Abstract. A GIS-based Normalized Differential Vegetation Index (NDVI) was analyzed using space-based data between 1972 and 2011 as Input data. The result of the NDVI using Landsat 7 ETM+ shows clearly that the values range from 0.19 to −0.31. Mountains and highlands of the Itagunmodi-Igun area revealed stressed vegetation cover between 0.11 to −0.31. The NDVI was also performed on Landsat imageries of four different epoch: 1972, 1986, 2000, and 2010. Results showed that vegetation index ranged from −0.105 to 0.033, −0.25 to 0.480, −0.313 to 0.19 and −0.29 to 0.5 in 1972, 1986, 2000 and 2010 respectively. The analysis revealed that the study area experienced an increase in biomass between 1972 and 1986 but with some areas experiencing outright disappearance of vegetation as indicated by the lower bands of index values in 1972 (−0.105) and 1986 (−0.25). The result showed that the Basin experienced a rapid and significant increase in biomass between 2000 and 2010, as indicated by the lower bands of index values in 2000 (−0.291) and 2010 (0.5). The results of the NDVI in 1972 ranged from −0.105 to 0.033 while NDVI in 1986 ranged from −0.25 to 0.480 which indicated a significant increase in the vegetation index. The results of the NDVI in 2000 ranged from 0.291 to 0.5 also indicated a significant increase in the vegetation index. The study concluded that artisanal mining could cause land and vegetation degradation with consequent loss of biodiversity, ecological modification.
Abstract. This study examines the spatial distribution of COVID-19 incidence and mortality rates across the counties in the conterminous US in the first 604 days of the pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables and health outcomes variables (N = 3106). OLS estimates accounted for 31% of the regression plain (adjusted R2 = 0.31) with AIC value of 9263, and Breusch-Pagan test for heteroskedasticity indicated 472.4, and multicollinearity condition number of 74.25. This result necessitated spatial autoregressive models, which were performed on GeoDa 1.18 software. ArcGIS 10.7 was used to map the residuals and selected significant variables. Generally, the Spatial Lag Model (SLM) and Spatial Error Model (SEM) models accounted for substantial percentages of the regression plain. While the efficiency of the models is the order of SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. R2 = 0.56) > SEM (AIC: 8282.0; Breucsh-Pagan test: 697.2; Adj. R2 = 0.56). In this case, the least predictive model is SEM. The significant contribution of male, black race, poverty and urban and rural dummies to the regression plain indicated that COVID-19 transmission is more of a function of socio-economic, and rural/urban conditions rather than health outcomes. Although, diabetes and obesity showed a positive relationship with COVID-19 incidence. However, the relationship was relatively low based on the dataset. This study further concludes that the policymakers and health practitioners should consider spatial peculiarities, rural-urban migration and access to resources in reducing the transmission of COVID-19 disease.
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