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As open source solution you may use GRASS GIS 7 which offers the module r.viewshed that computes the viewshed of a point on an elevation raster map. You can define observer_elevation=value - Viewing elevation above the ground target_elevation=value - Offset for target elevation above the ground In addition, there is r.horizon which lets you generate a ...


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http://www.tigergeocoder.com/ using TIGER 2013 data, ready to run your own server instance in Amazon EC2 cloud and geocode 1,000,000+ per day.


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If you export the lines as DXF, you should be able to load them into QGIS. Once they are in QGIS, you need to create an attribute that contains the elevation of each line (ie. a data column with a number). Depending on the total number of lines you could possibly do this manually. If doing it manually is not an option, you could concievably do some Python ...


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HDFview is geared for use with satellite data or climate model output that often comes in hierarchical data formats or netcdfs, but it's one of those things like a good text editor (ex. notepad++ or vim), where once you come across certain file types you need this tool to get a first look at them and understand how things are structured. It's not really ...


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Here is how you could get the slope, using R and the raster package. To (also) get the intercept see help(calc) library(raster) # your file # b <- brick("file.nc") # example data: b <- brick(system.file("external/rlogo.grd", package="raster")) # here time is 1 to n, but you can set it something else time <- 1:nlayers(b) # write a function that ...


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GDAL has support for netCDF files: gdalinfo --formats|grep -i cdf GMT (rw): GMT NetCDF Grid Format netCDF (rw+): Network Common Data Format So, you can open this kind of files directly in QGIS. For this page: http://www.unidata.ucar.edu/software/netcdf/examples/files.html I downloaded this ECMWF_ERA-40_subset.nc climate sample file. It has 17 multiband ...


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If you're familiar with Python you can use the netCDF4-python library that can read and write both netCDF 3 and 4 data to numpy arrays. For example: from netCDF4 import Dataset root_group = Dataset("path_to_dataset", format='NETCDF4') print root_group $ netCDF4 style dump data = root_group.variables["some_variable"][:] Python has a large number of ...



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