Analysis of temporal and spatial variations of forest. A case of study in notheastern Armenia

The forest has a crucial ecological role and the continuous forest loss can cause colossal effects on the environment. As Armenia is one of the low forest covered countries in the world, this problem is more critical. Continuous forest disturbances mainly caused by illegal logging started from the early 1990s had a huge damage on the forest ecosystem by decreasing the forest productivity and making more areas vulnerable to erosion. Another aspect of the Armenian forest is the lack of continuous monitoring and absence of accurate estimation of the level of cuts in some years. In order to have insight about the forest and the disturbances in the long period of time we used Landsat TM/ETM + images. Google Earth Engine JavaScript API was used, which is an online tool enabling the access and analysis of a great amount of satellite imagery. To overcome the data availability problem caused by the gap in the Landsat series in 1988- 1998, extensive cloud cover in the study area and the missing scan lines, we used pixel based compositing for the temporal window of leaf on vegetation (June-late September). Subsequently, pixel based linear regression analyses were performed. Vegetation indices derived from the 10 biannual composites for the years 1984-2014 were used for trend analysis. In order to derive the disturbances only in forests, forest cover layer was aggregated and the original composites were masked. It has been found, that around 23% of forests were disturbed during the study period.

Practice variations in the production of health

This study investigates three questions related to medical practice variation. First, it tests whether average length of stay across Portuguese National Health Service hospitals varies when controlling for differences in patients’ characteristics. Second, it looks at hospital-level characteristics in order to find out whether these are able to explain differences in average length of stay across hospitals. Finally, it proposes a best practice average length of stay for each of the six episodes of care analyzed. To perform the analysis, administrative data from the Diagnosis-Related groups’ data set for the year of 2012 was used. A replication of a hierarchical two-stage model with hospital fixed effects was carried out. The results show that after taking patients’ characteristics into account, variation in average length of stay across hospitals exists. This variation cannot be explained by hospital-level characteristics.