Calculate index of dissimilarity in NetLogo - netlogo

Question

I'm want to calculate the index of dissimilarity in NetLogo. I have a world divided into different regions and want to examine how evenly species are distributed around the world.
Consider this example: A world is divided into 16 different regions. The world is populated with two types of ants, red and blue. It looks like this:
The world in the picture is produced with the following code:
globals[indexdissimilarity] ; where I want the index of dissimilarity to be stored.
to setup
ca
;Setting world.
resize-world 0 19 0 19
set-patch-size 15
;Creating regions.
let x 5
let y 5
let col 45
while [y <= max-pycor + 1 ][
while [x <= max-pxcor + 1][
ask patches with [pxcor < x and pxcor >= x - 5 and pycor < y and pycor >= y - 5][
set pcolor col
]
set x x + 5
set col col + 3
]
set x 5
set y y + 5
]
ask n-of (count patches * 0.85) patches[sprout 1[
set shape "bug"
set color red]]
ask n-of (count turtles * 0.50) turtles [set color blue]
dissimilarity
end
; Here is where I want to calculate the index of dissimilarity.
to dissimilarity
let tot_red (count turtles with [color = red])
let tot_blue (count turtles with [color = blue])
; set indexdissimilarity
end
My main issue is how to iterate parts of the calculations over each neighborhood.
Thanks!

Answer 1

I think I managed to solve it. Please, let me know if it looks correct. Here is the full updated code.
globals[indexdissimilarity
dis
]
patches-own [reg]
to setup
ca
;Setting world.
resize-world 0 19 0 19
set-patch-size 15
;Creating regions.
let x 5
let y 5
let col 45
while [y <= max-pycor + 1 ][
while [x <= max-pxcor + 1][
ask patches with [pxcor < x and pxcor >= x - 5 and pycor < y and pycor >= y - 5][
set pcolor col
]
set x x + 5
set col col + 3
]
set x 5
set y y + 5
]
ask patches [set reg [pcolor] of self]
ask n-of (count patches * 0.85) patches[sprout 1[
set shape "bug"
set color red]]
ask n-of (count turtles * 0.7) turtles [set color blue]
update
end
to update
;Dissimilarity index.
let tot_red (count turtles with [color = red])
let tot_blue (count turtles with [color = blue])
let neighb1 [reg] of turtles
let neighb remove-duplicates neighb1
set dis []
foreach neighb [i -> set dis lput abs((count turtles with [reg = i and color = red] / tot_red) - (count turtles with [reg = i and color = blue] / tot_blue)) dis]
set indexdissimilarity sum(dis) / 2
print(indexdissimilarity)
end