Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective

Abstract

Unlike previous regionalized studies on a worldwide crisis, this study aims to analyze spatial distribution patterns and evolution characteristics of the COVID-19 pandemic, using space-time aggregation and spatial statistics from a global perspective. Hence, various spatial statistical methods, such as the heat map, global Moran’s I, geographic mean center, and emerging hot spot analysis were utilized comprehensively to mine and analyze spatiotemporal evolution patterns. The main findings were as follows: Overall, the spatial autocorrelation of confirmed cases gradually increased from the initial outbreak until September 2020 and then decreased slightly. The geographic centroid migration ranges of the pandemic in Asia, Europe, and Africa are wider than those in South America, Oceania, and North America. The spatiotemporal evolution pattern of the global pandemic mainly consisted of oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. This study provides auxiliary decision-making information for pandemic prevention and control.