ABSTRACT
Tourism is linked to multiple dimensions, such as the economy, society, and environment, and the relationships among its influencing factors are complex, diverse, and overlapping. This study constructed an evaluation index system to measure the degree of coordinated development of tourism, transportation, and the regional economy, then built a tourism-transportation-based Spatial Durbin Model (SDM) regarding the process of the coordinated development of tourism in the Beijing-Tianjin-Hebei region (BTHR) from 2010 to 2020. This paper explains the current status of sustainable tourism development in the BTHR and the impact and spillover effects of transportation on tourism development. The results show that the normalized tourism coordinated development index (NTCDI) of the BTHR increased from 13.61 in 2010 to 18.75 in 2019, then decreased to 14.45 in 2020. The results of SDM show that different transportation modes have different spillover effects on tourism. Specifically, civil aviation transportation has a positive impact and significant spillover on a city's tourism revenue (TR), while high-speed railway transportation has a negative spillover effect. The model results also show that the degree of openness of the city and city economic development level have significant positive effects and spillover effects on tourism development. Finally, the implications of related variables are discussed, and some suggestions are put forward on tourism development in the BTHR. However, there are some limitations in this study. In the future, international cooperation and data sharing will be strengthened, and multivariate methods such as social network analysis, artificial intelligence, and machine learning will be further integrated to achieve accurate simulation and prediction of the spatial spillover effects of tourism transportation.
ABSTRACT
Objective: To analyze the symptom characteristics of Coronavirus Disease 2019(COVID-19) and to improve its prevention by using big data. Methods: Using Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources, we obtained the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020, in Hubei province and other top 10 impacted provinces in China and the epidemic data. Data of 2020 were compared with the previous three years. Data of Hubei province were compared with confirmed cases. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlation between the SV of COVID-19 and new confirmed or suspected cases were analyzed and the hysteresis effects were discussed. R3.6.2 software was used to analyze the data. Results: Compared the data from January 1 to February 20, 2020, with the SV for the same period of previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P<0.001). The SV of COVID-19 in Hubei province was significantly correlated with new confirmed or suspected cases (r(confirmed)=0.723, r(suspected)=0.863, all P<0.001). The results of the distributed lag model suggested that the patients who retrieved relevant symptoms on the internet may begin to see a doctor in 2-3 days later and be diagnosed in 3-4 days later. Conclusions: The total SV of lower respiratory symptoms is higher than that of upper respiratory symptoms, and the SV of diarrhea also increases significantly. It warns us to pay attention to not only the symptoms of lower respiratory tract, but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. There is a relationship between internet retrieval behavior and the number of new confirmed or suspected cases. Big data have a certain role in the early warning of infectious diseases.