The system coupling between tourist flow and destination: an empirical analysis of inbound tourism in six major cities
ZHANGChunhui, MAYaofeng, BAIKai Tourism and Environment College of Shaanxi Normal University,Xi'an 710119,China 收稿日期:2016-04-20 修回日期:2016-05-23 网络出版日期:2016-06-20 版权声明:2016《资源科学》编辑部《资源科学》编辑部 基金资助:国家自然科学基金项目(41271158)陕西省软科学研究计划项目(2016KRM119)北京市自然科学基金项目(9132006)北京市教育委员会2013年长城****培养计划项目(CIT&TCD20130302) 作者简介: -->作者简介:张春晖,男,河北石家庄人,博士,讲师,研究方向为旅游目的地营销。E-mail:chunhui_1985@126.com
关键词:入境旅游流;城市目的地;系统耦合;时空关系;灰色关联模型 Abstract System Integration Research on the interaction of tourist flow and destination contributes to the theoretical framework of tourism system and settles issues on internal element relations,law of development and evolvement,forecasting,regulating supply and demand balance. Taking inbound tourism of Beijing,Shanghai,Guangzhou,Xi’an,Chengdu,and Kunming as research objects,we constructed a coupling evaluation index system for inbound tourist flows and destination. With application of a gray associative model,we conducted a quantitative evaluation of the coupling degree of inbound tourist flow system and destination system in the six cities from 1993 to 2012,and examined the coupling effect degree of system elements. We found that the velocity and quantity of tourist flow are the most important coupling dominant elements in inbound tourist flow system,while tourism service facility,personnel and destination economic environment are the most important coupling dominant elements in destination system. The coupling system between tourist flow and destination in these tourist cities has long been amelioration coupling. The fluctuation of coupling degree between the two systems is an inverted U in Beijing and Shanghai,and in the other cities coupling degree shows a downward trend. In the aspect of the coupling dominant elements in inbound tourist flow system,the differentiation of dominant elements in eastern cities is obvious,but in western cities the dominant elements are similar,showing tourist flow scale as the dominant element and other elements as supplementary. As for coupling dominant elements in the destination system,the natural,economic and social environment have stronger effects on system coupling in eastern cities,while tourism service facility and personnel and infrastructure have stronger effects in western cities. This indicates that enhancing the coupling quality of the systems needs to rely on the support of the overall environment of the destination. Hindered by poor geographical conditions and low levels of economic development,investment in core supply elements and media support elements on the destination system in western cities is still at a stage of increasing marginal returns.
Keywords:inbound tourist flows;city destination;system coupling;spatio-temporal relationship;gray associative model -->0 PDF (915KB)元数据多维度评价相关文章收藏文章 本文引用格式导出EndNoteRisBibtex收藏本文--> 张春晖, 马耀峰, 白凯. 旅游流与目的地系统耦合研究——以六大城市入境旅游为例[J]. , 2016, 38(6): 1013-1027 https://doi.org/10.18402/resci.2016.06.02 ZHANGChunhui, MAYaofeng, BAIKai. The system coupling between tourist flow and destination: an empirical analysis of inbound tourism in six major cities[J]. 资源科学, 2016, 38(6): 1013-1027 https://doi.org/10.18402/resci.2016.06.02
从旅游流与目的地发展的内在关系出发,依据科学性、整体性、层次性、动态连续性和可操作性等原则,分别构建旅游流和目的地两大系统的评价指标体系。基于文献总结,通过频度分析建立初始指标体系,再利用相关分析、变异系数分析和因子分析,对初始指标进行冗余性筛选、鉴别力筛选和主成分性筛选[34,35],最终从游客流量、资金流量、流速、流质四大方面,以及旅游核心吸引物、旅游服务设施与服务人员、基础设施、自然环境、经济环境、社会环境六大方面建立旅游流与目 的地系统耦合评价指标体系(表1)。在评价指标体系中,多数指标直接来自统计资料,或经简单计算即可得到,计算过程较复杂,需特别说明的指标如下。 在旅游流评价指标体系中,流质指数的计算公式见文献[36]。衡量消费潜力的4个指标,其计算步骤如下:首先选取各案例城市目的地客流量规模最大的前16个客源国(总游客比例约占各目的地城市接待入境游客的60%~80%),对这些国家历年游客规模比例进行归一化处理作为权重;其次通过世界银行网站收集各国指标数据;再次对这些指标数据乘以权重相加,求得4个指标的历年数值。如此处理考虑了各样本目的地客源市场的历年变动,也顾及了各客源市场的重要性程度的变动,可较为准确地反映主体客源国的各方面情况。 在目的地评价指标体系中,用以衡量旅游核心吸引物的高级别旅游资源共包括13种(括号内为品位度赋分值):世界遗产(40)、国家5A级旅游景区(25)、国家级风景名胜区(12)、国家4A级旅游景区(12)、国家森林公园(8)、国家地质公园(8)、国家级水利风景区(8)、国家城市湿地公园(8)、全国重点文物保护单位(5)、国家级自然保护区(5)、中国历史文化名镇(5)、中国历史文化名村(5)和中国历史文化名街(2)。在旅游核心吸引物各评价指标中,丰度,是指上述各类旅游资源的总数目,若某个评价对象同时拥有多个称号则仅计算1次;品位度,重点参考苏伟忠[37]和黄耀丽[38]的赋分方法,根据各类旅游资源的等级高低制定能够拉开档次的赋分规则计算得到,同时拥有多个称号的旅游资源,先以最高等级称号赋分,再加上次高等级称号的一半分值,以体现此类资源比同类单称号资源的优势;密度,以高级别旅游资源总数量除以目的地城市总面积,实际上是旅游资源的空间密度[39],能够反映目的地旅游资源的集聚程度;组合度,用以反映目的地各类旅游资源的匹配协调情况,组合度指数的计算公式见文献[40]。 Table 1 表1 表1入境旅游流与目的地系统耦合评价指标体系 Table 1Coupling evaluation index system of inbound tourist flows and destination
总体上,与旅游流系统平均关联度由高到低的领域层要素依次为旅游服务设施与服务人员、经济环境、基础设施、旅游核心吸引物、社会环境和自然环境。 (1)旅游服务设施与服务人员的平均关联度最高,达到0.710,是对旅游流系统发展响应与反馈作用最强的目的地系统要素,表现出此类要素占据着区域旅游供给的决定性地位[56]。具体来看,在准则层指标要素中,旅游企业从业人员的平均关联度(0.717)较企业数量(0.706)和企业固定资产(0.707)明显更高,一方面说明劳动力优势是中国在近20年的国际旅游服务贸易中获得一定优势的因素之一,另一方面也印证了左冰等[57]的研究发现,即驱动中国旅游业快速发展的最主要因素是劳动力投入,而资本投入的影响作用相对较弱。 (2)从经济环境上看,经济外向性对入境旅游流的反馈作用更明显,平均关联度达0.716,而其指标层要素中,外贸依存度(X45)的平均关联度最高(0.730),表明城市的外向型经济主要通过进出口贸易与入境旅游流产生交互影响,这印证了进出口贸易对中国城市入境旅游发展的重要影响作用[58],并反映出发展商务旅游的重要性。 (3)从基础设施上看,该领域层要素的平均关联度与经济环境相近,为0.691。具体而言,人均年民航客运量(X11)、人均年铁路客运量(X12)、每百人互联网用户数(X20)、人均邮电业务量(X21)、人均全年社会用电总量(X22)、每万人石油液化气供气总量(X25)6个指标与入境旅游人次(Y1)间达到了极高关联水平,表明城市对外交通、网络通讯以及以电能和石油能源为主的基础设施对入境旅游流发展的反馈作用最为明显。需要注意的是,以往旅游发展与基础设施关系的研究多对能源设施有所忽略,而大规模旅游流进入目的地所带来的能源消费不但集中于旅游交通,还涉及住宿、景区游览及其他旅游服务设施,覆盖旅游全行业,因此,城市目的地能源基础设施对入境旅游发展的重要作用应得到重视。能源的平均关联度在4个准则层要素中最高(0.694)正支持了上述观点。 Table 2 表2 表21993-2012年六大城市入境旅游流与目的地系统要素关联矩阵 Table 2Systems factors incidence matrix between inbound tourist flows system and destination system of 6 cities from 1993 to 2012
分别计算六大城市1993-2012年入境旅游流与城市目的地系统的耦合度(图1),从历时性角度对比分析各城市耦合度演变趋势特征,结果发现: (1)六大城市历年两系统耦合度介于0.583~0.725之间,绝大多数年份处于磨合阶段,说明中国典型旅游城市的入境旅游流与目的地系统始终处于磨合发展阶段,尚未跨入协调阶段。北京、上海的耦合度呈现出一定的倒“U”型变化趋势,广州、西安、成都和昆明的耦合度则表现出波动下降的趋势,而六大城市耦合度的发展均可以2003年为分界点划分为两大阶段。 显示原图|下载原图ZIP|生成PPT 图11993-2012年六大城市入境旅游流与目的地系统的耦合度 -->Figure 1Coupling degree of inbound tourism flows and destination system of 6 cities from 1993 to 2012 -->
以指标体系标准化数据为基础,分别计算六大城市1993-2012年整体耦合度,分析耦合度的空间分异规律,并探究两系统内部要素耦合关联特征的城际差异。从六大城市整体耦合度上看,广州最高(0.687),上海次之(0.669),此后依次为北京(0.667)、西安(0.663)、成都(0.647)和昆明(0.642)。灰色关联耦合度是对两系统内部要素间互动耦合关联程度的综合反映,因此以上结果表明东部城市入境旅游流与目的地系统内部要素间耦合关联程度较西部城市更为紧密。 从入境旅游流系统要素关联序上看(表3),客流规模、客流增长、客流比重、消费规模、停留时间、消费增长、消费占比和旅游流效益是各城市入境旅游流系统中的耦合主导要素。这些耦合主导要素的空间分异特征如下: (1)客流规模与客流比重的平均关联度在除广州外的所有城市中占据着优势地位,表明近20年来,在中国典型目的地城市中,游客数量规模是主导入境旅游流与目的地系统耦合关系的最主要要素。 (2)东部三 大城市耦合主导要素存在明显分异。从关联序前3位可以看出,北京与广州分别以游客流量和资金流量(主要为消费规模)为两系统耦合关系的主导要素,而上海则基本以游客流量和资金流量并重。 (3)西部三大城市耦合主导要素趋同。西安、成都和昆明3城市入境旅游流系统准则层中关联度前5位的要素分布格局基本一致,表明3城市均以旅游客流规模为耦合主导要素,而其他要素为辅。 (4)西部三大城市资金流量主要以间接方式对目的地的发展施加影响。从资金流量下属的准则层要素上看,西部城市的消费占比要素排名靠前,而东部三大城市则是消费规模要素排名居前。即东部城市主要以入境旅游外汇收入、入境旅游者的人均天花费直接对目的地产生影响,而西部城市则以提升入境旅游产业在第三产业以及整个国民经济中的地位来对目的地施加影响。 Table 3 表3 表3入境旅游流系统中关联度前5位的准则层要素 Table 3Top 5 elements of the criterion layer’ relevancy degree of inbound tourist flows
位序
北京
上海
广州
西安
成都
昆明
1
客流规模
客流规模
消费规模
客流规模
客流规模
客流比重
2
客流增长
客流比重
旅游流效益
客流比重
客流比重
客流规模
3
客流比重
消费增长
停留时间
消费占比
消费占比
旅游流效益
4
消费规模
消费规模
消费增长
旅游流效益
停留时间
停留时间
5
停留时间
消费占比
客流规模
消费规模
旅游流效益
消费占比
新窗口打开 从目的地系统要素关联序上看(表4),六大领域层要素与入境旅游流系统间的关联性存在如下特征: (1)东部的北京、上海、广州在自然、经济和社会环境要素上的关联度位序整体高于西部的西安、成都和昆明,但在旅游服务设施与服务人员以及基础设施方面的位序却低于西部城市。这表明,在目的地系统对入境旅游流系统的反馈作用中,环境支撑要素在东部城市中发挥的作用更为明显,而在西部城市中的作用较弱;目的地系统的核心供给要素和媒介支撑要素在西部城市的耦合关系中发挥的作用较强,而在东部城市中的作用稍弱。 Table 4 表4 表4目的地系统领域层要素关联度排序 Table 4The average relevancy degree sequence of the domain layer elements of destination
受资料限制,本研究仅选取了中国六大典型城市展开分析,而针对城市群的区域旅游流与城市群目的地的耦合研究应得到重视。另外,灰色关联模型虽可有效揭示两系统内部诸要素的耦合作用强度大小,然而,对各系统要素发挥耦合作用的详细路径却不能给予精准刻画,这尚需在后续研究中应用系统演化模型等方法进行深入探讨。 The authors have declared that no competing interests exist.
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