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

Abstract

Abstract 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.

Keywords

• COVID‐19
• spatial‐temporal patterns
• visualization
• mixed GWR
• XGBoost
• geographical perspective