Overcrowding and its effects on inmate violent behavior in priSon

Prisons in the United States are overcrowded. The problem of overcrowding has worsened as the years have gone on, especially since the war on drugs and determinate sentencing, have become a part of the American Justice System. This overcrowding has led to concerns about what effects it might have on those that are incarcerated. This study looks at the effects this overcrowding has on inmate violent behavior. Prison administrators are charged with providing a safe and secure atmosphere for inmates to serve the time that they are sentenced to by the states and federal governments. If this overcrowding is creating violence within the walls of the prisons, then the states and the federal government are not fulfilling their obligation and are subjecting inmates to cruel and unusual punishment, a violation of the 8th Amendment; To examine the relationship between crowding and prison disorder, this study used data from the US Department of Justice\u27s 1995 Census of Adult Correctional Facilities. Information on the numbers of inmates in custody compared with their design and rated capacities, and the number of violent occurrences within the facilities were used

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting