Gwanak_v6.nlogo

;1.Set Globals and Agent Properties;

extensions [table csv]
globals [districtPop districtadminCode %riskpop date where number-dead ts_kal q_kal]

;Generate agent's attributes of 22 sub-districtNames in Gwanak
breed[people person]

;Assign attributes
patches-own [gwanak dong land road locationName hospital ts__kal]
people-own  [health age edu districtName district-code
             homeName homePatch destinationName destinationPatch]

;2.Click Setup;
to setup
  clear-all
  reset-ticks
  setup-gis
  set-dictionaries
  set-people
  set-destination
  set-at-hospital
end

to go
  calc-pm10
  landprice-change
  move-people
  ask people [
  inhalation
  ]
  go-hospital
  ask people [
    adaptive-cap
    sensitivity
    road-effect
    ]

  gu-plot
  dong-plot
  age-plot
  edu-plot
  pm10-plot
  update-plots
  tick
  if (ticks = 8764) [stop]
  set date item 0 table:get ts_kal (ticks + 1)
  set where item 2 table:get ts_kal (ticks + 1)
  set %riskpop    (count people with [color = red and destinationName != "others"] / count people with [destinationName != "others"]) * 100
  set number-dead count people with [health < 0]
end

to setup-gis
;--gwanak Administrative Boundary(Displayed)
  file-open "data/Gwanak_admin.txt" ;
  let patch-admin file-read
  file-close
  (foreach sort patches patch-admin [ [the-patch the-admin] ->
    ask the-patch [ set dong the-admin]
  ])
  let max-a max [dong] of patches
  let min-a min [dong] of patches with [dong > -9999]
  ask patches with [dong > -9999]
    [ set pcolor scale-color blue dong min-a max-a]

 ;--gwanak Land Price
  file-open "data/Gwanak_land.txt"
  let patch-land file-read
  file-close
  (foreach sort patches patch-land [ [the-patch the-land] ->
    ask the-patch [ set land the-land]
  ])

 ;--gwanak (Main)Roads
  file-open "data/Gwanak_road.txt"
  let patch-road file-read
  file-close
  (foreach sort patches patch-road [ [the-patch the-road] ->
    ask the-patch [ set road the-road]
  ])
     ask patches with [road >= 60][set pcolor yellow + 2
    set road true
   ]
   ask patches with [road = -9999][set road false]

 ;-- Set territory
ask patches [ ifelse dong = -9999 [set gwanak false][set gwanak true]]

 ;-- Set Location Name
  let rawCode csv:from-file "data/census2010_age.csv"
  let adCode table:make
  foreach rawCode [ code ->
         if item 1 code = "gwanak"
        [table:put adCode item 0 code list (item 1 code)(item 2 code) ]
        ]
  foreach table:keys adCode [ adminCODE ->
       let loops 0
       foreach table:get adCode adminCODE [ x ->
           ask patches [if dong = adminCODE [set locationName x]]
            ]
       set loops loops + 1
       ]

; Import daily pollution
  let p0 csv:from-file "data/Gwanak_hourly-pollution-stats.csv"
  let p1 remove-item 0 p0
  let rep 0
  set ts_kal  table:make

  foreach p1 [ poll ->
  if item 2 poll = "ts_kal"
    [ let counter item 0 poll
      let the-rest remove-item 0 poll
      table:put ts_kal counter the-rest
    ]
  ]
set rep rep + 1

  ask patches with [gwanak = true] [
    set ts__kal  item (3 + random 13) table:get ts_kal 1
  ]


;;Scenarios
  let quarter csv:from-file "data/gwquart.csv"
  let q1 remove-item 0 quarter
  let looop 0
  set q_kal table:make

   foreach q1 [p ->
    if item 1 p = "ts_kal"
    [ let counter item 0 p
      let the-rest remove-item 0 p
      table:put q_kal counter the-rest]
  ]
  set looop looop + 1

end

;;;;;;;;;;;;;;;;;;;;;;;;;;
to set-dictionaries
  let csv-age csv:from-file "data/census2010_age.csv"
  set districtpop table:make
  set districtadminCode table:make

  foreach csv-age [ code ->
  if item 1 code = "gwanak"
    [let twogroups list (item 3 code) (item 4 code)
     let threeAgegroups lput item 5 code twogroups
     table:put districtpop item 2 code threeAgegroups
     table:put districtadminCode item 2 code item 0 code
    ]
  ]

end

;;;;;;;;;;;;;;;;;;;;;;;;;;
to set-people
  foreach table:keys districtpop [ dist ->
    let ageGroupID 0
    foreach table:get districtpop dist [ number ->
      create-people number [
        setupAgeGroup agegroupID
        set districtName dist
        set district-code table:get districtadminCode dist
        set shape "person"
        set heading random 360
        set homeName dist
        set homePatch one-of patches with [dong = [district-code] of myself ]
        move-to homePatch
        set destinationName "unidentified"
        set destinationPatch "unidentified"
        set health 300
        ;set age-counter 1 + random 730
      ]
      set ageGroupID AgeGroupID + 1
      ]
  ]
end

to setupAgeGroup [ID]
  if ID = 0 [set size 1 set age "young"  set edu 2 + random 2 set color orange]
  if ID = 1 [set size 1 set age "active" set edu 5 - random 2 set color turquoise]
  if ID = 2 [set size 1 set age "old"    set edu 2 + random 4 set color brown]
end

;;;;;;;;;;;;;;;;;;;;;
to set-destination   ;; Decomposing matrix
  let gncsv csv:from-file "data/gwanak_odmatrix.csv"
  let rawheader item 0 gncsv
  let destinationNames remove-item 0 rawheader
  let gnMat remove-item 0 gncsv

  let loopnum 1
  let gnMatrix table:make ;; This is a matrix where each origin has its name as a "key"
  foreach gnMat [ origin-chart ->
    let numberMat remove-item 0 origin-chart    ;; fraction has to be btw 0-1,
    let fraction map [ i -> i / 100 ] numberMat ;; but the original file is btw 1-100
    table:put gnMatrix item 0 origin-chart fraction
                 ]
  set loopnum loopnum + 1

  foreach table:keys gnMatrix [ originName ->
    let matrix-loop 0
    let Num count people with [homeName = originName and (age = "active")]
	;; Count people whose homename(agent attribute) equals to originname(patch attribute) and age 15-64
    let totalUsed 0
    let number 0

	foreach table:get gnMatrix originName
       [ percent ->
          let newDestination item matrix-loop destinationNames ;; Let agents of 22 origins choose their destinations
          ifelse (newDestination != "others") [set number round(percent * Num) set totalUsed totalUsed + number]
              [set number Num - totalUsed ]
		  ;; if agents move within district, then count agents by rounding the values of population x
      ;; "fraction of region A", population x "fraction of region B"...
		  ;; if agents move outside district, then count the remainder of the population not used for inbound population
          ask n-of number (people with [homeName = originName and destinationName = "unidentified"
                and (age = "active")]) [
                set destinationName newDestination ;; assign destination name
                set destinationPatch one-of patches with [locationName = newDestination]
       ]
    set matrix-loop matrix-loop + 1
  ]
  type totalused type " " type Num type " " print originName ;; print inbound agents out of the total population (age 15-64)
  ]


;; Send agents selected as "others" to the NE corner
 ask people [ if destinationName = "others"
             [ set destinationPatch patch max-pxcor max-pycor]
             ]

 ask people with [destinationpatch = "unidentified" and age = "young"]
                 [set destinationName homeName
                  set destinationPatch one-of patches in-radius 3] ;; Under 15
 ask people with [destinationpatch = "unidentified" and age = "old"]
                 [set destinationName homeName
                  set destinationPatch one-of patches in-radius 1] ;; Over 65

 output-print "People without destinations(nobody)"
 let wordloop 0
 foreach destinationNames [ dn ->
    output-print word (word(word (word dn ": ") count people with
    [homename = dn and destinationPatch = nobody] ) " out of " ) count people with
    [homename = dn ]
       ]
 set wordloop wordloop + 1

end


to set-at-hospital
  ask patch min-pxcor min-pycor [set pcolor grey + 1]
  ;ask patches with [ count neighbors with [pcolor = grey + 1] = 8 ][set pcolor grey + 1 set hospital "true"]
end


;;;;;;;;;;;;;;;;;;
to go-hospital
  ask people [if (health <= 0) [move-to patch min-pxcor min-pycor fd 1]]
    ;move-to one-of patches with [pcolor = grey + 1] fd 1]];
end

to move-people
  ifelse ticks mod 2 = 0 [move-out][come-home]

end

to move-out
  ask people [if patch-here != destinationPatch [ move-to destinationPatch fd 1]
  ]
end

to come-home
  ask people [
    if patch-here != homePatch [move-to homePatch fd 1]
  ]
end


;;;;;;;;;;;;;;;;;;;;;;;;;;
to adaptive-cap
  if (health < AC) and ([land] of patch-here  < 920459)  [set health health + 0.01]
  if (health < AC) and ([land] of patch-here >= 920459)   and ([land] of patch-here < 1550940) [set health health + 0.01]
  if (health < AC) and ([land] of patch-here >= 1550940)  and ([land] of patch-here < 2091133) [set health health + 0.02]
  if (health < AC) and ([land] of patch-here >= 2091133)  and ([land] of patch-here < 2637070) [set health health + 0.04]
  if (health < AC) and ([land] of patch-here >= 2637070)  and ([land] of patch-here < 3273274) [set health health + 0.05]
  if (health < AC) and ([land] of patch-here >= 3273274)  and ([land] of patch-here < 4140183) [set health health + 0.05]
  if (health < AC) and ([land] of patch-here >= 4140183)  and ([land] of patch-here < 5443608) [set health health + 0.06]
  if (health < AC) and ([land] of patch-here >= 5443608)  and ([land] of patch-here < 8361806) [set health health + 0.07]
  if (health < AC) and ([land] of patch-here >= 8361806)  and ([land] of patch-here < 11545447) [set health health + 0.12]
  if (health < AC) and ([land] of patch-here >= 11545447) and ([land] of patch-here < 20261596) [set health health + 0.15]

end
;;;;;;;;;;;;;;;;;;;;;;;;;;
to inhalation
  ifelse ([road] of patch-here = true)[road-effect] [non-road-effect]

end
;;;;;;;;;;;;;;;;;;;;;;;;;;
to sensitivity
  if (ts__kal >= PM10-parameters) and (health < 300) and ((age = "old") or (age = "young" or edu < 1 + random 3))
     [set health (health - random-float 0.004 * (310 - health))]
  if (health < 200 and health >= 100) [set color violet + 2]
  if (health < 100) [set color red]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;
to non-road-effect
  if(ts__kal >= PM10-parameters)
     [set health health - random-float 0.004 * (310 - health)] ;arbitrarily
end
;;;;;;;;;;;;;;;;;;;;;;;;;;
to road-effect
  if(ts__kal * 1.426 >= PM10-parameters)
     [set health health - random-float 0.005 * (310 - health)] ;arbitrarily
end

;;;;;;;;;;;;;;;;;;;;;;;;;;


to calc-pm10
  if (Scenario = "BAU")
  [ask patches with [gwanak = true]
    [
     if ticks > 0 [
     if (ticks + 1) mod 2 = 0 [set ts__kal item (3 + random 13) table:get ts_kal ticks + 1]
     if ticks mod 2 = 0       [set ts__kal item (3 + random 11) table:get ts_kal ticks + 1]
    ]
  ]]

    if (Scenario = "INC")
  [ask patches with [gwanak = true]
    [
     if ticks > 0 and ticks <= 4382 [set-BAU]
     if ticks > 4382 [set-INC&DEC]
    ]
  ]

  if (Scenario = "DEC")
  [ask patches with [gwanak = true]
    [
     if ticks > 0 and ticks <= 4382 [set-BAU]
     if ticks > 4382 [set-INC&DEC]
    ]
  ]
end

to set-BAU
   if (ticks + 1) mod 2 = 0 [set ts__kal  item (3 + random 13) table:get ts_kal  ticks + 1]
   if ticks mod 2 = 0       [set ts__kal  item (3 + random 11) table:get ts_kal  ticks + 1]
end

to set-INC&DEC
  let homeID item (3 + random 13) table:get ts_kal ticks + 1
  let workID item (3 + random 11) table:get ts_kal ticks + 1

  let %3inc 5
  if scenario-percent = "inc-sce" [set %3inc %3inc]
  if scenario-percent = "dec-sce" [set %3inc %3inc + 1]

     if ticks > 4382 and ticks <= 4562 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 1)]
     if ticks > 4562 and ticks <= 4744 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 2)]
     if ticks > 4744 and ticks <= 4850 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 3)]
     if ticks > 4850 and ticks <= 5112 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 4)]
     if ticks > 5112 and ticks <= 5292 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 5)]
     if ticks > 5292 and ticks <= 5474 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 6)]
     if ticks > 5474 and ticks <= 5658 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 7)]
     if ticks > 5658 and ticks <= 5842 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 8)]
     if ticks > 5842 and ticks <= 6024 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 9)]
     if ticks > 6024 and ticks <= 6206 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 10)]
     if ticks > 6206 and ticks <= 6390 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 11)]
     if ticks > 6390 and ticks <= 6574 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 12)]
     if ticks > 6574 and ticks <= 6754 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 13)]
     if ticks > 6754 and ticks <= 6936 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 14)]
     if ticks > 6936 and ticks <= 7120 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 15)]
     if ticks > 7120 and ticks <= 7304 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 16)]
     if ticks > 7304 and ticks <= 7484 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 17)]
     if ticks > 7484 and ticks <= 7666 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 18)]
     if ticks > 7666 and ticks <= 7850 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 19)]
     if ticks > 7850 and ticks <= 8034 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 20)]
     if ticks > 8034 and ticks <= 8214 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 21)]
     if ticks > 8214 and ticks <= 8396 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 22)]
     if ticks > 8396 and ticks <= 8580 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 23)]
     if ticks > 8580 and ticks <= 8764 and ticks mod 2 = 0 [set ts__kal workID + (item %3inc table:get q_kal 24)]

     if ticks > 4382 and ticks <= 4562 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 1)]
     if ticks > 4562 and ticks <= 4744 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 2)]
     if ticks > 4744 and ticks <= 4850 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 3)]
     if ticks > 4850 and ticks <= 5112 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 4)]
     if ticks > 5112 and ticks <= 5292 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 5)]
     if ticks > 5292 and ticks <= 5474 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 6)]
     if ticks > 5474 and ticks <= 5658 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 7)]
     if ticks > 5658 and ticks <= 5842 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 8)]
     if ticks > 5842 and ticks <= 6024 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 9)]
     if ticks > 6024 and ticks <= 6206 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 10)]
     if ticks > 6206 and ticks <= 6390 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 11)]
     if ticks > 6390 and ticks <= 6574 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 12)]
     if ticks > 6574 and ticks <= 6754 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 13)]
     if ticks > 6754 and ticks <= 6936 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 14)]
     if ticks > 6936 and ticks <= 7120 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 15)]
     if ticks > 7120 and ticks <= 7304 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 16)]
     if ticks > 7304 and ticks <= 7484 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 17)]
     if ticks > 7484 and ticks <= 7666 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 18)]
     if ticks > 7666 and ticks <= 7850 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 19)]
     if ticks > 7850 and ticks <= 8034 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 20)]
     if ticks > 8034 and ticks <= 8214 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 21)]
     if ticks > 8214 and ticks <= 8396 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 22)]
     if ticks > 8396 and ticks <= 8580 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 23)]
     if ticks > 8580 and ticks <= 8764 and (ticks + 1) mod 2 = 0 [set ts__kal homeID + (item %3inc table:get q_kal 24)]
end



;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to landprice-change
  ask patches with [land >= 0][set land (random-float .1 + land)]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to gu-plot
  set-current-plot "District level"
  set-current-plot-pen "risky"     plot ((count people with [color = violet + 2 and destinationName != "others"]) /
                                        (count people with [destinationName != "others"]) * 100)
  set-current-plot-pen "dangerous" plot ((count people with [color = red and destinationName != "others"]) /
                                        (count people with [ destinationName != "others"]) * 100)
end


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
to dong-plot
  set-current-plot "Subdistrict level"
  set-current-plot-pen "boramae_risk"  plot((count people with [color = red and districtName = "boramae"]) / (count people with [districtName = "boramae"]) * 100)
	set-current-plot-pen "chnim_risk"    plot((count people with [color = red and districtName = "chnim"])   / (count people with [districtName = "chnim"])   * 100)
	set-current-plot-pen "hengun_risk"   plot((count people with [color = red and districtName = "hengun"])  / (count people with [districtName = "hengun"])  * 100)
	set-current-plot-pen "inheon_risk"   plot((count people with [color = red and districtName = "inheon"])  / (count people with [districtName = "inheon"])  * 100)
	set-current-plot-pen "nak_risk"      plot((count people with [color = red and districtName = "nak"])     / (count people with [districtName = "nak"])     * 100)
	set-current-plot-pen "jung-ang_risk" plot((count people with [color = red and districtName = "jungang"])/ (count people with [districtName = "jungang"])* 100)
	set-current-plot-pen "namhyeon_risk" plot((count people with [color = red and districtName = "namhyeon"])/ (count people with [districtName = "namhyeon"])* 100)
	set-current-plot-pen "seowon_risk"   plot((count people with [color = red and districtName = "seowon"])  / (count people with [districtName = "seowon"])  * 100)
	set-current-plot-pen "sinwon_risk"   plot((count people with [color = red and districtName = "sinwon"])  / (count people with [districtName = "sinwon"])  * 100)
	set-current-plot-pen "seorim_risk"   plot((count people with [color = red and districtName = "seorim"])  / (count people with [districtName = "seorim"])  * 100)
	set-current-plot-pen "sinsa_risk"    plot((count people with [color = red and districtName = "sinsa"])   / (count people with [districtName = "sinsa"])   * 100)
	set-current-plot-pen "sillim_risk"   plot((count people with [color = red and districtName = "sillim"])  / (count people with [districtName = "sillim"])  * 100)
	set-current-plot-pen "nanhyang_risk" plot((count people with [color = red and districtName = "nanhyang"])/ (count people with [districtName = "nanhyang"])* 100)
	set-current-plot-pen "jowon_risk"    plot((count people with [color = red and districtName = "jowon"])   / (count people with [districtName = "jowon"])   * 100)
	set-current-plot-pen "daehak_risk"   plot((count people with [color = red and districtName = "daehak"])  / (count people with [districtName = "daehak"])  * 100)
	set-current-plot-pen "euncheon_risk" plot((count people with [color = red and districtName = "euncheon"])/ (count people with [districtName = "euncheon"])* 100)
	set-current-plot-pen "sunghyun_risk" plot((count people with [color = red and districtName = "seonghyeon"])/ (count people with [districtName = "seonghyeon"])* 100)
	set-current-plot-pen "chungryong_risk" plot((count people with [color = red and districtName = "chungryong"])/ (count people with [districtName = "chungryong"]) * 100)
	set-current-plot-pen "nangok_risk"   plot((count people with [color = red and districtName = "nangok"])  / (count people with [districtName = "nangok"])  * 100)
	set-current-plot-pen "samsung_risk"  plot((count people with [color = red and districtName = "samsung"]) / (count people with [districtName = "samsung"]) * 100)
	set-current-plot-pen "miseong_risk"  plot((count people with [color = red and districtName = "miseong"]) / (count people with [districtName = "miseong"]) * 100)
end


to age-plot
  set-current-plot "By Age Group"
  set-current-plot-pen "Young"  ;ifelse(count people with [(age < 15)] != 0)[
    plot(count people with [age = "young" and color = red and destinationName != "others"]) /
        (count people with [age = "young" and destinationName != "others"]) * 100
  set-current-plot-pen "Middle" plot((count people with [age = "active" and color = red and destinationName != "others"]) / (count people with [age = "active" and destinationName != "others"]) * 100)
  set-current-plot-pen "Old"    plot((count people with [age = "old" and color = red and destinationName != "others"]) / (count people with [age = "old" and destinationName != "others"]) * 100)

end

to edu-plot
  set-current-plot "By Education"
  set-current-plot-pen "High" plot((count people with [edu >= 3 and color = red and destinationName != "others"]) / (count people with [edu >= 3 and destinationName != "others"]) * 100)
  set-current-plot-pen "Low"  plot((count people with [edu < 3 and color = red and destinationName != "others"]) / (count people with [edu < 3 and destinationName != "others"]) * 100)

end

to pm10-plot
  set-current-plot "PM10 patches"
  set-current-plot-pen "pm10-jungang-road" plot [ts__kal] of patch 155 171
  ;set-current-plot-pen "pm10-yeoksam1"   plot [ts__kal] of patch 60 160
  ;set-current-plot-pen "pm10-daechi1"    plot [ts__kal] of patch 140 140
  ;set-current-plot-pen "pm10-segok"      plot [ts__kal] of patch 260 60
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;Copyright: Hyesop Shin, University of Cambridge, hs621@cam.ac.uk;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
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"" ""
PENS
"boramae_risk" 1.0 0 -7500403 true "" ""
"chnim_risk" 1.0 0 -2674135 true "" ""
"hengun_risk" 1.0 0 -955883 true "" ""
"inheon_risk" 1.0 0 -6459832 true "" ""
"nak_risk" 1.0 0 -1184463 true "" ""
"jung-ang_risk" 1.0 0 -10899396 true "" ""
"namhyeon_risk" 1.0 0 -14835848 true "" ""
"seowon_risk" 1.0 0 -11221820 true "" ""
"sinwon_risk" 1.0 0 -13791810 true "" ""
"seorim_risk" 1.0 0 -13345367 true "" ""
"sinsa_risk" 1.0 0 -8630108 true "" ""
"sillim_risk" 1.0 0 -5825686 true "" ""
"nanhyang_risk" 1.0 0 -2064490 true "" ""
"jowon_risk" 1.0 0 -14454117 true "" ""
"daehak_risk" 1.0 0 -1069655 true "" ""
"euncheon_risk" 1.0 0 -8330359 true "" ""
"sunghyun_risk" 1.0 0 -10603201 true "" ""
"chungryong_risk" 1.0 0 -12186836 true "" ""
"nangok_risk" 1.0 0 -15582384 true "" ""
"samsung_risk" 1.0 0 -13297659 true "" ""
"miseong_risk" 1.0 0 -5987164 true "" ""

PLOT
10
187
170
307
District level
NIL
NIL
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"risky" 1.0 0 -8630108 true "" ""
"dangerous" 1.0 0 -2674135 true "" ""

PLOT
10
317
170
437
By Age Group
NIL
NIL
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"Young" 1.0 0 -955883 true "" ""
"Middle" 1.0 0 -14835848 true "" ""
"Old" 1.0 0 -6459832 true "" ""

PLOT
180
317
340
437
By Education
NIL
NIL
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"High" 1.0 0 -13345367 true "" ""
"Low" 1.0 0 -4699768 true "" ""

TEXTBOX
682
27
832
49
Gwanak District
18
0.0
1

TEXTBOX
242
55
333
73
*PM10 Trend
14
0.0
1

MONITOR
17
127
89
172
NIL
%riskpop
17
1
11

MONITOR
94
127
186
172
Date
date
17
1
11

MONITOR
190
127
259
172
Location
where
17
1
11

PLOT
348
187
510
307
PM10 patches
time
pm10
0.0
10.0
0.0
200.0
true
false
"" ""
PENS
"pm10-jungang-road" 1.0 0 -7500403 true "" ""
"pm10-nangok" 1.0 0 -2674135 true "" ""
"pm10-nak" 1.0 0 -955883 true "" ""
"pm10-namhyeon" 1.0 0 -6459832 true "" ""

CHOOSER
240
77
332
122
Scenario
Scenario
"BAU" "INC" "DEC"
0

OUTPUT
350
317
511
436
12

CHOOSER
267
126
391
171
PM10-parameters
PM10-parameters
50 80 100 150
2

MONITOR
399
128
465
173
Hospital
number-dead
17
1
11

CHOOSER
358
72
496
117
scenario-percent
scenario-percent
"inc-sce" "dec-sce"
0

TEXTBOX
113
48
263
74
*Adaptive Capacity Change
12
0.0
1

@#$#@#$#@
## WHAT IS IT?
**SIMULATING PEDESTRIAN EXPOSURE TO AMBIENT URBAN AIR POLLUION**

The model’s objective is to understand the cumulative effects on the population’s vulnerability as represented by exposure to PM10 (particulate matter with diameter less than 10 micrometres) by different age and educational groups in Gangnam. Using this model, readers can explore individual's daily commuting routine, and its health loss when the PM10 concentration of the current patch breaches the national limit.

The model is initialised with a starting population with no previous exposure, in other words every agents have health of 300 in the beginning. This is because we couldn't find reliable references or statistics to access individual health reports.

## HOW IT WORKS
*Number of agents*. We used a 1% sample of Gangnam's total population, which was retrieved from the census website, rounded to a total of 5050 agents waiting for simulation. Agents aged under 15 are coloured in orange, between 15 and 64 are in turquoise, and over 65 in brown.

*Set destination*. During the setup process, every agent is assigned a fixed home name (sub-district) and home patch as well as their destination name and patch. In addition, agents will have their destination names and patches but differ by age groups. Agents between *age 15 and 65*, so called the economically active population, will move to their destination patches according to the fraction in the origin-destination (OD) matrix. Those who commute to other districts are assigned as *others*, and are allocated to patches outside Gangnam during working hours. Agents *aged under 15* will move to a random patch within 3 radius, while those *aged over 65* will move to a random patch within 1 radius. Given that the model is a simplified version, the model was designed to move agents in two phases: move-out and come-home.

*Health loss*. With initial health of 300, each agent will be located within their residential patch and be exposed to the patches at which they are stepping on. When the model activates, agents will move back and forth to their home destination patches. An agent will lose health when  exposed to a patch exceeding the PM10 concentration of 50, 100, or 150 μg/m3. Once the agent's health goes below 200, its colour will change to violet, then to red when it goes below 100. While the district plot captures both agents coloured in violet and red, the other plots capture agents only coloured in red. Even without pollution, people's health naturally aggravate over time, depending on age groups. Indeed, this model is not designed to kill people due to a short simulation period of 6 years. However, the interest here is to investigate health variances between groups and sub-regions.

*Health recovery* There is also a recovery mode that heals people's health. We selected land price as a representative factor for remedy. We assumed that hospitals and clinics in Seoul locate in costly areas, which therefore creates better opportunities to nearby residents to visit the doctor when needed.

*Expected outcomes*. This model expects four different outcomes. First plot is the total percentage of risk population in Gangnam. Second plot is the total percentage of risk population by subdistrict level. Third plot is the total percentage of risk population by age groups, followed by the last plot by education groups.

*Simulation time*. The model estimates the dynamic change (vulnerability) of the risk population by socioeconomic factors, and district for 6 years (4381 ticks) for which we have pollution data.


## HOW TO USE IT
**SIMPLE**: Press setup and go to run the model. The model would not stop until the total risk population exceeds 90% or the calendar date reaches the 31st of December 2015. If you would like to know the background information, keep on reading!

### Setup
*GIS DATA*: We imported GIS data of administrative boundary, road networks, land price, and a daily-mean interpolation map of PM10 all in ASCII format.

*ATTRIBUTE DATA*: We imported attributes from the 2010 Korean census of Gangnam in sub-district level by age and education status. Also, daily pollution statistics, provided by the national institution for environmental research (NIER), were cleaned and aggregated to office and home hours. We defined office hours from 9am to 7pm, and the remainder to home hours. For atmospheric settings, each patch contains its own daily average value of PM10 in the beginning, and changes its value over time according to its daily pollution statistics.

### Parameter settings
*AC*: These scenarios impose “adaptive capacity” on the maximum health for the whole population of 100, 150, and 200. Compared to scenario 100, scenario 200 means that the population has more capacity to accumulate resilience when pollution levels remain low.
 
*Scenario*: The pollution scenario consists of 3 types: business as usual (BAU), assuming the pollution trend as per the statistics continues for 6 years; increase (INC), where an addition of 15% of BAU is added everyday; and decrease (DEC), an 15% of BAU is degraded everyday.

*PM10 parameter*: We chose parameters to correspond different daily standards in various countries, where EU/UK in 50μg/m3, South Korea in 100μg/m3, and US in 100μg/m3. 

### Monitors
*%riskpop* counts the total population coloured in red
*Date*: Actual date of pollution provided
*Where* is the current location of agents

## THINGS TO NOTICE
One tick is a half a day.
Agents will jump to their destinations and origins.
Agents who are assigned as *others* are inter-district commuters, but move to the far east corner for simplicity.
## THINGS TO TRY
Use different PM10 parameters and see the temporal difference.
Apply Scenarios.


## EXTENDING THE MODEL
We are planning to combine this model to a traffic model that takes into account exposures to tailpipe emission.

## NETLOGO FEATURES
(interesting or unusual features of NetLogo that the model uses, particularly in the Code tab; or where workarounds were needed for missing features)

## RELATED MODELS
UrbanSuite - Pollution

## CREDITS AND REFERENCES
@#$#@#$#@
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x
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@#$#@#$#@
NetLogo 6.0.4
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
default
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0.0 1 1.0 0.0
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link direction
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Line -7500403 true 150 150 90 180
Line -7500403 true 150 150 210 180
@#$#@#$#@
0
@#$#@#$#@