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The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.

An outbreak of the COVID-19 pandemic caused by the SARS CoV 2 has profoundly affected the world. This study aimed to identify the spatio-temporal clustering of COVID-19 patterns using spatial statistics. Local Moran’s I spatial statistic and Moran scatterplot were first used to identify high-high and low-low clusters and low-high and high-low outliers of COVID-19 cases. Getis-Ord’s〖 G〗_i^* statistic was then applied to detect hotspots and coldspots. We finally illustrated the used method by using a dataset of 10,742 locally transmitted cases in four COVID-19 waves in 63 prefecture-level cities/provinces in Vietnam. The results showed that significant low-high spatial outliers of COVID-19 cases were first detected in the north-eastern region in the first wave and in the central region in the second wave. Whereas, spatial clustering of high-high, low-high and high-low was mainly found in the north-eastern region in the last two waves. It can be concluded that spatial statistics are of great help in understanding the spatial clustering of COVID-19 patterns.

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

Flash floods have been blamed for significant losses and destruction all around the world are widely, including Vietnam, a developing nation that has been particularly hard hit by climate change. Therefore, flash flood hazards are essential for reducing flood risks. The topographic wetness index (TWI), altitude, slope, aspect, rainfall, land cover, normalized difference vegetation index (NDVI), distances to rivers and roads, and flow length were used in this study to create a spatial database of ten exploratory factors influencing the occurrence of flash floods in the Ngan Sau and Ngan Pho river basins (North-Central Vietnam). Subsequently, the analytic hierarchy process (AHP) was applied to calculate the weights of these influencing factors. The flood threat was then mapped using GIS techniques. The validation of the flash flood hazards involved 151 flood inventory sites in total. The findings demonstrate that (i) distance from rivers (0.14) and TWI (0.14) factors have the greatest influence on flash flooding, whereas distance from roads (0.06) and NDVI (0.06) factors were found to have the least influence; (ii) a good conformity of 84.8 percent between flood inventory sites and moderate to very high levels of flash flood hazard areas was also discovered; (iii) high and very high flood hazard levels covering areas of 275 and 621.1 km2 were mainly detected along and close to the main rivers and streams, respectively. These results demonstrated the effectiveness of GIS techniques, AHP, and Landsat-8 remote sensing data for flash flood hazard mapping.