Résumé
The effects of COVID-19 measures on the quantitative changes in atmospheric substances associated with economic activities have been explored in various studies. However, the specific relationship between the changes in economic activities due to government interventions and the associated changes in atmospheric substances remains unclear. This relationship needs to be understood in detail, especially in the tourism industry where conflicting responses are needed, such as promoting people flow and socioeconomic development while adopting measures to prevent the spread of infectious diseases. Therefore, this study focused on the tourism industry in Japan, used Google Earth Engine to calculate the changes in five atmospheric substances before and during the government's Go To Travel Campaign, which was implemented to support domestic travel, and analyzed the correlations with statistical data such as the number of travelers, use of buses, and taxis. As a result, a series of connected behaviors clarified the fact that socioeconomic activities affected by the government's Go To Travel Campaign surfaced with the changes in the atmospheric substances. To further understand the relationship between interventions to promote tourism and air quality, additional datasets and approaches are needed, including data on the operation status of facilities and the behavioral characteristics of Japanese travelers.
Résumé
Various interventions were made by the Thai government to prevent the COVID-19 spread by controlling socioeconomic activities, but the effectiveness of these interventions and other factors have not yet been fully clarified. Thus, this study aims to provide further scientific evidence on those potential factors which affect the socioeconomic activities changes during the pandemic, by using spatial analysis on atmospheric composition. By taking Phra Nakhon Si Ayutthaya Province, Thailand as the case area. Results of the government's COVID-19 measures and statuses of industries were compared with changes in aerosols, including PM 2.5 which was analyzed by Google Earth Engine with nine open datasets including meteorological and hydrological factors. The analysis revealed that the aerosol index in ueban area of the province decreased at 28.03% in 2020 compared in 2019. Besides, PM 2.5 drastically decreased from March 2020, even without the influence of wind speed which as the highest causal relationship, and kept low level compared with previous years. The reason of the tendency would be explained that other than government interventions including national-level state of the emergency decree, reduction of factories' activities at Rojana Industrial Park and reduction of the number of tourists had significant influence to reduce the mean value of PM 2.5.
Résumé
In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world's most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers' opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.
Résumé
Millions of people in the countries around the world are suffering due to one of the most dangerous disasters the Coronavirus disease 2019 or COVID-19. This research has the main objective to see the effectiveness of the HEALTH-EDRM framework from the World Health Organization (WHO) for dealing with the COVID-19 situation. The data set from the two popular online sources which are ourworldindata.org and worldmeter.info are collected and used for performing the analysis. The t-Test analysis method is used as the main tool for performing the analysis based on the 3 hypotheses related to the COVID-19 situation in Asia-Oceania countries. The result shows a significantly higher effectiveness in the member states which applied the HEALTH-EDRM for dealing with the COVID-19 affected cases, but not with the COVID-19 death cases. © 2021 IEEE.