Analyzing COVID-19 Using Multisource Data: An Integrated Approach of Visualization, Spatial Regression, and Machine Learning.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, was first identified in Wuhan, China, in December 2019. As the number of COVID-19 infections and deaths worldwide continues to increase rapidly, the prevention and control of COVID-19 remains urgent. This article aims to analyze COVID-19 from a geographical perspective, and this information can provide useful insights for rapid visualization of spatial-temporal epidemic information and identification of the factors important to the spread of COVID-19. A new type of vitalization method, called the point grid map, is integrated with calendar-based visualization to show the spatial-temporal variations in COVID-19. The combination of mixed geographically weighted regression (mixed GWR) and extreme gradient boosting (XGBoost) is used to identify the potential factors and the corresponding importance. The visualization results clearly reflect the spatial-temporal patterns of COVID-19. The quantified results reveal that the impact of population outflow from Wuhan is the most important factor and indicate statistically significant spatial heterogeneity. Our results provide insights into how multisource big geodata can be employed within the framework of integrating visualization and analytical methods to characterize COVID-19 trends. In addition, this work can help understand the influential factors for controlling and preventing epidemics, which is important for policy design and effective decision-making for controlling COVID-19. The results reveal that one of the most effective ways to control COVID-19 include controlling the source of infection, cutting off the transmission route, and protecting vulnerable groups.

Association of built environment attributes with the spread of COVID-19 at its initial stage in China.

Evidence of the association of built environment (BE) attributes with the spread of COVID-19 remains limited. As an additional effort, this study regresses a ratio of accumulative confirmed infection cases at the city level in China on both inter-city and intra-city BE attributes. A mixed geographically weighted regression model was estimated to accommodate both local and global effects of BE attributes. It is found that spatial clusters are mostly related to low infections in 28.63 % of the cities. The density of point of interests around railway stations, travel time by public transport to activity centers, and the number of flights from Hubei Province are associated with the spread. On average, the most influential BE attribute is the number of trains from Hubei Province. Higher infection ratios are associated with higher values of between-ness centrality in 70.98 % of the cities. In 79.22 % of the cities, the percentage of the aging population shows a negative association. A positive association of the population density in built-up areas is found in 68.75 % of county-level cities. It is concluded that the countermeasures in China could have well reflected spatial heterogeneities, and the BE could be further improved to mitigate the impacts of future pandemics.