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Variable raster scan patterns of 1024 × 64, 512 × 128, and 256 × 256 pixels (horizontal × vertical) can be selected.
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To account for the possible influence of climatic variables, raster data in a resolution of 1 × 1 km were provided by the German Weather Service (Deutscher Wetterdienst--DWD).
Eqn (2) was calculated using the spatial analyst tools module based on spatialized variables in raster format data layers.
The dissection pattern is encapsulated by a continuous map of drainage density, a raster variable best suited for showing spatial variability of dissection.
All input files were manipulated in ArcGIS 10 (ESRI, Redlands, CA) to produce variable layers in raster format at 10 m resolution, except for the digital elevation model where the finest available resolution was 30 m. From the latter data, relative elevation was defined as the mean centered city-specific elevation.
For the environmental variables available in raster format, this involved extracting the mean value of the pixels that fell within the polygon that defined the boundary of the postcode.
In a different part of the MGET toolbox there is a tool called Create Rasters for HYCOM GLBu0.08 4D Variable that can download GLBu0.08 rasters and store them in a directory.
Finally, we extracted values from the raster habitat variables to both used and available locations.
All environmental variables were resampled to raster data with a spatial resolution of 0.00833° (nearly 1 km).
The kriging was done per variable per year with a raster resolution of 1 km using the kriging function with a spherical semi-variogram (ArcGIS 10) and then averaged by raster cell over the period covered.
Using kernel density methods for point data and inverse-distance weighing methods for polygon data, each variable was transformed into a raster data surface layer that spanned the entire surface area of Philadelphia and was expressed as a continuous variable.
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