I have large (~3 GB) CSV Data which holds information on timestep, lon, lat, and emissions (Z value). I would like to convert this data to raster data using Python by summing all emission values of points within a certain cell.
The pandas dataframe looks like this:
date lon lat IMO emission
2015-01-01 00:05:00 5.13700 61.98248 6926622.0 0.003370
2015-01-01 00:10:00 5.13700 61.98249 6926622.0 0.003497
2015-01-01 00:15:00 5.13699 61.98249 6926622.0 0.001663
..
The raster is a 12kmx12km with 196 rows/cols defined LCC:
-1275000.000 -1000000.000 12000.000 12000.000 196 196 1
How can I do this? I checked make_geoube geocube package, however it should be as fast as possible, because data is large and there are more files to be processed. Also i am not quite sure if it excatly does what I want.
But if so, how would I get the LCC raster to be used by geocube?
In R the rasterize function seems to do what I want.
Thanks!