智慧旅游城市旅游竞争力评价

doi:10.11821/dlxb201702005

摘要/Abstract

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

黄松, 李燕林, 戴平娟. 智慧旅游城市旅游竞争力评价[J]. 地理学报, 2017, 72(2): 242-255.

Song HUANG, Yanlin LI, Pingjuan DAI. Evaluation of tourism competitiveness of Chinesesmart tourism city[J]. Acta Geographica Sinica, 2017, 72(2): 242-255.

图/表 12

表1

智慧旅游城市旅游竞争力评价指标体系及权重

一级指标	二级指标	三级指标	指标类型	指标编号
旅游经济发展竞争力(0.233)	智慧旅游经济水平 (0.174)	旅游总收入(0.054)	☆	E₁
		城镇居民可支配收入(0.034)	☆	E₂
		旅游业占 GDP 的比重(0.086)	☆	E₃
	智慧旅游营销能力 (0.059)	旅游电子商务平台水平(0.044)	△	E₄
	智慧旅游营销能力 (0.059)	新媒体旅游营销能力(0.015)	△	E₅
旅游科技创新竞争力(0.476)	智慧旅游硬件支撑条件 (0.120)	基础网络覆盖率(0.052)	☆	E₆
		旅游物联网建设水平(0.034)	△	E₇
		智慧旅游公共服务平台水平(0.012)	△	E₈
		旅游信息数据库建设水平(0.022)	△	E₉
	智慧旅游科技创新能力(0.356)	地方财政科技支出(0.057)	☆	E₁₀
		高新技术产业产值(0.089)	☆	E₁₁
		ICT研发投入(0.023)	☆	E₁₂
		发明专利授权量(0.016)	☆	E₁₃
		研究与实验发展经费(0.034)	☆	E₁₄
		信息化发展指数(0.137)	☆	E₁₅
旅游发展潜力竞争力(0.090)	旅游资源禀赋 (0.067)	旅游资源丰度(0.022)	△	E₁₆
	旅游资源禀赋 (0.067)	旅游资源垄断性(0.045)	△	E₁₇
	旅游区位条件 (0.023)	年客运总量(0.008)	☆	E₁₈
	旅游区位条件 (0.023)	旅游智能交通水平(0.015)	△	E₁₉
旅游环境支撑竞争力(0.062)	社会经济环境 (0.039)	人均GDP(0.026）	☆	E₂₀
	社会经济环境 (0.039)	全社会固定资产投资总额(0.013)	☆	E₂₁
	社会文化环境 (0.015)	居民对游客友好程度(0.004)	△	E₂₂
	社会文化环境 (0.015)	旅游创新能力(0.011)	△	E₂₃
	社会自然环境 (0.008)	环境质量自动化监测率(0.002)	☆	E₂₄
	社会自然环境 (0.008)	城市绿化覆盖率(0.006)	☆	E₂₅
旅游发展保障竞争力(0.139)	智慧旅游基础设施 (0.072)	移动电话年末用户数(0.019)	☆	E₂₆
	智慧旅游基础设施 (0.072)	国际互联网用户数(0.053)	☆	E₂₇
	智慧旅游服务 (0.024)	智慧旅游门户网站数(0.007)	☆	E₂₈
	智慧旅游服务 (0.024)	智慧旅游服务应用水平(0.017)	△	E₂₉
	智慧旅游保障体系 (0.043)	智慧旅游政策法规完善程度(0.005)	△	E₃₀
		智慧旅游投入和持续保障能力(0.020)	△	E₃₁
		智慧旅游应急体系水平(0.006)	△	E₃₂
		智慧旅游发展规划及实施水平(0.012)	△	E₃₃

表1

图1

表2

表3

智慧旅游城市旅游竞争力评价指标无量纲化值

指标编号	智慧旅游城市
指标编号	北京	南京	镇江	武汉	成都	福州	大连	厦门	黄山	温州	烟台	洛阳
E₁	1.000	0.297	0.051	0.406	0.333	0.029	0.163	0.094	0.000	0.083	0.066	0.063
E₂	1.000	0.932	0.586	0.460	0.430	0.419	0.477	0.783	0.000	0.828	0.588	0.141
E₃	0.179	0.135	0.131	0.170	0.123	0.000	0.065	0.214	1.000	0.111	0.019	0.152
E₄	1.000	1.000	0.667	0.667	0.500	0.500	0.667	0.667	0.000	0.500	0.667	0.167
E₅	1.000	1.000	1.000	1.000	0.750	0.750	1.000	0.750	0.000	0.500	0.750	0.250
E₆	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	1.000	1.000	1.000
E₇	1.000	0.833	0.833	0.833	0.667	0.667	0.667	0.667	0.000	0.667	0.833	0.333
E₈	1.000	1.000	1.000	0.500	0.500	0.500	0.500	0.500	0.000	0.000	0.500	0.000
E₉	1.000	0.800	0.600	0.800	0.400	0.400	0.400	0.600	0.000	0.400	0.600	0.200
E₁₀	1.000	0.215	0.032	0.132	0.218	0.025	0.007	0.022	0.000	0.021	0.052	0.005
E₁₁	1.000	0.157	0.105	0.185	0.164	0.091	0.074	0.113	0.000	0.040	0.167	0.038
E₁₂	1.000	0.406	0.335	0.228	0.174	0.124	0.223	0.249	0.090	0.108	0.219	0.000
E₁₃	1.000	0.224	0.051	0.164	0.170	0.054	0.066	0.040	0.000	0.038	0.026	0.025
E₁₄	1.000	0.470	0.192	0.135	0.105	0.056	0.157	0.080	0.000	0.127	0.080	0.010
E₁₅	1.000	0.406	0.172	0.231	0.150	0.184	0.374	0.297	0.063	0.286	0.302	0.000
E₁₆	1.000	0.737	0.211	0.211	1.000	0.053	0.000	0.000	0.263	0.000	0.000	1.000
E₁₇	1.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	1.000
E₁₈	1.000	0.147	0.000	0.097	0.085	0.124	0.036	0.035	0.009	0.253	0.036	0.092
E₁₉	1.000	0.714	0.571	0.571	0.571	0.571	0.714	0.714	0.000	0.429	0.714	0.286
E₂₀	0.862	0.970	0.899	0.873	0.452	0.456	1.000	0.682	0.000	0.218	0.218	0.171
E₂₁	1.000	0.700	0.227	0.920	0.866	0.547	0.887	0.146	0.000	0.357	0.506	0.353
E₂₂	0.000	0.333	0.667	0.667	1.000	1.000	0.667	0.333	1.000	0.333	0.667	0.333
E₂₃	1.000	0.800	0.400	0.600	0.400	0.600	0.400	0.600	0.000	0.200	0.400	0.200
E₂₄	1.000	0.667	0.333	0.667	0.333	0.667	0.667	0.667	0.000	0.333	0.333	0.000
E₂₅	1.000	1.000	0.500	0.000	0.000	1.000	1.000	0.500	1.000	0.000	0.500	0.200
E₂₆	1.000	0.249	0.058	0.130	0.528	0.198	0.186	0.115	0.000	0.253	0.179	0.132
E₂₇	0.544	0.311	0.057	0.359	0.273	0.191	0.113	0.569	0.000	1.000	0.153	0.410
E₂₈	0.750	1.000	0.750	0.500	0.500	0.500	0.250	0.500	0.000	0.250	0.500	0.000
E₂₉	1.000	1.000	0.750	0.750	0.750	0.500	0.500	0.500	0.000	0.250	0.500	0.250
E₃₀	1.000	1.000	1.000	0.750	0.750	0.500	0.750	0.750	0.000	0.250	0.750	0.250
E₃₁	1.000	0.750	0.500	0.250	0.250	0.500	0.500	0.500	0.000	0.250	0.250	0.250
E₃₂	1.000	0.667	1.000	0.667	0.667	0.667	0.667	0.667	0.000	0.000	0.333	0.000
E₃₃	1.000	0.667	0.667	0.333	0.333	0.667	0.667	0.667	0.000	0.000	0.333	0.333

表3

表4

表5

图2

图3

表6

表7

表8

图4

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隐含层节点数	5	6	7	8	9	10	11	12
均方误差mse	0.06508	0.06234	0.00073	0.08751	0.05922	0.05095	0.11024	0.03215
隐含层节点数	13	14	15	16	17	18	19	20
均方误差mse	0.05569	0.00022	0.10279	0.02015	0.04896	0.00874	0.00198	0.00107

类别	南京	镇江	武汉	成都	福州	大连	厦门	黄山	温州	烟台
期望值	0.546	0.334	0.380	0.342	0.275	0.341	0.358	0.158	0.296	0.301
训练值	0.536	0.340	0.384	0.348	0.269	0.359	0.344	0.176	0.303	0.302
绝对误差	0.010	0.006	0.004	0.006	0.006	0.019	0.014	0.018	0.007	0.001
相对误差(%)	1.77	1.86	0.95	1.69	2.28	5.42	3.77	11.20	2.33	0.38