USING REMOTE SENSING AND BIG DATA TO ANALYSIS DIRECT SOLAR RADIATION IMPACTS ON THE SPREAD OF COVID-19 (CASE STUDIES: SIX STATES OF THE UNITED STATES)
Keywords: Solar Radiation, Remote Sensing, Big Data, Covid-19, GIS, the USA
Abstract. The COVID-19 virus and its outbreak were among the most considered research areas in different science branches throughout last year. Meanwhile, some extensive studies have been conducted on the factors influential in increasing the infection rate. The present study examines the relationship between two sets of factors. Firstly, the rate of infection with the virus and the ways it spreads are considered. Secondly, one of the most important climatic factors (direct solar radiation and radiation duration) is studied. This study aims to examine the relationship between these factors and the infection rate with COVID-19 in the six selected U.S. states, including Arizona, California, Colorado, Nevada, New Mexico, and Utah, which receive the maximum radiation and whose population density is at a high level. In order to conduct this study, the analysis of big data and referring to the United States Geological Survey (USGS) have been implemented. This quantitative study is based on analyzing satellite data in the GIS and R software applications. Generally, the research comprises three phases. Firstly, it produces data. Secondly, it examines the satellite images. Finally, it explores and analyses data via sampling techniques and regression tests. The results of this study have revealed that direct solar radiation and density are significantly related to the spread of COVID-19. In the case of radiation duration, investigations show that this variable does not influence the spread of the COVID-19 virus.