A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

• Published in: Transactions in GIS Vol. 25; no. 5; pp. 2191 - 2239
• Main Authors: Franch‐Pardo, Ivan, Desjardins, Michael R., Barea‐Navarro, Isabel, Cerdà, Artemi
• Format: Journal Article Web Resource
• Language: English
• Published: England John Wiley & Sons, Inc 01.10.2021; Blackwell Publishing Ltd; John Wiley and Sons Inc
• Subjects: Bayesian analysis; COVID-19; Dynamics; Evolution; Geographical information systems; Human influences; Mathematical models; Pandemics; Pollution; Probability theory; Regression analysis; Remote sensing; Review; Reviews; Risk analysis; Risk factors; Scientific papers; Social factors; Socioeconomic factors; Spatial analysis; Statistical analysis
• ISSN: 1361-1682; 1467-9671
• DOI: 10.1111/tgis.12792

COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.