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How to speed up raster to Polygon speed?
First lets create a discrete raster with values [5:8] and two polygons. Now we can extract the raster values for each polygon. The lapply function is used with table to get the cell counts of each raster class. We can now merge the raster cell counts with the polygon data.
How to check if GDAL Python bindings are installed?
To Make sure Gdal is installed open a command prompt and type: ‘ogrinfo’ is not recognized as an internal or external command, operable program or batch file. To check gdal python bindings is installed open command prompt and type:
How to install GDAL command line tools on Windows?
Install Gdal command line tools and check to see if its binaries are added to path environment variable. e.g. in windows: open Run and type: Download and install gdal python bindings from here according to your python and OS. You may encounter issues while installing gdal. Please see Installing GDAL with Python on windows?
How does parallel processing improve the speed of raster?
Parallel processing cut the user time by half, but negated the benefit by doubling the system time. Raster uses this for the extract function, but unfortunately not for the crop or mask function. Unfortunately, this leaves a slighly larger amount of total elapsed time due to “waiting around” by the “IO.”
How to increase the speed of crop, mask and extract raster?
Instead I: Stack the 300mx300m land cover map and the 300mx300m grid from step 2, using the raster package: stack_file <- stack (lc, grid). Use dplyr to calculate the share of land cover class cells in the 1kmx1km cells.
Is there a slow method to extract raster?
Slow Method: The alternative method of doing an extract directly from the raster function takes a lot lot longer, and I’m not sure about the data management to get it into the form I want: I have finally gotten around to improving this function.