Impact of the COVID-19 on spatial structure of tourism market:A case study of the Yangtze River Delta

doi:10.3969/j.issn.1004-9479.2025.01.20222218

Abstract

Abstract:

The shift in tourism market preference and market structure adjustment caused by COVID-19 will reshape the spatial pattern of the tourism market. Based on the search data pointed out by Baidu, the spatial autocorrelation and social network analysis methods are applied to explore the evolution characteristics and patterns of the spatial and network structures of the tourism market under the influence of the COVID-19, using the Yangtze River Delta region as a case site. The results show that: ① under the influence of COVID-19, the spatial structure of the tourism market exhibits the characteristics of reduced overall agglomeration and less stable local structure. ② From January to October 2020, tourism market ties within the Yangtze River Delta region gradually strengthened, showing a pattern of gradual diffusion from provincial capitals to other cities and from proximity to distance. ③ Under the influence of COVID-19, the point-out and point-in degrees of most cities in the Yangtze River Delta are significantly reduced, and the diffusion and gathering functions for passenger flow are weakened. In addition, the growth rate of point-in degrees is significantly faster than point-out degrees, indicating that the gathering functions for passenger flow in most cities are more resilient under the influence of the epidemic. ④ Under the influence of COVID-19, the tourism market exhibits obvious proximity grouping characteristics with strong spatial dependence. This study can provide empirical evidence to fully understand the spatial evolution pattern of the tourism market under the influence of a mega public safety crisis and accelerate the recovery of the tourism economy.

Key words: COVID-19, tourism market, spatial structure, network structure, social network analysis

摘要：

新冠疫情引起的旅游市场偏好转变和市场结构调整会重塑旅游市场空间格局。基于百度指数搜索数据，以长三角地区作为案例地，运用空间自相关和社会网络分析方法探讨新冠疫情影响下旅游市场空间结构和网络结构的演变特征及规律。结果表明：①在新冠疫情的影响下，长三角旅游市场空间结构表现出整体集聚程度降低和局部结构稳定性较差的特点。②在2020年1—10月期间，长三角区域内的旅游市场联系逐步加强，呈现出由省会城市向其他城市、由近程向远程逐渐扩散的规律。③在新冠疫情的影响下，长三角大部分城市的点出度和点入度明显降低，对客流的扩散和集聚功能均被弱化，另外，点入度的增长速度明显快于点出度，表明在疫情影响下大部分城市客流集聚功能的恢复能力更强。④在新冠疫情的影响下，旅游市场表现出明显的近程化组团特征，具有强烈的空间依赖性。本研究可为充分认识重大公共安全危机影响下旅游市场的空间演变规律及加快旅游经济复苏提供经验。

关键词: 新冠疫情, 旅游市场, 空间结构, 网络结构, 社会网络分析

Xue WU, Yong YANG, Taiyu SHI. Impact of the COVID-19 on spatial structure of tourism market:A case study of the Yangtze River Delta[J]. World Regional Studies, 2025, 34(1): 181-192.

邬雪, 杨勇, 施泰宇. 新冠疫情对旅游市场空间格局的影响[J]. 世界地理研究, 2025, 34(1): 181-192.

Figures/Tables 4

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时期	年/月	Moran' I指数	Z(I)	P(I)	空间格局	时期	年/月	Moran' I指数	Z(I)	P(I)	空间格局
新冠疫情发生前	2019/1	0.177	2.128	0.033	弱集聚分布	新冠疫情影响下	2020/1	0.081	1.147	0.130	随机分布
	2019/2	0.223	2.577	0.010	弱集聚分布		2020/2	0.041	0.666	0.225	随机分布
	2019/3	0.185	2.130	0.034	弱集聚分布		2020/3	0.076	1.022	0.147	随机分布
	2019/4	0.197	2.281	0.019	弱集聚分布		2020/4	0.057	0.822	0.179	随机分布
	2019/5	0.200	2.321	0.019	弱集聚分布		2020/5	0.095	1.224	0.122	随机分布
	2019/6	0.185	2.202	0.024	弱集聚分布		2020/6	0.114	1.432	0.087	随机分布
	2019/7	0.160	1.944	0.037	随机分布		2020/7	0.055	0.880	0.176	随机分布
	2019/8	0.153	1.862	0.045	随机分布		2020/8	0.953	1.300	0.096	随机分布
	2019/9	0.180	2.067	0.030	弱集聚分布		2020/9	0.079	1.124	0.131	随机分布
	2019/10	0.124	1.490	0.880	随机分布		2020/10	0.087	1.264	0.108	随机分布

时期	年/月	Moran' I指数	Z(I)	P(I)	空间格局	时期	年/月	Moran' I指数	Z(I)	P(I)	空间格局
新冠疫情发生前	2019/1	0.177	2.128	0.033	弱集聚分布	新冠疫情影响下	2020/1	0.081	1.147	0.130	随机分布
	2019/2	0.223	2.577	0.010	弱集聚分布		2020/2	0.041	0.666	0.225	随机分布
	2019/3	0.185	2.130	0.034	弱集聚分布		2020/3	0.076	1.022	0.147	随机分布
	2019/4	0.197	2.281	0.019	弱集聚分布		2020/4	0.057	0.822	0.179	随机分布
	2019/5	0.200	2.321	0.019	弱集聚分布		2020/5	0.095	1.224	0.122	随机分布
	2019/6	0.185	2.202	0.024	弱集聚分布		2020/6	0.114	1.432	0.087	随机分布
	2019/7	0.160	1.944	0.037	随机分布		2020/7	0.055	0.880	0.176	随机分布
	2019/8	0.153	1.862	0.045	随机分布		2020/8	0.953	1.300	0.096	随机分布
	2019/9	0.180	2.067	0.030	弱集聚分布		2020/9	0.079	1.124	0.131	随机分布
	2019/10	0.124	1.490	0.880	随机分布		2020/10	0.087	1.264	0.108	随机分布

子群	新冠疫情发生之前		新冠疫情影响之下
子群	二级层面	三级层面	二级层面	三级层面
Ⅰ	上海、台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	上海	上海、南京、杭州、苏州、无锡、宁波、舟山、黄山	上海、南京、杭州、苏州
Ⅰ	上海、台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	上海、南京、杭州、苏州、无锡、宁波、舟山、黄山	无锡、宁波、舟山、黄山
Ⅱ	无锡、南京、杭州、苏州、宁波、连云港、镇江、扬州、常州、黄山	无锡、南京、杭州、苏州	嘉兴、湖州、绍兴、台州、温州、丽水、衢州、金华	嘉兴、湖州、绍兴、台州、温州
Ⅱ	无锡、南京、杭州、苏州、宁波、连云港、镇江、扬州、常州、黄山	宁波、连云港、镇江、扬州、常州、黄山	嘉兴、湖州、绍兴、台州、温州、丽水、衢州、金华	丽水、衢州、金华
Ⅲ	盐城、淮安、南通、宿迁、泰州、徐州、合肥	盐城、淮安、南通、宿迁、泰州	盐城、南通、镇江、扬州、常州、泰州、连云港、淮安、徐州、宿迁	盐城、南通、镇江、扬州、常州、泰州
Ⅲ	盐城、淮安、南通、宿迁、泰州、徐州、合肥	徐州、合肥	盐城、南通、镇江、扬州、常州、泰州、连云港、淮安、徐州、宿迁	连云港、淮安、徐州、宿迁
Ⅴ	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安、池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安	芜湖、合肥、宣城、铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安	芜湖、合肥、宣城
Ⅴ	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安、池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	芜湖、合肥、宣城、铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安	铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安

子群	新冠疫情发生之前		新冠疫情影响之下
子群	二级层面	三级层面	二级层面	三级层面
Ⅰ	上海、台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	上海	上海、南京、杭州、苏州、无锡、宁波、舟山、黄山	上海、南京、杭州、苏州
Ⅰ	上海、台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	台州、丽水、温州、衢州、舟山、嘉兴、湖州、绍兴、金华	上海、南京、杭州、苏州、无锡、宁波、舟山、黄山	无锡、宁波、舟山、黄山
Ⅱ	无锡、南京、杭州、苏州、宁波、连云港、镇江、扬州、常州、黄山	无锡、南京、杭州、苏州	嘉兴、湖州、绍兴、台州、温州、丽水、衢州、金华	嘉兴、湖州、绍兴、台州、温州
Ⅱ	无锡、南京、杭州、苏州、宁波、连云港、镇江、扬州、常州、黄山	宁波、连云港、镇江、扬州、常州、黄山	嘉兴、湖州、绍兴、台州、温州、丽水、衢州、金华	丽水、衢州、金华
Ⅲ	盐城、淮安、南通、宿迁、泰州、徐州、合肥	盐城、淮安、南通、宿迁、泰州	盐城、南通、镇江、扬州、常州、泰州、连云港、淮安、徐州、宿迁	盐城、南通、镇江、扬州、常州、泰州
Ⅲ	盐城、淮安、南通、宿迁、泰州、徐州、合肥	徐州、合肥	盐城、南通、镇江、扬州、常州、泰州、连云港、淮安、徐州、宿迁	连云港、淮安、徐州、宿迁
Ⅴ	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安、池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安	芜湖、合肥、宣城、铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安	芜湖、合肥、宣城
Ⅴ	芜湖、淮南、蚌埠、滁州、亳州、宣城、六安、池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	池州、宿州、淮北、马鞍山、阜阳、安庆、铜陵	芜湖、合肥、宣城、铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安	铜陵、滁州、池州、马鞍山、宿州、淮北、亳州、阜阳、蚌埠、淮南、安庆、六安

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