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If I understand your desired outcome correctly, you would like a count (richness) of species for each grid cell in the defined raster. I cannot speak to the differences between R and QGIS but I came up with a much more optimized and faster way to conduct your analysis. I leverage the raster package and use a raster stack to accumulate species. The workflow ...


3

Take a look at the raster function in the raster package. It will let you create a raster with a specified extent, number of rows/columns and resolution. Here I will use characteristics of your data summary to create a 100x100 raster within the specified extent. I am passing an extent object to define the x and y limits. You can also use the specific ...


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1) The easiest solution is to use the processing module in the QGIS Python console: import processing processing.runalg("qgis:joinattributesbylocation","BKMapPLUTO.shp","DCP_nyc_freshzoning.shp","['intersects']",0,"sum,mean,min,max,median",0,'result.shp') 2) Without a GIS, you can use Fiona (read and write shapefiles as Python dictionaries) and Shapely ...



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