Globally, the COVID-19 pandemic is a top-level public health concern. This paper is an attempt to identify and COVID-19 pandemic in Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases, deaths, recoveries, number of hospitals, hospital beds and population from March 2, 2019 to the end of November 2021 in 31 provinces of Iran from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS10.3 were utilized to analyze and evaluate COVID-19, including Geographic Weight Regression (GWR), Getis-OrdGi* (G-i-star) statistics (hot and cold spot), and Moran autocorrelation spatial analysis. Moran statistics, based on the GWR model, demonstrated that deaths and recoveries followed a clustering pattern for the confirmed cases index during the study period. The Moran Z-score for all three indicators confirmed cases, deaths, and recoveries, which was greater than 2.5 (95% confidence level). The Getis-OrdGi* (G-I-Star) (hot and cold spot) data revealed a wide range of levels for six variables (confirmed cases, deaths, recoveries, population, hospital beds, and hospital) across Iran's provinces. The overall number of deaths exceeded the population and the number of hospitals in the central and southern regions, including the provinces of Qom, Alborz, Tehran, Markazi, Isfahan, Razavi Khorasan, East Azerbaijan, Fars, and Yazd, which had the largest number and The Z-score for the deaths Index is greater than 14.314. The results of this research can pave the way for future studies.

As one of the most catastrophic atmospheric events, drought affects various aspects of the environment. Groundwater resources are one the sectors that are influenced by long-term droughts and received insufficient attention compared to other aspects of the environment. Rainfall data collected by 23 metrological stations for 20 years (2005, 2010, 2015,2020) was used to investigate the drought event and its situation in Qazvin plain, the central plateau of Iran. Drought characteristics are evaluated using the 24-month standardized precipitation index (SPI). The results of SPI indicated that insufficient precipitation, excessive use of groundwater for irrigation, and utilization of uncontrolled wells caused a significant reduction in groundwater aquifers from 2015 to 2020. To assess the performance of the SPI, a five-year moving average of available precipitation data was calculated, and the result confirmed the outcomes of SPI. Appropriate geostatistical interpolation methods are used to generate maps of drought zoning. Based on the results of this investigation in the northeastern part of the study area, June and November had the highest and the lowest rate of drought, respectively. The linear regression between the annual average of precipitation and the changes of groundwater aquifer level exposed a significant correlation of R2 = 0.4253. Furthermore, linear regression between 24-month SPI and groundwater aquifer level indicated a correlation of R2 = 0.614. Considering the results of this study, the reduction of groundwater aquifer levels in Qazvin plain from 2015 to 2015 exposed a significant negative difference compared to previous years (2005 to 2010).