地理学报 ›› 2017, Vol. 72 ›› Issue (2): 242-255.doi: 10.11821/dlxb201702005

• 城市与区域发展 • 上一篇    下一篇

智慧旅游城市旅游竞争力评价

黄松1(), 李燕林2(), 戴平娟1   

  1. 1. 广西师范大学历史文化与旅游学院,桂林 541001
    2. 广西师范大学职业技术师范学院,桂林 541004
  • 收稿日期:2016-09-10 修回日期:2016-11-28 出版日期:2017-02-15 发布日期:2017-04-28
  • 作者简介:

    作者简介:黄松(1967-), 男, 博士, 教授, 中国地理学会会员(S110005868M), 主要从事旅游开发与规划研究。E-mail: hs0773@126.com

  • 基金资助:
    国家自然科学基金项目(41361019);国家社会科学基金项目(08BMZ041);教育部“新世纪优秀人才支持计划”(NCET-12-0652)

Evaluation of tourism competitiveness of Chinesesmart tourism city

Song HUANG1(), Yanlin LI2(), Pingjuan DAI1   

  1. 1.College of Historical Culture and Tourism, Guangxi Normal University, Guilin 541001, Guangxi, China
    2. College of Vocation-Technical Teachers, Guangxi Normal University, Guilin 541004, Guangxi, China
  • Received:2016-09-10 Revised:2016-11-28 Online:2017-02-15 Published:2017-04-28
  • Supported by:
    National Natural Science Foundation of China, No.41361019;National Social Science Foundation of China, No.08BMZ041;Program for New Century Excellent Talents in University, No.NCET-12-0652

摘要:

提升旅游竞争力是智慧旅游城市建设的核心目标。在借鉴前人研究的基础上,建立包括5个一级指标、14个二级指标和33个三级指标的智慧旅游城市旅游竞争力评价指标体系,选取北京、南京、武汉、成都、大连、厦门等12个首批国家智慧旅游试点城市,通过模拟仿真运算构建智慧旅游城市旅游竞争力评价BP神经网络模型,并运用该模型对上述智慧旅游城市旅游竞争力进行评价和分析。结果表明:① 旅游科技创新竞争力是影响智慧旅游城市旅游竞争力最关键的一级指标,其他一级指标按重要性排序依次为旅游经济发展竞争力、旅游发展保障竞争力、旅游发展潜力竞争力和旅游环境支撑竞争力;② 中国智慧旅游城市旅游竞争力整体水平不高且极不均衡,根据评价等级和竞争态势分为5类。第一类北京市是中国智慧旅游城市旅游竞争力的标杆,5项一级指标评价值均领先于其他城市,总评价值高达0.887,评价等级AA、竞争态势“优势”;第二类南京市旅游环境支撑竞争力评价值与北京市并列第1,旅游经济发展竞争力、旅游科技创新竞争力和旅游发展保障竞争力评价值均位列第2,总评价值0.536,明显高于除北京市外的其他智慧旅游城市,评价等级BB、竞争态势“较强”;第三类武汉、大连、成都、厦门、镇江、温州、烟台7市代表目前中国智慧旅游城市旅游竞争力的普遍水平,5项一级指标评价值偏低且差距不明显,总评价值在0.3~0.4之间,评价等级B、竞争态势“一般”;第四类福州、洛阳2市绝大多数一级指标评价值较低,总评价值在0.2~0.3之间,评价等级CC、竞争态势“较弱”;第五类黄山市总评价值仅0.176,评价等级C、竞争态势“弱势”;③ 构建的智慧旅游城市旅游竞争力评价BP神经网络模型具有较好的科学性、普适性和可操作性,根据评价值的大小判断智慧旅游城市旅游竞争力的优劣,比照指标体系寻找差距并加以完善,同时对评价指标进行动态监测,将是智慧旅游城市旅游竞争力研究的重要发展方向。

关键词: 智慧旅游城市, 旅游竞争力评价, BP神经网络模型, 中国

Abstract:

To enhance tourism competitiveness is the core objective of the construction of a smart tourism city. On the basis of the previous studies, the research builds an index system on the tourism competitiveness evaluation of smart tourism city, including 5 indexes of first grade, 14 of second grade and 33 of third grade, selects the first 12 pilot cities of national smart tourism such as Beijing, Nanjing, Wuhan, Chengdu, Dalian and Xiamen, constructs the BP neural network model of tourism competitiveness evaluation of smart tourism city through the simulation computation, and applies the model to evaluate and analyze the tourism competitiveness of the smart tourism cities mentioned above. The results indicated that the scientific and technological innovation of the smart tourism city is the most important first grade index which impacts the tourism competitiveness. The other first grade indexes are economic development of smart tourism city, support capability of tourism development, tourism development potentiality and environmental support capability ranked according to their importance; the overall level of tourism competitiveness of Chinese smart tourism cities is not high and extremely uneven and the smart tourism cities are divided into five categories according to their own evaluation level and competition situation. The first category includes Beijing, which is the benchmark of tourism competitiveness of Chinese smart tourism cities. All the five first grade indexes of Beijing run ahead of other smart tourism cities. Its comprehensively-evaluated value reaches 0.887, rated AA level, and its competition situation is superior. The second category includes Nanjing, whose evaluation value of the environmental support capability of smart tourism city is tied for the first place with Beijing. Its evaluation values of economic development power, scientific and technological innovation, and support capability of development are all ranked 2nd. The comprehensively-evaluated value of Nanjing is 0.536, rated BB level, which is significantly higher than that of other smart tourism cities except Beijing and its competition situation is better. The third category cities, including Wuhan, Dalian, Chengdu, Wenzhou, Xiamen, Zhenjiang and Yantai, are representatives of the present general level of tourism competitiveness of Chinese smart tourism cities, whose 5 first grade index values are all low and there are no remarkable gaps between them. Their comprehensively-evaluated values are between 0.3 and 0.4, rated B level and their competition situation is on the average. The fourth category includes Fuzhou and Luoyang, the vast majority of its first index values are lower. Their comprehensively-evaluated values are between 0.2 and 0.3, rated CC level and their competition situation is weak. The fifth category includes Huangshan, whose comprehensively-evaluated value is just 0.176, rated C level and its competition situation is weaker. The BP neural network model of tourism competitiveness evaluation of smart tourism city is of better scientificity, universality and operability. Judging the tourism competitiveness of smart tourism cities according to their evaluation values, comparing it with the index system to find and bridge the gaps, and monitoring the evaluation index dynamically will be an important direction of tourism competitiveness research of smart tourism cities.

Key words: smart tourism city, tourism competitiveness evaluation, BP neural network model, China