中国旅游城市星级饭店韧性时空分异及影响因素

doi:10.13249/j.cnki.sgs.2022.08.016

地理科学 ›› 2022, Vol. 42 ›› Issue (8): 1483-1491.doi: 10.13249/j.cnki.sgs.2022.08.016

中国旅游城市星级饭店韧性时空分异及影响因素

王庆伟¹^,²(), 梅林²^,³^,^*(), 姜洪强², 姚前⁴, 石勇⁵, 付占辉⁶

1.郑州大学管理学院，河南郑州 450001
2.东北师范大学地理科学学院，吉林长春 130024
3.长春财经学院管理学院，吉林长春 130122
4.华东师范大学地理科学学院，上海 200241
5.上海体育学院经济管理学院，上海 200438
6.河南大学地理与环境学院，河南开封 475004

收稿日期:2022-01-01 修回日期:2022-06-18 出版日期:2022-08-25 发布日期:2022-10-11
通讯作者: 梅林 E-mail:wangqingwei@zzu.edu.cn;meil682@nenu.edu.cn
作者简介:王庆伟（1979-），女，河南周口人，讲师，博士研究生，主要从事旅游地理研究。E-mail: wangqingwei@zzu.edu.cn
基金资助:
国家自然科学基金项目(41971202);国家自然科学基金项目(41601566);河南省高校人文社会科学研究一般项目资助(2022-ZDJH-00102)

Spatio-temporal Differentiation and Influencing Factors of Star-rated Hotels’ Resilience of China’s Tourism Cities

Wang Qingwei¹^,²(), Mei Lin²^,³^,^*(), Jiang Hongqiang², Yao Qian⁴, Shi Yong⁵, Fu Zhanhui⁶

1. School of Management, Zhengzhou University, Zhengzhou 450001, Henan, China
2. School of Geographical Sciences, Northeast Normal University, Changchun 130024, Jilin, China
3. School of Management, Changchun University of Finance and Economics, Changchun 130122, Jilin, China
4. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
5. Department of Economics and Management, Shanghai University of Sport, Shanghai 200438, China
6. College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China

Received:2022-01-01 Revised:2022-06-18 Online:2022-08-25 Published:2022-10-11
Contact: Mei Lin E-mail:wangqingwei@zzu.edu.cn;meil682@nenu.edu.cn
Supported by:
National Natural Science Foundation of China(41971202);National Natural Science Foundation of China(41601566);Humanities and Social Sciences Program of Higher Education in Henan Province(2022-ZDJH-00102)

摘要/Abstract

摘要：

新冠肺炎疫情重创全球旅游业、饭店业，但不同城市旅游业、饭店业应对和适应扰动的能力不同，即韧性存在差异。以中国41个旅游城市的星级饭店为研究对象，构建基于累积损失的韧性评估模型，运用SARIMA、随机森林等方法，探讨新冠肺炎疫情干扰下2020年旅游城市星级饭店韧性的时空分异特征及影响因素。研究发现：①在中国疫情防控成效逐渐趋好的态势下，中国旅游城市星级饭店韧性不断增强，但韧性演变存在差异。②中国旅游城市星级饭店韧性空间分异明显，存在交通廊道效应和地理邻近效应。③影响旅游城市星级饭店韧性水平的主要因素依次为：客房平均出租率增长率、餐饮与客房收入比、人均公园绿地面积、国内旅游收入增长率、空气质量优良率、城镇居民人均可支配收入等，它们对旅游城市星级饭店韧性水平的影响呈现出非线性的复杂作用。对指导新冠肺炎疫情干扰下旅游城市星级饭店韧性增强具有重要的参考价值。

关键词: 旅游城市, 星级饭店韧性, 新冠肺炎疫情, 随机森林模型

Abstract:

COVID-19 has caused heavy losses to the tourism industry and the hotel industry. However, it is different that the ability of tourism cities’ hotels to cope with and adapt to the epidemic, that is, their resilience is different. Taking star-rated hotels of 41 tourism cities in China as an example, this paper constructs a resilience evaluation model based on their cumulative loss. Using SARIMA Model and Random Forest Model, this paper analyzes the spatio-temporal differentiation and factors of star-rated hotels’ resilience of tourism cities in 2020 under the impact of COVID-19. The results show that: 1) With the gradual improvement of the epidemic prevention and control in China, star-rated hotels’ resilience of tourism cities continues to increase, but the evolution of their resilience is different. 2) From a spatial point of view, it is different that star-rated hotels’ resilience of tourism cities. The resilience’s spatial differentiation shows the traffic corridor effect and the geographical proximity effect. 3) There are six important factors affecting star-rated hotels’ resilience of tourism cities, which are the growth rate of average rental rate, the ratio of catering to room income, the urban parks and green spaces per capita, the growth rate of domestic tourism income, the rate of excellent air quality, and the disposable income of urban residents per capita, and which have complex and nonlinear effects on the resilience. This paper is beneficial to star-rated hotels’ resilience of tourism cities under the impact of COVID-19.

Key words: tourism cities, star-rated hotels’ resilience, COVID-19, Random Forest Model

中图分类号:

F129.9

王庆伟, 梅林, 姜洪强, 姚前, 石勇, 付占辉. 中国旅游城市星级饭店韧性时空分异及影响因素[J]. 地理科学, 2022, 42(8): 1483-1491.

Wang Qingwei, Mei Lin, Jiang Hongqiang, Yao Qian, Shi Yong, Fu Zhanhui. Spatio-temporal Differentiation and Influencing Factors of Star-rated Hotels’ Resilience of China’s Tourism Cities[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(8): 1483-1491.

图/表 8

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区域	旅游城市	专业型旅游城市	半专业型旅游城市	综合型旅游城市
注：不含港澳台地区。
东部	北京、天津、石家庄、秦皇岛、沈阳、上海、南京、苏州、宁波、温州、福州、厦门、泉州、济南、青岛、广州、深圳、珠海、东莞、南宁、桂林、三亚	秦皇岛、南宁、桂林、三亚	北京、天津、上海、深圳、南京	石家庄、沈阳、苏州、宁波、温州、福州、厦门、泉州、济南、青岛、广州、珠海、东莞
中部	太原、呼和浩特、长春、哈尔滨、黄山、南昌、郑州、武汉、宜昌、长沙	黄山	武汉、长沙、南昌	太原、呼和浩特、长春、哈尔滨、郑州、宜昌
西部	重庆、成都、贵阳、拉萨、西安、兰州、西宁、银川、乌鲁木齐	贵阳、拉萨	重庆、成都、西安	兰州、西宁、银川、乌鲁木齐

目标层	准则层	指标层	指标含义	IncMSE/%
注: IncMSE/%为平均增加的均方误差，值越高，表示该指标越重要；未含港澳台数据。
吸收能力	旅游吸引物	高等级景区数量X₁/个	城市旅游吸引物品质	-1.0634
吸收能力	接待服务	高星级饭店数量X₂/家	城市星级饭店业接待服务水平	-0.1644
恢复能力	生态环境	人均公园绿地面积X₃/（m²/人）	城市绿化水平	2.7374
	生态环境	空气质量优良率X₄/%	城市空气质量优良状况	0.5332
	经济基础	城镇居民人均可支配收入X₅/(元/人)	城市居民生活水平	0.2907
	旅游市场	城市常住人口数X₆/人	城际潜在客源市场规模	-1.1873
	旅游市场	省份常住人口数X₇/人	省际潜在客源市场规模	-2.6063
	政府政策	支持旅游业政策X₈/个	政府支持城市旅游业力度	-2.1799
适应能力	旅游业创新	旅游专利授权量X₉/件	城市旅游业科技创新能力	-3.8102
	旅游业创新	旅游学术论文发表量X₁₀/篇	城市旅游业知识创新能力	-2.5580
	饭店业创新	餐饮与客房收入比X₁₁/%	城市星级饭店业创新能力	5.9999
	旅游发展潜力	国内旅游收入增长率X₁₂/%	城市国内旅游业经济潜力	1.0680
		国内旅游人数增长率X₁₃/%	城市国内旅游业规模潜力	0.0962
		客房平均出租率增长率X₁₄/%	城市星级饭店业经营潜力	7.7143

中国旅游城市星级饭店韧性时空分异及影响因素

Spatio-temporal Differentiation and Influencing Factors of Star-rated Hotels’ Resilience of China’s Tourism Cities

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