基于BP神经网络的旅游经济系统脆弱性省际空间分异

doi:10.18402/resci.2019.12.09

资源科学 ›› 2019, Vol. 41 ›› Issue (12): 2248-2261.doi: 10.18402/resci.2019.12.09

基于BP神经网络的旅游经济系统脆弱性省际空间分异

马慧强^1,², 廉倩文¹, 论宇超³, 席建超²()

^1. 山西财经大学文化旅游学院,太原 030000
^2. 中国科学院地理科学与资源研究所/陆地表层格局与模拟重点实验室,北京 100101
^3. 辽宁师范大学地理科学学院,大连 041000

收稿日期:2019-03-25 修回日期:2019-09-21 出版日期:2019-12-25 发布日期:2019-12-25
通讯作者: 席建超
作者简介:马慧强,男,山西大同人,博士,副教授,研究方向为经济地理,旅游开发与规划。E-mail: Mahuiqiang001@126.com
基金资助:
国家自然科学基金项目(41671151);山西省教育厅项目(2019W074);山西省体育局项目(18TY136)

Spatial differentiation of tourism economic system vulnerability based on BP neural network in different provinces of China

MA Huiqiang^1,², LIAN Qianwen¹, LUN Yuchao³, XI Jianchao²()

^1. College of Culture and Tourism, Shanxi University of Finance and Economics, Taiyuan 030000, China
^2. Institute of Geographic Sciences and Natural Resources Research, CAS /Key Laboratory of Land Surface Pattern and Simulation, Beijing 100101, China
^3. College of Geographic Sciences, Liaoning Normal University, Dalian 041000, China

Received:2019-03-25 Revised:2019-09-21 Online:2019-12-25 Published:2019-12-25
Contact: XI Jianchao

摘要/Abstract

摘要：

开展旅游经济系统脆弱性评价与影响因素研究,是制定旅游经济科学发展策略,提高区域旅游发展质量的客观要求。本文在探讨旅游经济系统脆弱性内涵的基础上,从旅游经济系统敏感性和应对能力两个方面来构建旅游经济系统脆弱性评价指标体系,并运用BP人工神经网络模型、脆弱性评价指数模型、地理探测器等研究方法,对中国30个省级行政单元（不包括西藏和港澳台地区）的旅游经济系统脆弱性空间分异特征及影响因素进行分析。结果表明：①中国旅游经济系统整体处于较高脆弱、中等敏感、较高应对能力状态,且旅游经济系统脆弱性、敏感性和应对能力省际空间分异明显;②中国旅游经济系统脆弱性、敏感性和应对能力均呈现“集群化”和“极差化”分布特征,东部地区旅游经济系统脆弱性较高,且内部差异显著,中部和西部地区脆弱性较低,内部差异较不明显;③中国旅游经济系统脆弱性空间分异是不同影响因素共同作用的结果,其中产业结构多样化指数对脆弱性空间分异的影响最大,旅游外汇收入占旅游总收入比重对脆弱性空间分异的影响最小。研究结果可丰富旅游可持续发展理论,并为解决旅游经济质量提升问题等提供科学依据。

关键词: 旅游经济系统, 脆弱性, BP神经网络, 地理探测器, 空间分异

Abstract:

Vulnerability evaluation of tourism economic systems and influencing factor identification are necessary for improving the quality of regional tourism development and formulating a scientific strategy for such development. This study constructed an index system of vulnerability assessment of tourism economic system from three aspects of vulnerability, sensitivity, and coping ability of the system. By using the back propagation (BP) artificial neural network model, vulnerability evaluation index, geographic detector, and other research methods, the spatial differentiation characteristics and influencing factors of tourism economic system vulnerability of 30 provincial-level administrative units in China (excluding Tibet, Hong Kong, Macao, and Taiwan) were evaluated and analyzed. The results show that: (1) The tourism economic system of China is in a state of high vulnerability, medium sensitivity, and high coping capacity as a whole, and the vulnerability, sensitivity, and coping ability of China’s tourism economic system are clearly different among the examined provinces; (2) The vulnerability, sensitivity, and coping capacity of China’s tourism economic system present the characteristics of clustering and polarized distribution. The vulnerability of the tourism economic system in the eastern region is relatively high, with significant internal differences, while the vulnerability of the central and western regions is relatively low, with less obvious internal differences; (3) The spatial differentiation of vulnerability in China’s tourism economic system is the result of the joint action of different influencing factors, among which the diversification of industrial structure has the greatest influence on the spatial differentiation of vulnerability, and the proportion of tourism foreign exchange income in total tourism income has the least influence on the spatial differentiation of vulnerability. The results of this study provide some scientific basis for enriching the theory of sustainable tourism development and solving the problems of improving the quality of tourism economy.

Key words: tourism economic system, vulnerability, BP neural network, geographical detector, spatial differentiation

马慧强, 廉倩文, 论宇超, 席建超. 基于BP神经网络的旅游经济系统脆弱性省际空间分异[J]. 资源科学, 2019, 41(12): 2248-2261.

MA Huiqiang, LIAN Qianwen, LUN Yuchao, XI Jianchao. Spatial differentiation of tourism economic system vulnerability based on BP neural network in different provinces of China[J]. Resources Science, 2019, 41(12): 2248-2261.

图/表 9

表1

旅游经济系统脆弱性评价指标体系"

目标层	准则层	代码	指标层	指标解释及性质	主观权重	客观权重	组合权重
旅游经济系统脆弱性指数	敏感性指数	S1 S2 S3 S4 S5 S6 S7	旅游收入占GDP比重/% 旅游外汇收入占外贸出口总值比重/% 旅游业增长弹性系数旅游外汇收入占旅游总收入比重/% 产业结构多样化指数入境旅游人数占游客总人数比重/% 外贸依存度/%	经济对旅游产业的依赖性（+）外汇收入对旅游业的依赖性（+）旅游增长对经济增长的弹性（+）旅游业对入境旅游的依赖程度（+）产业结构多样化程度（-）旅游业对入境游客的吸引力（+）旅游经济内外需求的平衡性（+）	0.0204 0.0473 0.0050 0.0079 0.0078 0.0766 0.0784	0.0196 0.0309 0.0038 0.0337 0.0064 0.0902 0.0698	0.0200 0.0391 0.0044 0.0208 0.0071 0.0834 0.0741
		S8 S9 S10 S11 S12 S13 S14	旅游业从业人数占总从业人员比重/% 城镇登记失业率/% 交通事故发生数/起交通事故伤亡人数/人交通事故财产损失/万元文盲率/% 犯罪率/%	地区就业对旅游业的依赖度（+）城镇失业状况（+）内部灾害事故发生情况（+）游客交通安全性（+）交通事故对经济发展的影响（+）地区文化教育普及和发达程度（+）社会环境安全性（+）	0.0112 0.0043 0.0522 0.0496 0.0303 0.0313 0.0623	0.0226 0.0119 0.0492 0.0438 0.0293 0.0307 0.0591	0.0169 0.0081 0.0507 0.0467 0.0298 0.0310 0.0607
		S15 S16 S17	自然灾害经济损失占比/% 自然灾害受灾人口比重/% 森林覆盖率/%	自然灾害对经济发展的影响（+）自然灾害对游客安全的影响（+）地区自然生态状况（-）	0.1079 0.0259 0.0754	0.0369 0.0367 0.0764	0.0724 0.0313 0.0759
	应对能力指数	R1 R2 R3 R4 R5 R6	人均旅游GDP/万元旅游收入增长率/% 旅游劳动生产率/% 旅游资源丰富度/分食宿企业收入比重/% 社会消费品零售总额/亿元	旅游经济实力（-）旅游经济增长能力（-）旅游产品生产效率（-）旅游资源禀赋（-）地区食宿接待能力（-）地区消费接待能力（-）	0.0036 0.0129 0.0323 0.0093 0.0276 0.0089	0.0134 0.0157 0.0357 0.0089 0.0264 0.0121	0.0085 0.0143 0.0340 0.0091 0.0270 0.0105
		R7 R8 R9 R10 R11 R12 R13 R14	公共服务支出占财政支出比重/% 教育支出占财政支出比重/% 等级公路、铁路网密度/（km/km²）互联网普及率/% 邮电业务量/亿元文化体育投资占比/% 城镇化率/% 医疗卫生机构数/个	公共服务财政支持力度（-）教育财政支持力度（-）地区交通运输接待能力（-）信息化服务程度（-）信息传递服务能力（-）居民休闲环境投入能力（-）城镇化发展情况（-）医疗救援能力（-）	0.0358 0.0267 0.0204 0.0310 0.0136 0.0153 0.0091 0.0121	0.0346 0.0259 0.0186 0.0374 0.0228 0.0125 0.0141 0.0257	0.0352 0.0263 0.0195 0.0342 0.0182 0.0139 0.0116 0.0189
		R15 R16 R17	生活垃圾无害化处理率/% 工业固体废弃物综合利用率/% 环境保护投资增长率/%	生活废弃物处理能力（-）工业废弃物处理能力（-）旅游发展环境支持保障度（-）	0.0251 0.0149 0.0096	0.0197 0.0127 0.0168	0.0224 0.0138 0.0132

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S	低	中	高	R	低	中	高
S1	[0.05,0.14）	[0.14,0.23）	[0.23,0.31]	R1	[0.22,0.81）	[0.81,1.40）	[1.40,1.99]
S2	[0.00,0.03）	[0.03,0.05）	[0.05,0.08]	R2	[-0.23,0.02）	[-0.02,0.19）	[0.19,0.40]
S3	[-5.71,6.49）	[6.49,18.69）	[18.69,30.89]	R3	[987.53,3419.22）	[3419.22,5850.90）	[5850.90,8282.59]
S4	[0.00,0.04）	[0.04,0.08）	[0.08,0.11]	R4	[170.00,1026.67）	[1026.67,1883.33）	[1883.33,2740.00]
S5	[-1.03,-0.87）	[-0.87,-0.72）	[-0.72,-0.56]	R5	[27.33,100.45）	[100.45,173.56）	[173.56,246.68]
S6	[0.00,0.04）	[0.04,0.07）	[0.07,0.10]	R6	[314.90,5016.12)	[5016.12,9717.33）	[9717.33,14418.55]
S7	[0.05,0.43）	[0.43,0.82）	[0.82,1.21]	R7	[0.05,0.08）	[0.08,0.10）	[0.10,0.13]
S8	[0.16,0.51）	[0.51,0.85）	[0.85,1.20]	R8	[0.12,0.15）	[0.15,0.17）	[0.17,0.20]
S9	[1.30,2.37）	[2.37,3.43）	[3.43,4.50]	R9	[1.29,9.47)	[9.47,17.66）	[17.66,25.8]
S10	[1029.00,9501.00)	[9501.00,17973.00)	[17973.00,26445.00]	R10	[34.10,47.83)	[47.83,61.57）	[61.57,75.30]
S11	[1526.00,12920.67)	[12920.6,24315.33）	[24315.33,35710.00]	R11	[80.75,1171.62)	[1171.62,2262.48）	[2262.48,3353.35]
S12	[462.90,2748.73)	[2748.73,5034.57）	[5034.57,7320.40]	R12	[0.00,0.01)	[0.01,0.02）	[0.02,0.03]
S13	[0.01,0.04)	[0.04,0.08）	[0.08,0.11]	R13	[0.40,0.57）	[0.57,0.73）	[0.73,0.90]
S14	[0.00,0.01)	[0.01,0.02）	[0.02,0.03]	R14	[79.32,257.62）	[257.62,435.92）	[435.92,614.25]
S15	[0.00,0.02)	[0.02,0.03）	[0.03,0.05]	R15	[0.00,0.02）	[0.02,0.04）	[0.04,0.06]
S16	[0.00,0.23)	[0.23,0.46）	[0.46,0.69]	R16	[30.02,36.69）	[36.69,43.36）	[43.36,50.03]
S17	[17.26,33.29）	[33.29,49.32）	[49.32,65.35]	R17	[0.03,0.05)	[0.05,0.08）	[0.08,0.11]

省份	S	R	V	省份	S	R	V
北京	0.178	0.124	1.438	河南	0.104	0.187	0.556
天津	0.132	0.187	0.706	湖北	0.115	0.170	0.678
河北	0.098	0.187	0.523	湖南	0.142	0.155	0.913
山西	0.091	0.204	0.446	广东	0.374	0.124	3.019
内蒙古	0.147	0.195	0.756	广西	0.139	0.178	0.778
辽宁	0.107	0.197	0.544	海南	0.203	0.171	1.189
吉林	0.102	0.266	0.383	重庆	0.140	0.191	0.729
黑龙江	0.100	0.267	0.375	四川	0.148	0.204	0.727
上海	0.229	0.156	1.471	贵州	0.142	0.215	0.66
江苏	0.163	0.122	1.333	云南	0.227	0.198	1.148
浙江	0.242	0.121	1.989	陕西	0.147	0.174	0.846
安徽	0.188	0.166	1.13	甘肃	0.091	0.256	0.356
福建	0.198	0.152	1.303	宁夏	0.106	0.251	0.423
江西	0.092	0.173	0.533	青海	0.089	0.208	0.430
山东	0.143	0.101	1.414	新疆	0.138	0.215	0.644

		平均值	极差	标准差	变异系数
东部	S	0.184	0.276	0.075	0.408
	R	0.205	0.143	0.043	0.210
	V	1.224	2.589	0.725	0.592
中部	S	0.120	0.097	0.033	0.275
	R	0.163	0.086	0.030	0.184
	V	0.647	0.928	0.289	0.447
西部	S	0.137	0.138	0.041	0.299
	R	0.176	0.150	0.044	0.250
	V	0.770	1.082	0.350	0.455
全国	S	0.151	0.284	0.061	0.404
	R	0.184	0.166	0.043	0.234
	V	0.915	2.663	0.570	0.623

	S5	R8	S6	R2	S4
q statistic	0.576	0.487	0.564	0.502	0.271
p value	0.028	0.179	0.052	0.012	0.213

	S4	S5	S6	R2	R8
S4	0.562
S5	0.857	0.452
S6	0.709	0.769	0.571
R2	0.679	0.781	0.710	0.562
R8	0.693	0.743	0.773	0.679	0.278

基于BP神经网络的旅游经济系统脆弱性省际空间分异

Spatial differentiation of tourism economic system vulnerability based on BP neural network in different provinces of China

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