度第二的二级影响因素,自然环境因素发展为影响程度最小的二级影响因素。结果可为合理评价和指导区域旅游业全面发展提供有力支撑,促进旅游业发展,为旅游景区空间结构优化和旅游产业升级提供科学依据。

关键词: 国家A级旅游景区, 地理探测器, 空间分布演化, 影响因素, 山西省

Abstract: Methods

of spatial analysis technology of GIS as well as Geodetector are proposed to identify the spatial distribution and influencing factors of national A-level tourist attractions in Shanxi Province from 2001 to 2017, based on data from tourism administration network, Geospatial Data Cloud, government reports and statistical yearbooks. These influencing factors include socio-political factor (including related policies, resident population and traffic conditions), economic-industrial factor (including the proportion of three industries, tourism income and GDP) and natural environmental factor (including altitude, river system and average temperature). The results show that: (1) Dynamic factors, such as socio-political factor as well as economic-industrial factor, changed greatly, while static factors, such as natural environment factor, changed little; (2) Nine third-level influencing factors affected the spatial distribution of national A-level tourist attractions in Shanxi to different degrees, five of which, including related policies, resident population, traffic conditions, tourism income and GDP, were continuously strengthened from 2001 to 2017, while the proportion of three industries decreased in a fluctuating way. For the rest of third-level influencing factors, i.e. altitude, river system and average temperature, their influence on the spatial distribution of national A-level tourist attractions in the province remained relatively stable. (3) With respect of the evolution of national A-level tourist attractions in Shanxi from 2001 to 2017, the extent of impacts of the socio-political factor, one of the second-level factors, increased year by year and the factor became the most important, followed by the economic-industrial factor, while the natural environmental factor had the minimum influence. The results can provide a strong support for assessing and guiding the overall development of regional tourism, promoting the development of tourism, and providing a scientific method to optimize the spatial structure of tourist attractions and tourism industry upgrading.

Key words: national A-level tourist attractions, Geodetector, evolution of spatial distribution, influencing factor, Shanxi Province

刘敏, 郝炜. 山西省国家A级旅游景区空间分布影响因素研究[J]. 地理学报, 2020, 75(4): 878-888.

LIU Min, HAO Wei. Spatial distribution and its influencing factors of national A-level tourist attractions in Shanxi Province[J]. Acta Geographica Sinica, 2020, 75(4): 878-888.

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影响因素	取值范围	对应等级	影响因素	取值范围	对应等级
相关政策(x₁)	根据每一年的具体数值,采用自然间断点自动分级	1	生产总值(x₆)	根据每一年的具体数值,采用自然间断点自动分级	1
		2			2
		3			3
		4			4
		5			5
常住人口(x₂)	根据每一年的具体数值,采用自然间断点自动分级	1	海拔高度(x₇)(m)	<800	1
		2		800~1000	2
		3		1000~1200	3
		4		1200~1600	4
		5		≥ 1600	5
交通状况(x₃)	根据每一年的具体数值,采用自然间断点自动分级	1	河流水系(x₈)(km)	0~5	1
		2		5~10	2
		3		10~15	3
		4		15~20	4
		5		>20	5
三产占比(x₄)	根据每一年的具体数值,采用自然间断点自动分级	1	平均气温(x₉)(℃)	<4	1
		2		4~8	2
		3		8~12	3
		4		12~14	4
		5		≥ 14	5
旅游收入(x₅)	根据每一年的具体数值,采用自然间断点自动分级	1
		2
		3
		4
		5

年份	x₁	x₂	x₃	x₄	x₅	x₆	x₇	x₈	x₉
2001	0.0246	0.0110	0.0143	0.0888	0.0630	0.0533	0.0099	0.0024	0.0650
2002	0.0341	0.0087	0.0398	0.0478	0.0262	0.0299	0.0663	0.00054^**	0.0282
2003	0.0182	0.0403	0.0553	0.0656	0.0560	0.0529	0.0863	0.0012	0.0808
2004	0.0182	0.0403	0.0425	0.0425	0.0542	0.0503	0.0863	0.0012	0.0808
2005	0.0523	0.0465	0.0503	0.0299	0.0617	0.0250	0.1234	0.0008	0.1098
2006	0.0522	0.0465	0.0236	0.0796	0.0617	0.0340	0.1234	0.0008	0.1098
2007	0.0645	0.0345	0.0206	0.0592	0.0326	0.0174	0.1231	0.0004^**	0.0851
2008	0.0834	0.0263	0.0318	0.0283	0.0411	0.0239	0.1149	0.0005^**	0.0920
2009	0.0801	0.0312	0.0459	0.0368	0.0479	0.0398	0.1009	0.0008	0.1064
2010	0.0326	0.0554	0.0971	0.0509	0.0598	0.0652	0.1065	0.0014	0.1001
2011	0.0572	0.0531	0.0918	0.0623	0.0405	0.0730	0.1164	0.0013	0.1100
2012	0.0322	0.0507	0.1025	0.0622	0.0676	0.0660	0.1154	0.0016	0.1208
2013	0.0502	0.0562	0.1268	0.0437	0.0738	0.0833	0.1183	0.0054	0.1218
2014	0.0890	0.0469	0.1178	0.0211	0.0715	0.0551	0.1109	0.0019	0.1129
2015	0.0652	0.0455	0.1256	0.0500	0.0884	0.0765	0.1010	0.0021	0.1033
2016	0.1446	0.0470	0.1598	0.0287	0.1231	0.1198	0.1100	0.0022	0.1091
2017	0.1804	0.0520	0.1052	0.0370	0.1484	0.1374	0.1062	0.0027	0.1072