Spatio-Temporal Evolution Characteristics and Spatial Differences in Urban Tourism Network Attention in China: Based on the Baidu Index

Abstract

There is a long-term equilibrium relationship between urban tourism network attention (UTNA) and the volume of tourism. Understanding the spatio-temporal patterns of UTNA before and after the COVID-19 pandemic has important implications for destination management. On the basis of the Baidu index, this study collected the tourism network attention of 337 prefecture-level cities in China from 2018 to 2021 through data mining and analyzed the spatio-temporal evolution characteristics and regional differences in UTNA in China by using the seasonal concentration index, the Zipf model and the Dagum Gini coefficient. The results show that, firstly, the UTNA decreased significantly during the study period, with significant seasonal variability and spatial unevenness; April, July, August and October comprise the high season, while January, February, November and December comprise the low season. Secondly, in terms of regional heterogeneity, the seasonal differences in UTNA are generally greater in the northeast regions than in the central, and western regions, and are the smallest in the eastern regions. Thirdly, the UTNA shows a strong rank-scale characteristic, indicating that Beijing, Chongqing, Shanghai, Guangzhou, Xi’an, and others that are rich in tourism resources are the main high-value cities, and “core-edge” characteristics gradually formed around these municipalities and capital cities. Lastly, of the four regions, the northeast regions had the largest intraregional and inter-regional differences. From the perspective of the contribution to regional difference sources Gnb > Gt > Gw, inter-regional disparities are the main reasons for the overall differences. Accordingly, policy suggestions are proposed to further promote the sustainable development of tourism destinations.