Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.

COVID-19 is the latest of many pandemic affecting the world in the past few decades, and it has had a significant impact on the global environment. Some research has analysed the effects of the pandemic on air quality; however, very few studies have employed relationship analytics. In order to analyse the potential relationship between pandemic-related information and air quality data from a more holistic and detailed point of view, we propose a methodology based on pure data analysis. Three types of data were collected, namely air quality index, pandemic-related events, and number of COVID cases. Data were collected from five cities—Wuhan, New York, Seoul, Melbourne, and Singapore—to further analyse the response of air quality index to COVID events, thus revealing how human activity influences air quality from a pandemic perspective. The results show that a potential connection does exist in most cases and provide more evidence showing that air pollution declined during the pandemic. However, the strength of this relationship may also be related to other factors, such as geography, politics, population density, and measures imposed by local authorities, etc. This study provides another perspective to assist stakeholders in improving environmental decision making.

Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China’s ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China’s 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China’s carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China’s carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030.

COVID-19 is the latest among the many pandemics in the last few decades in the world, and it has stricken the global economy severely. It has consequently affected the stock market, which affects the economy of the country to a great extent. Some research has managed to analyse the impacts on the stock market during the outbreak; however, few of them have been related to relationship analytics. Here, we proposed a methodology based on pure data analytics, which gathers two types of data, event and stock index in China, to explore their relationship through analysing the stock index’s shift in reacting to pertinent social events, hence revealing insights into how events affect fluctuations in stock indices. The results showed that the relationship does exist in most cases. However, the closeness of the relationship may also be associated with the location of the stock company and other factors such as its geographical, political, and local authority considerations. This study may potentially assist stakeholders in adjusting their investments in the stock market.