Hi Ian Murray and Inception Woznicki,
I would probably still use dictionaries. They provide a flexible way of processing this kind of problems. Take a look at the code below (please note that I did not test the code, so I am sure there will still be some errors in it).
import arcpy, os
#Set environment settings
arcpy.env.overwriteOutput = True
arcpy.env.workspace = "C:/Subhasis/Project-06-02-14/Landuse/New folder/Merged/Landuse_raster"
# you're not use this workspace in your code, but I suppose you eventually will.
# outws="C:/Subhasis/Project-06-02-14/Landuse/New folder/Merged/Landuse_raster/Table"
# configuration of lists of values
lst_agr = [2100, 2200, 2300, 2400] # agriculture
lst_bar = [7100, 7200, 7300, 7400, 7500, 7600] # Barren Land
lst_for = [4110, 4120, 4210, 4220, 4230, 4311, 4312, 4321, 4322, 4410] # Forest
lst_urb = [1110, 1120, 1130, 1140, 1150, 1200, 1211, 1214, 1300, 1400,
1410, 1411, 1420, 1440, 1462, 1463, 1499, 1500, 1600, 1700,
1710, 1741] # Urban
lst_wat = [1419, 5100, 5190] # Water
lst_wet = [1461, 1711, 1750, 1850] # Wetlands
# Create a dictionary with the classname vs the lists
dct_lsts = {"Agriculture": lst_agr, "Barren Land": lst_bar,
"Forest": lst_for, "Urban": lst_urb,
"Water": lst_wat, "Wetlands": lst_wet}
# optionally, divide the Urban area further into 4 categories.
# instead of creating 1 list, create 4 separate lists, like this:
##lst_urb1 = [1110, 1120, 1130, 1140, 1150, 1200]
##lst_urb2 = [1211, 1214, 1300, 1400, 1410, 1411]
##lst_urb3 = [1420, 1440, 1462, 1463, 1499, 1500]
##lst_urb4 = [1600, 1700, 1710, 1741]
##dct_lsts = {"Agriculture": lst_agr, "Barren Land": lst_bar,
## "Forest": lst_for, "Urban 1": lst_urb1,
## "Urban 2": lst_urb2, "Urban 3": lst_urb3,
## "Urban 4": lst_urb4, "Water": lst_wat,
## "Wetlands": lst_wet }
# loop through rasters
flds = ("VALUE", "COUNT")
lst_ras = arcpy.ListRasters("*", "GRID")
for ras in lst_ras:
# create dct with value vs count for this raster:
dct = {row[0]:row[1] for row in arcpy.da.SearchCursor(ras, flds)}
# now create an initial dictionay that will hold the resulting counts per LU class
dct_res = {lu: 0 for lu in dct_lsts.keys()}
# now fill the resulting dictionary
for val, cnt in dct.items():
# determine the lu class and add count
for lu, lst in dct_lsts.items():
if val in lst:
dct_res[lu] += cnt
break
# now print the result:
print "\nLanduse statistics for raster: '{0}'".format(ras)
for lu, cnt in dct_res:
print " - {0}: {1}".format(lu, cnt)
This is what happens (if the code works):
- On lines 11 - 18 lists of the values corresponding to a landuse class are created
- On Lines 21 - 23 a dictionary is created that uses a landuse description as key and the list of values is the value assigned to the key
- On Line 28 - 37 (commented) an example is shown how you could incorporate subdividing urban into 4 classes (the values assigned to each class is arbitrary)
- I placed the line 41 outside the loop, since assigning it one time is enough
- On line 42 - 43, a list of GRID rasters is created and the loop is started
- Line 45 creates the dictionary with pixelvalues vs count for the current raster
- Line 48 creates an initial dictionary with the landuse name as key and the count set to 0
- Line 51 starts a loop through the pixel values vs count of the current raster
- Line 53, with that loop another loop is started to go through the dictionary with the lists.
- On line 54 a check is done to see of the current pixelvalue is inside the current list of pixel values for the current land use. If the is the case, the count is added to the count in the dictionary that holds the landuse class description vs the count This is followed by a break to step out of the inner loop (the one that goes through each landuse class and list of related pixel values)
- Lines 59 - 61 should create a list per raster with each landuse class and the calculated count.
Writing this down, I think you could also create a reference table with the pixelvalues on each row and the corresponding landuse classname in another column and join this table to the attribute table of the raster and perform a summerize on the landuse class column, summing the count...
Kind regards,
Xander
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