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To predict the overall land surface, the GIS is used with the interpolation concept.
We used generalized additive models (GAMs) to predict annual outdoor levels of PM10 and NO2 using smooth spatial surfaces and GIS-derived covariates.
Djokic et al. (1994) have developed a tight coupling procedure, namely ARC/HEC-2, which exports the terrain data from Arc/info into HEC-2 and converts HEC-2 water surface elevations into GIS coverage in Arc/info.
Environmental data for plot locations were extracted from biophysical GIS surfaces that included primary and secondary terrain attributes (Palmer 2008), biophysical surfaces (Leathwick et al. 2003) and monthly and annual climate data (Mitchell 1991; Leathwick et al. 2002).
In addition to the raw survey data, we release model outputs in the form of map images (in PDF and PNG formats), GIS surfaces (in binary float and GeoTiff formats), and population estimates.
We performed further analyses within the ECRHS cohort to allow direct comparison with a previous ECRHS publication (Jacquemin et al. 2009b) that estimated NO2 using the APMoSPHERE (Air Pollution Modelling for Support to Policy on Health and Environmental Risk in Europe) model, a 1 × 1 km surface model developed using GIS-based techniques (Vienneau et al. 2009).
This paper presents the design architecture and functioning of CoalMan, a tailor made Geographic Information System (GIS) for managing surface and underground fires in coal mining areas.
While the classical GIS presents the surface of a planet that is a 2D dataset, CATMAID was designed to show 3D microscopy data, in particular from serially sectioned volumes.
Hence we created a GIS toolbox, the LAGOS-GIS toolbox, containing multiple tools to calculate a series of metrics from these layers, in order to define, classify, and characterize the population of surface water environments found in the study extent, based on their hydrologic and landscape context.
