Du durchsuchst gerade das Archiv des Tags ‘GIS Mapping’.

Archiv: GIS Mapping

Project Backgrround:
The analysis of the City of Roanoke’s urban tree canopy (UTC) was carried out by the Virginia Department of Forestry in collaboration with the City of Roanoke and the Roanoke Valley— Alleghany Regional Commission. Assistance was provided by the Virginia Geospatial Extension Program (VGEP) at Virginia Tech’s Department of Forestry and by the Spatial Analysis Laboratory (SAL) of the University of Vermont. The goal of the project was to apply the USDA Forest Service’s UTC assessment protocols to the City of Roanoke. This analysis was conducted based on year 2008 data.

Abstract
Background: Various geographic information systems (GIS) are now widely used to map the distribution of diseases and mortality. However, the mapping of raw mortality rates has been found to be inappropriate since it does not account for the spatial heterogeneity of the population at risk. Bayesian techniques have therefore been suggested as a solution to the problem. Methods: Annual mortality rates for each of the 39 villages of the study area in the Kossi Province in northwest Burkina Faso were calculated using midyear populations of children under five. Two mapping techniques were then used. Firstly, the GIS software ArcView was used to map the crude mortality rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. The geostatistical prediction method of Kriging was then used to spatially interpolate the data for successive years. Results: No spatial pattern is identifiable from the circles representing mortality rates drawn on the map using ArcView. The circles are scattered over the study area and comparing annual distributions between them is difficult. The maps produced by the Bayesian technique also do not show a clear spatial trend pattern. However, they indicate the tendency of villages in the northeastern region to produce higher incidence or risk values, confirming the results of an earlier study reporting a significant cluster of high childhood mortality in the same area.


GIS läuft unter Wordpress 2.5.1
28 Verweise - 0.231 Sekunden.