diff --git a/1_Code/0000_General/0001_model_control-flow.r b/1_Code/0000_General/0001_model_control-flow.r
deleted file mode 100644
index 7b7f41eb..00000000
--- a/1_Code/0000_General/0001_model_control-flow.r
+++ /dev/null
@@ -1,14 +0,0 @@
-source("1_code/0000_general/general_init.R")
-params <- init("middle")
-
-time_start <- Sys.time()
-
-# run_scen(
-#     scenarios = c("lower", "middle", "upper"),
-#     modules = c("all"))
-
-# run_scen(
-#     scenarios = c("lower", "middle", "upper"),
-#     modules = c("out"))
-
-render_book(cache_refresh = TRUE)
diff --git a/1_Code/0000_General/general_init.R b/1_Code/0000_General/general_init.R
index 8269dc56..0c94352e 100644
--- a/1_Code/0000_General/general_init.R
+++ b/1_Code/0000_General/general_init.R
@@ -10,40 +10,44 @@
 #' @export
 #'
 #' @examples util_gf("lower")
+
+
 init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.yml"){
+  
   # packages
-  require(tidyverse)
+  require(ckanr) # download of open data directly from ckan  
   require(dvmisc) # for expand_grid
-  require(gridExtra) # for ggplot on multiple pages
-  require(nlme) # needed for mgcv, see below
-  require(mgcv) # for gam
-  require(flexclust) # weighted kmeans clustering
-  require(ggrepel) # to label lines (when too many for a normal legend)
-  require(zoo) # moving average (no confusion with filter function in base R)
-  require(ckanr) # download of open data directly from ckan
-  require(scales) # for pretty axis breaks
-  require(rlang) # for functions: symbols, quasiquotation (!!, !!!, :=)
-  require(gtools) # invalid function (to check for NA, NULL, NaN), e.g. in constrained regression
-  # require(this.path) # to extract the current file name
-  require(modelr) # add_predictions
+  require(flexclust) # weighted kmeans clustering  
+  require(ggrepel) # to label lines (when too many for a normal legend)  
+  require(gridExtra) # for ggplot on multiple pages  
+  require(gtools) # invalid function (to check for NA, NULL, NaN), e.g. in constrained regression  
+  require(mgcv) # for gam   
+  require(modelr) # add_predictions  
+  require(nlme) # needed for mgcv  
   require(quarto) #for programmatically rendering quarto
+  require(rlang) # for functions: symbols, quasiquotation (!!, !!!, :=)  
+  require(scales) # for pretty axis breaks  
+  require(this.path) #for this.dir function
+  require(tidyverse) #tidyverse functionality
+  require(zoo) # moving average (no confusion with filter function in base R)  
+
   
   # no scientic notation
   options(scipen = 999)
     
-  # general stuff (without dependency on parameters) ----------------------------------
+  # general objects without dependency on parameters ----------------------------------
   # read general variables for common use
   vars <- yaml::read_yaml(paste0(here::here(), var_file), eval.expr = TRUE, fileEncoding = "UTF-8")
   vars$i_scen <- i_scen
   
   # import parameters
   para <- read_delim(paste0(here::here(), vars$para_file), ";", lazy = FALSE) %>%
-    select(parameter, lower, middle, upper) %>%
+    select(parameter, lower, middle, middle_birth_lower, middle_birth_upper, upper) %>%
     pivot_longer(cols = lower:upper, names_to = "scenario") %>%
     filter(scenario == i_scen) %>%
     select(parameter, value)
   
-  # read parameters (depend on scenario)
+  # read parameters (depending on scenario)
   for (i_para in 1:nrow(para)) {
     assign(para$parameter[i_para], para$value[i_para], envir = .GlobalEnv)
   }
@@ -53,11 +57,11 @@ init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.ym
   
 
   # create variables --------------------------------------------------------
+  
   # lookup tables
   vars$look_dis <- read_csv2(paste0(here::here(), vars$dis_file), lazy = FALSE) %>%
     select(QuarCd, distr)
 
-  
   vars$look_reg <- mutate(vars$look_dis, distnum = if_else(distr == "Kreis 1", 10, as.numeric(QuarCd))) %>%
     select(distnum, distr) %>%
     unique() %>%
@@ -105,6 +109,16 @@ init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.ym
       age < vars$age_4[5] ~ vars$age_4t[4],
       TRUE ~ vars$age_4t[5]
     ), levels = vars$age_4t))
+  
+  vars$look_a5 <- tibble(age = 0:120) %>%
+    mutate(age_5 = factor(case_when(
+      age < vars$age_5[1] ~ vars$age_5t[1],
+      age < vars$age_5[2] ~ vars$age_5t[2],
+      age < vars$age_5[3] ~ vars$age_5t[3],
+      TRUE ~ vars$age_5t[4]
+    ), levels = vars$age_5t))  
+  
+  
 
   # unique levels
   vars$text_d <- unique(vars$look_dis$distr)
@@ -118,6 +132,7 @@ init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.ym
   vars$uni_t <- factor(vars$pro_category, levels = vars$pro_category)
   vars$uni_i <- factor(vars$text_i, levels = vars$text_i)
   vars$uni_c <- factor(vars$text_c, levels = vars$text_c)
+  vars$uni_cb <- factor(vars$text_cb, levels = vars$text_cb) 
   
   vars$look_own <- tibble(EigentumGrundstkCd = as.integer(labels(vars$uni_w)), owner = levels(vars$uni_w))
   
@@ -165,7 +180,10 @@ init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.ym
   # relocation functions
   source(paste0(vars$code_path, "/0000_general/general_relocation-function.r"))
      
-  # general stuff (with dependency on parameters) ----------------------------------
+  
+  
+  
+  # general objects with dependency on parameters ----------------------------------
   
   # path for results
   vars$res_path <- paste0(vars$res_path, i_scen)
@@ -202,7 +220,7 @@ init <- function(i_scen = "middle", var_file = "/2_Data/3_Parameter/variables.ym
   # color for year (base period)
   vars$col_y_base <- colorRampPalette(vars$col_6)(length(vars$uniy_bir_base))
   
-  # colour for time distributions
+  # colors for time distributions
   vars$col_time <- c(
     rep(vars$col_grey, length(vars$uniy_bir_base)),
     colorRampPalette(vars$col_6[1:5])(length(vars$uniy_scen))
diff --git a/1_Code/0000_General/general_utils.R b/1_Code/0000_General/general_utils.R
index 11f0acba..20e1a30f 100644
--- a/1_Code/0000_General/general_utils.R
+++ b/1_Code/0000_General/general_utils.R
@@ -18,7 +18,6 @@
 #' spa (living space)  
 #' aca (allocation)  
 #' pro (projects)  
-#' own (ownership)  
 #' hou (housing model)  
 #' deh (demography and housing model)  
 #' out (model outputs)  
@@ -104,11 +103,6 @@ run_scen <- function(scenarios, modules, keep_log = TRUE) {
       source(paste0(code_path, "1100_Projects/1100_projects.r"))
     }
     
-    # ownership
-    if (modules %in% c("all", "alw", "how", "hom", "own")) {
-      source(paste0(code_path, "1200_Ownership/1200_ownership.r"))
-    }
-    
     # housing model
     if (modules %in% c("all", "alw", "how", "hom", "hou")) {
       source(paste0(code_path, "1300_Housing-Model/1300_housing-model.r"))
@@ -246,20 +240,20 @@ dir_ex_create <- function(path){
 }
 
 
-#' check if 3 files are present
+#' check if 5 files are present
 #' 
-#' @description checks if the input contains 3 values and stops execution if this is not the case
+#' @description checks if the input contains 5 values and stops execution if this is not the case
 #'
 #' @param files character vector
 #'
 #' @return stops execution if condition is not met
 #' @export
 #'
-#' @examples stop_3(c("a.csv", "b.csv", "c.csv"))
-stop_3 <- function(files){
+#' @examples stop_5(c("a.csv", "b.csv", "c.csv", "d.csv", "e.csv"))
+stop_5 <- function(files){
   stopifnot("missing files;\
             make sure to run the whole model (0001_model_control-flow.r) beforehand" = 
-              length(read_files) == 3)
+              length(read_files) == 5)
 }
 
 
diff --git a/1_Code/0000_General/model_control-flow.r b/1_Code/0000_General/model_control-flow.r
new file mode 100644
index 00000000..81a6430e
--- /dev/null
+++ b/1_Code/0000_General/model_control-flow.r
@@ -0,0 +1,19 @@
+source("1_code/0000_general/general_init.R")
+params <- init("middle")
+
+time_start <- Sys.time()
+
+# run_scen(
+#     scenarios = c("lower", "middle", "middle_birth_lower", "middle_birth_upper", "upper"),
+#     modules = c("alw"))
+
+# run_scen(
+#     scenarios = c("lower", "middle", "middle_birth_lower", "middle_birth_upper", "upper"),
+#     modules = c("all"))
+
+Sys.time() - time_start
+
+
+render_book(cache_refresh = TRUE)
+
+
diff --git a/1_Code/0100_Birth/0100_birth-fertility.r b/1_Code/0100_Birth/0100_birth-fertility.r
index 885c10e1..d5641cb0 100644
--- a/1_Code/0100_Birth/0100_birth-fertility.r
+++ b/1_Code/0100_Birth/0100_birth-fertility.r
@@ -146,7 +146,7 @@ fer_fit <- arrange(fer_tail, district, year, origin, age) %>%
 # constrained regression
 # (proportion of linear model and mean, within bandwidth)
 
-fer_pred <- con_reg(
+fer_pred_temp <- con_reg(
   data = fer_fit, x = "year", y = "fer_fit",
   group_cols = c("district", "age", "origin"),
   window = bir_window_thres, base_t0 = bir_base_begin,
@@ -162,7 +162,30 @@ fer_pred <- con_reg(
   left_join(select(fer_dyao, district, year, age, origin, fer_dyao),
     by = c("district", "year", "age", "origin")
   ) %>%
-  mutate(fer_all = if_else(year <= bir_base_end, fer_dyao, pred_roll))
+  mutate(fer_all_temp = if_else(year <= bir_base_end, fer_dyao, pred_roll))
+
+
+# fertility multiplier ----------------------------------------------------
+
+# multiplier by year
+fer_mult_dat <- tibble(year = c(scen_begin, scen_end),
+                       mult = c(bir_mult_begin, bir_mult_end)) |> 
+  mutate(year_new = (year - scen_begin + 1)^bir_mult_exp)
+
+fer_mult <- tibble(year = scen_begin:scen_end) |> 
+  mutate(year_new = (year - scen_begin + 1)^bir_mult_exp) |> 
+  add_predictions(lm(mult ~ year_new, data = fer_mult_dat), var = "mult")
+
+# ggplot(fer_mult) + 
+#   geom_point(aes(x = year, y = mult))
+
+
+
+# fertility rates with multiplier
+fer_pred <- fer_pred_temp |> 
+  left_join(fer_mult, by = "year") |> 
+  mutate(fer_all = fer_all_temp * mult)
+
 
 # plot 0140: the predictions: age distribution by district and year
 
@@ -175,7 +198,7 @@ pred_fit <- filter(fer_pred, year >= scen_begin) %>%
   arrange(district, year, origin, age) %>%
   group_by(district, year, origin) %>%
   mutate(pred_fit = pmax(0, predict(
-    loess(pred_roll ~ age, span = bir_fer_span_pred, degree = 1, na.action = na.aggregate)
+    loess(fer_all ~ age, span = bir_fer_span_pred, degree = 1, na.action = na.aggregate)
   ))) %>%
   ungroup()
 
diff --git a/1_Code/0200_Death/0200_death.r b/1_Code/0200_Death/0200_death.r
index f70c3578..f1e2ad2f 100644
--- a/1_Code/0200_Death/0200_death.r
+++ b/1_Code/0200_Death/0200_death.r
@@ -51,7 +51,7 @@ pop <- read_csv(pop_od) %>%
 # FSO data (used in the prediction)
 # rate is converted to percent
 mor_fso <- read_csv(dea_fso_od) %>%
-  rename(year = EreignisDatJahr, age = AlterVCd) %>%
+  rename(year = EreignisDatJahr, age = AlterVCd, KategorieCd = KategorieDatenBFSCd) %>%
   filter(HerkunftCd == 0) %>%
   mutate(
     sex = fact_if(SexCd, uni_s),
@@ -316,6 +316,18 @@ mor_zh_yas_past_future <- select(mor_zh_yas, year, age, sex, mor_yas) %>%
 
 # plot 0210
 
+# ZH and CH: past and future
+mor_zh_ch_yas_past_future <- mor_zh_yas_past_future |> 
+  mutate(region = factor(text_r[1], uni_r)) |> 
+  right_join(mor_yasr, by = c("year", "age", "sex", "region")) |> 
+  mutate(mor_new = if_else((year >= scen_begin) & (region == uni_r[1]), 
+                           mor_yas, mor_yasr)) |> 
+  select(year, age, sex, region, mor_new) |> 
+  rename(mor_yasr = mor_new) |> 
+  arrange(year, age, sex, region)
+
+# plot 0210a
+
 # export mortality rates --------------------------------------------------
 
 # prepare the export data
diff --git a/1_Code/0900_Living-Space/0900_living-space.r b/1_Code/0900_Living-Space/0900_living-space.r
index 6e060ee2..df69a3fa 100644
--- a/1_Code/0900_Living-Space/0900_living-space.r
+++ b/1_Code/0900_Living-Space/0900_living-space.r
@@ -16,7 +16,7 @@ spa_dat <- read_csv(spa_od) %>%
   rename(year = StichtagDatJahr, area = Wohnflaeche, apartments = AnzWhgStat, people = AnzBestWir) %>%
   left_join(look_dis, by = "QuarCd") %>%
   mutate(
-    owner = fact_if(EigentuemerSSZPubl3Cd_noDM , uni_w),
+    owner = fact_if(EigentuemerSSZPubl3Cd_noDM, uni_w),
     district = factor(distr, uni_d)
   ) %>%
   group_by(year, district, owner) %>% 
diff --git a/1_Code/1100_Projects/1100_projects.r b/1_Code/1100_Projects/1100_projects.r
index 5d522dac..73d74216 100644
--- a/1_Code/1100_Projects/1100_projects.r
+++ b/1_Code/1100_Projects/1100_projects.r
@@ -100,7 +100,7 @@ pro_delay <- as_tibble(expand_grid(
   owner = uni_w,
   status = pro_category,
   indicator = uni_i,
-  delay = as.double(0:5)
+  delay = as.double(0:pro_max_delay)
 )) %>%
   left_join(select(pro_not, c(district, year, owner, status, indicator, realized)),
     by = c("district", "year", "owner", "status", "indicator")
diff --git a/1_Code/1200_Ownership/1200_ownership.r b/1_Code/1200_Ownership/1200_ownership.r
deleted file mode 100644
index dd6aaa60..00000000
--- a/1_Code/1200_Ownership/1200_ownership.r
+++ /dev/null
@@ -1,106 +0,0 @@
-# header ------------------------------------------------------------------
-# ownership
-
-# paths, general ----------------------------------------------------------
-
-# source(paste0(here::here(),"/1_code/0000_general/general_init.R"))
-# init()
-
-# start time
-t0 <- Sys.time()
-
-# import, data preparation ------------------------------------------------
-
-# ownership: data
-own_dat <- read_csv(spa_od) %>%
-  rename(year = StichtagDatJahr, apartments = AnzWhgStat, people = AnzBestWir) %>%
-  left_join(look_dis, by = "QuarCd") %>%
-  mutate(
-    owner = fact_if(EigentuemerSSZPubl3Cd_noDM, uni_w),
-    district = factor(distr, uni_d)
-  ) %>%
-  select(year, district, owner, apartments, people) %>%
-  group_by(district, year, owner) %>%
-  summarize(
-    apartments = sum_NA(apartments),
-    people = sum_NA(people),
-    .groups = "drop")
-
-# proportion of cooperative housing ---------------------------------------
-
-# proportions (based on apartments and people)
-own_prop <- group_by(own_dat, district, year) %>%
-  mutate(
-    prop_apartments = round(apartments / sum_NA(apartments) * 100, round_prop),
-    prop_people = round(people / sum_NA(people) * 100, round_prop)
-  ) %>%
-  filter(owner == uni_w[1]) %>%
-  select(district, year, prop_apartments, prop_people) %>%
-  pivot_longer(
-    cols = starts_with("prop"), names_prefix = "prop_",
-    names_to = "category", values_to = "prop"
-  )
-
-# plot 1200
-
-# prediction --------------------------------------------------------------
-
-# category: people
-# WHY? prediction is based on different sources
-# projects: apartments
-# reserves: m2
-# however, the combined prediction is for persons
-# therefore, the proportion based on people
-
-own_cat <- filter(own_prop, category == "people") %>%
-  select(district, year, prop) %>%
-  ungroup()
-tail(own_cat)
-
-# base years
-# WHY not in previous pipe? The previous tibble will be used later for plotting
-own_base <- filter(own_cat, (year >= own_base_begin) & (year <= own_base_end))
-
-# prediction
-own_pred <- con_reg(
-  data = own_base, x = "year", y = "prop",
-  group_cols = "district",
-  window = own_window_thres, base_t0 = own_base_begin,
-  scen_t0 = scen_begin, scen_t1 = scen_end,
-  prop_trend = own_prop_trend, thres_percent = own_thres_percent,
-  lower_thres = own_lower_thres, upper_thres = own_upper_thres
-)
-
-# past and prediction
-own_past_pred <- as_tibble(expand_grid(
-  district = uni_d,
-  year = (min(own_cat$year)):scen_end
-)) %>%
-  left_join(own_cat, by = c("district", "year")) %>%
-  left_join(select(own_pred, district, year, pred_roll),
-    by = c("district", "year")
-  ) %>%
-  mutate(own_all = if_else(year < scen_begin, prop, pred_roll))
-
-# plot 1201
-
-# export the results ------------------------------------------------------
-
-# data of past and future
-# WHY? to calculate population (by ownership) in the past
-# since the people in the open data dataset (with ownership)
-# is only people in apartments
-
-# export data
-own_ex_data <- mutate(own_past_pred, prop = round(own_all, round_prop)) %>%
-  select(district, year, prop) %>%
-  arrange(district, year)
-
-# export
-write_csv(own_ex_data, paste0(exp_path, "/ownership_past_future.csv"))
-
-# log info
-cat_log(paste0(
-  "ownership: ",
-  capture.output(Sys.time() - t0)
-))
diff --git a/1_Code/1300_Housing-Model/1300_housing-model.r b/1_Code/1300_Housing-Model/1300_housing-model.r
index e9229e98..2ef3ce6b 100644
--- a/1_Code/1300_Housing-Model/1300_housing-model.r
+++ b/1_Code/1300_Housing-Model/1300_housing-model.r
@@ -17,21 +17,40 @@ pro_dat <- read_csv(paste0(exp_path, "/projects_future.csv"), lazy = FALSE)
 # allocation (persons per apartment, dyw)
 aca_dat <- read_csv(paste0(exp_path, "/allocation_future.csv"), lazy = FALSE)
 
-# capacity/reserves (m2, dyw)
+# car_dat
 car_dat <- read_csv(paste0(exp_path, "/usage_area.csv"), lazy = FALSE)
 
 # living space (m2 per person, dyw)
 spa_dat <- read_csv(paste0(exp_path, "/living-space_future.csv"), lazy = FALSE)
 
-# ownership (% cooperative housing)
-own_dat <- read_csv(paste0(exp_path, "/ownership_past_future.csv"), lazy = FALSE)
+# ownership (proportion of people living in cooperative housing, past)
+own_dat <- read_csv(spa_od) %>%
+  rename(year = StichtagDatJahr, apartments = AnzWhgStat, people = AnzBestWir) %>%
+  left_join(look_dis, by = "QuarCd") %>%
+  mutate(
+    owner = fact_if(EigentuemerSSZPubl3Cd_noDM, uni_w),
+    district = factor(distr, uni_d)
+  ) %>%
+  select(year, district, owner, people) %>%
+  group_by(district, year, owner) %>%
+  summarize(
+    people = sum_NA(people),
+    .groups = "drop") %>%
+  group_by(district, year) %>%
+  mutate(
+    prop = round(people / sum_NA(people) * 100, round_prop),
+    .groups = "drop"
+  ) %>%
+  filter(owner == uni_w[1]) %>%
+  select(district, year, prop)
 
 # population
 # why population not from the housing open data file?
-# there only people in apartments (and not in care centers etc)
+# this file only contains people in apartments (and not in care centers etc)
 # the population number in the housing open data is below the total
 # amount of people in Zurich
 
+# dy (past years)
 pop <- read_csv(pop_od, lazy = FALSE) %>%
   rename(year = StichtagDatJahr, pop = AnzBestWir) %>%
   left_join(look_dis, by = "QuarCd") %>%
@@ -45,7 +64,9 @@ pop <- read_csv(pop_od, lazy = FALSE) %>%
 
 # projects and allocation (from apartments to people; future) -------------
 
-# calculate amount of people
+# calculate amount of people from 
+# - projects 'pro' (num of apartments)
+# - allocation 'aca' (number of pop per apartment)
 pro_aca <- left_join(pro_dat, aca_dat,
   by = c("district", "year", "owner")
 ) %>%
@@ -53,14 +74,14 @@ pro_aca <- left_join(pro_dat, aca_dat,
   select(district, year, owner, indicator, people) %>%
   pivot_wider(names_from = indicator, values_from = people)
 
-# if additional information is available on new projects (e.g. amount of people)
-# this could be incorporated here
-
 # capacity/reserves and living space (from m2 to people; future) ----------
 
 # combine: calculate amount of people
 # units: ha * 10,000 m2/ha / (m2/person) = person
 
+# calculate amount of people from ...
+# - capacity 'car' (area)
+# - living space 'spa' (area consumption)
 car_spa <- left_join(car_dat, spa_dat,
   by = c("district", "year", "owner")
 ) %>%
@@ -70,6 +91,7 @@ car_spa <- left_join(car_dat, spa_dat,
 # population by ownership (past) ------------------------------------------
 
 # join pop on ownership (since this data set begins later)
+# OUT: population of past year per ownership > this ownership information is still necessary
 pop_w <- left_join(own_dat, pop, by = c("district", "year")) %>%
   filter(year <= date_end) %>%
   mutate(
@@ -83,15 +105,16 @@ pop_w <- left_join(own_dat, pop, by = c("district", "year")) %>%
   mutate(owner = fact_if(owner_text, uni_w, "cooperative")) %>%
   select(district, year, owner, pop)
 
-# last year of data
-pop_last <- filter(pop_w, year == date_end)
+# last year of data 
+pop_last <- pop_w %>%
+  ungroup() %>%
+  filter(year == date_end)
 
 # combine: capacity/reserves and ownership prediction ---------------------
 
 # proportion of cooperative housing according to capacity/reserves
 # capacity/reserves contains only people due to additional (!) yearly (!) usage of reserves
 # therefore, add the cumulative values to the past population
-
 pop_total <- car_spa %>%
   arrange(district, owner, year) %>%
   group_by(district, owner) %>%
@@ -104,58 +127,27 @@ pop_total <- car_spa %>%
   select(district, year, owner, total) %>%
   rename(pop = total)
 
-# with past (for plot)
-# why? for plotting
-# why a plot? to check if capacity/reserves population values are...
-# ...meaningful in comparison to the past
-
+# row-binding past_pop and pop_total (which is today's pop + cumulative car_spa)
+# Why doing so? 
+# For plotting reasons, to for plotting to check if capacity/reserves population 
+# values are meaningful in comparison to the past
 pop_with_past <- bind_rows(pop_w, pop_total) %>%
   rename(distr = district) %>%
   mutate(district = factor(distr, uni_d)) %>%
   select(district, year, owner, pop)
 
-# plot 1300
-
-# proportion cooperative housing (according to capacity/reserves vs. district trends)
-prop_coop <- pop_with_past %>%
-  mutate(simple = if_else(owner == uni_w[1], "cooperative", "private")) %>%
-  select(-owner) %>%
-  pivot_wider(names_from = simple, values_from = pop) %>%
-  mutate(prop_car = cooperative / (cooperative + private) * 100) %>%
-  select(district, year, prop_car) %>%
-  left_join(own_dat, by = c("district", "year")) %>%
-  rename(prop_trend = prop)
-
-# plot 1301
-
-# new proportion of cooperative housing; apply the parameter (% from capacity/reserves)
-new_prop <- prop_coop %>%
-  mutate(prop = prop_car * car_coop / 100 + prop_trend * (1 - car_coop / 100)) %>%
-  filter(year >= scen_begin) %>%
-  select(district, year, prop)
 
-# apply the new proportion
+# factorize and subset - form final output tibble
 new_pop_car <- pop_total %>%
-  group_by(district, year) %>%
-  summarize(pop = sum(pop),
-            .groups = "drop") %>%
-  left_join(new_prop, by = c("district", "year")) %>%
-  mutate(
-    pop_cooperative = pop * prop / 100,
-    pop_private = pop * (1 - prop / 100)
-  ) %>%
-  select(-c(pop, prop)) %>%
-  pivot_longer(
-    cols = c("pop_cooperative", "pop_private"),
-    names_prefix = "pop_",
-    names_to = "category", values_to = "car"
-  ) %>%
-  mutate(owner = if_else(category == "cooperative", uni_w[1], uni_w[2])) %>%
-  select(district, year, owner, car)
+  mutate(owner = if_else(owner == "cooperative housing", uni_w[1], uni_w[2])) %>%
+  select(district, year, owner, pop) %>%
+  mutate(car = pop) %>%
+  select(-pop)
 
 # combine: projects and capacity (with new prop of cooperative) -----------
 
-# combine
+# Combining all prepared data in one tibble
+# dyo with pop (current year), new, removed and car (from capacity)
 pro_car <- as_tibble(expand_grid(
   district = uni_d,
   year = date_end:scen_end,
@@ -246,17 +238,30 @@ project_reserves <- function(x, ...) {
 
 }
 
-# Check
-x <- filter(pro_car, (district == "Escher Wyss") & (owner == "private housing"))
-plot(x$year, x$car, type = "o")
-project_reserves(x)
-
 # consider projects and reserves (apply the function)
 pro_res_all <- pro_car %>%
   group_split(district, owner) %>%
   map(project_reserves) %>%
   bind_rows()
 
+# check: car vs. car/projects
+car_pop_y <- new_pop_car |> 
+  left_join(pro_res_all, by = c("district", "year", "owner")) |>
+  group_by(year) |>
+  summarize(car = sum(car),
+            pop = sum(pop), .groups = "drop") |>
+  mutate(diff = pop - car)
+
+tail(car_pop_y)
+
+ggplot(car_pop_y) +
+  geom_line(aes(x = year, y = diff))
+
+
+
+
+
+
 # apply the parameter of empty apartments ---------------------------------
 
 # parameter can be applied directly to the population
@@ -295,11 +300,13 @@ pop_all <- pop_fut_past %>%
 
 # plots 1305, 1306, 1307, 1308, 1309: compare projects and reserves
 
+
+
+
 # export the results ------------------------------------------------------
 
 # per district and ownership
-write_csv(pop_fut_past %>% arrange(district, year, owner),
-          paste0(exp_path, "/housing_model_population_dw"))
+write_csv(pop_fut_past %>% arrange(district, year, owner),paste0(exp_path, "/housing_model_population_dw.csv"))
 
 # per district
 ex_data_d <- arrange(pop_d, district, year)
@@ -314,4 +321,4 @@ write_csv(ex_data_all, paste0(exp_path, "/housing-model_population_all.csv"))
 cat_log(paste0(
   "housing model: ",
   capture.output(Sys.time() - t0)
-))
+))
\ No newline at end of file
diff --git a/1_Code/1400_Demography-Housing/1400_demography-housing.r b/1_Code/1400_Demography-Housing/1400_demography-housing.r
index d42cb018..de72bf88 100644
--- a/1_Code/1400_Demography-Housing/1400_demography-housing.r
+++ b/1_Code/1400_Demography-Housing/1400_demography-housing.r
@@ -151,7 +151,7 @@ out_pop_smooth <- NULL # WHY? for smoothing checks
 # loop over years
 for (iyear in future) {
 
-  # iyear <- 2022
+  # iyear <- 2024
 
   # population at the begin of the year = population at the end of the previous year
   if (iyear == min(future)) {
@@ -161,27 +161,27 @@ for (iyear in future) {
   }
 
   # births (fertility rate * women in the population)
-  bir_do <- fer %>%
+  bir_dao <- fer %>%
     filter(year == iyear) %>%
     left_join(popu, by = c("district", "age", "sex", "origin")) %>%
     replace_na(list(fer = 0, pop = 0)) %>%
-    mutate(bir = pop * fer / 100) %>%
-    group_by(district, origin) %>%
+    mutate(bir = pop * fer / 100) |> 
+    group_by(district, age, origin) %>%
     summarize(bir = sum(bir),
               .groups = "drop")
 
-  # sum(bir_do$bir)
+  # sum(bir_dao$bir)
 
   # births: origin changes (from mother to baby)
-  bir_do_new <- cha %>%
+  bir_dao_new <- cha %>%
     filter(year == iyear) %>%
     select(-year) %>%
-    right_join(bir_do, by = c("district", "origin")) %>%
+    right_join(bir_dao, by = c("district", "origin")) %>%
     mutate(
       change = bir * cha / 100,
       keep = bir - change
     ) %>%
-    select(district, origin, change, keep) %>%
+    select(district, age, origin, change, keep) %>%
     pivot_longer(
       cols = c("change", "keep"),
       names_to = "category", values_to = "bir"
@@ -191,11 +191,15 @@ for (iyear in future) {
       origin == uni_o[1] ~ uni_o[2],
       TRUE ~ uni_o[1]
     )) %>%
-    select(district, new_origin, bir) %>%
+    select(district, age, new_origin, bir) %>%
     rename(origin = new_origin) %>%
-    group_by(district, origin) %>%
+    group_by(district, age, origin) %>%
     summarize(bir = sum(bir),
-              .groups = "drop")
+              .groups = "drop") |> 
+    rename(age_mother = age)
+  
+  # sum(bir_dao_new$bir)  
+  
 
   # births: with variable 'sex'
 
@@ -205,7 +209,7 @@ for (iyear in future) {
 
   pro_male_value <- filter(pro_male, year == iyear)
 
-  bir <- bir_do_new %>%
+  bir_temp <- bir_dao_new %>%
     mutate(
       pro_male = pro_male_value$pro_male,
       male = bir * pro_male / 100,
@@ -220,13 +224,27 @@ for (iyear in future) {
       age = -1,
       sex = factor(sex, levels = uni_s)
     ) %>%
-    select(district, age, sex, origin, bir) %>%
-    arrange(district, sex, origin)
+    select(district, age, age_mother, sex, origin, bir)
 
-  # sum(bir_do$bir)
-  # sum(bir_do_new$bir)
-  # sum(bir$bir)
 
+  # birth with age of the mother (used for TFR)
+  bir_age_mother <- bir_temp |> 
+    select(district, age_mother, sex, origin, bir) |>     
+    arrange(district, age_mother, sex, origin)   
+    
+  # birth without the age of the mother (used in this model)
+  bir <- bir_temp |> 
+    select(-age_mother) |> 
+    group_by(district, age, sex, origin) %>%
+    summarize(bir = sum(bir),
+              .groups = "drop") |> 
+    arrange(district, sex, origin)      
+  
+  # sum(bir_dao$bir)
+  # sum(bir_dao_new$bir)
+  # sum(bir_age_mother$bir)
+  # sum(bir$bir)
+  
   # deaths (mortality rate * population)
   dea <- mor %>%
     filter(year == iyear) %>%
@@ -494,9 +512,9 @@ for (iyear in future) {
     select(-pop_end_year)
   
   # outputs: birth (separate, since no 'age' variable), with variable 'year'
-  out_bir <- bir %>%
+  out_bir <- bir_age_mother %>%
     mutate(year = iyear) %>%
-    select(district, year, sex, origin, bir) %>%
+    select(district, year, age_mother, sex, origin, bir) %>%
     bind_rows(out_bir)
 
   # outputs: demographic processes, with variable 'year'
@@ -563,7 +581,7 @@ out_bal %>%
 
 # births
 out_bir %>%
-  arrange(district, year, sex, origin) %>%
+  arrange(district, year, age_mother, sex, origin) %>%
   write_csv(paste0(out_path, "/births_future.csv"))
 
 # demographic processes
diff --git a/1_Code/1500_Model/1501_model_outputs.r b/1_Code/1500_Model/1501_model_outputs.r
index 3e203580..d3a35f1f 100644
--- a/1_Code/1500_Model/1501_model_outputs.r
+++ b/1_Code/1500_Model/1501_model_outputs.r
@@ -29,19 +29,20 @@ pop_past <- read_csv(pop_od) %>%
 
 # births
 bir_past <- read_csv(bir_od) %>%
-  rename(year = EreignisDatJahr, bir = AnzGebuWir) %>%
+  rename(year = EreignisDatJahr, age_mother = AlterVMutterCd, bir = AnzGebuWir) %>%
   left_join(look_dis, by = "QuarCd") %>%
   mutate(
     district = factor(distr, uni_d),
     sex = fact_if(SexCd, uni_s),
     origin = fact_if(HerkunftCd, uni_o)
   ) %>%
-  select(district, year, sex, origin, bir) %>%
-  group_by(district, year, sex, origin) %>%
+  select(district, year, age_mother, sex, origin, bir) %>%
+  group_by(district, year, age_mother, sex, origin) %>%
   summarize(bir = sum(bir),
             .groups = "drop") %>%
   mutate(scenario = uni_c[1])
 
+
 # deaths
 dea_past <- read_csv(dea_od) %>%
   rename(year = EreignisDatJahr, age = AlterVCd, dea = AnzSterWir) %>%
@@ -153,6 +154,7 @@ sce <- read_csv(sce_od) %>%
   summarize(pop = sum(pop),
             .groups = "drop")
 
+
 # data import: future (lower, middle, upper scenario) ---------------------
 
 # population
@@ -165,6 +167,16 @@ pop_middle <- read_csv(paste0(data_path, "5_Outputs/middle/population_future.csv
 pop_upper <- read_csv(paste0(data_path, "5_Outputs/upper/population_future.csv")) %>%
   mutate(scenario = uni_c[4])
 
+
+# population for birth versions
+pop_middle_birth_lower <- read_csv(paste0(data_path, "5_Outputs/middle_birth_lower/population_future.csv")) %>%
+  mutate(scenario = uni_cb[2])
+
+pop_middle_birth_upper <- read_csv(paste0(data_path, "5_Outputs/middle_birth_upper/population_future.csv")) %>%
+  mutate(scenario = uni_cb[4])
+
+
+
 # births
 bir_lower <- read_csv(paste0(data_path, "5_Outputs/lower/births_future.csv")) %>%
   mutate(scenario = uni_c[2])
@@ -175,6 +187,15 @@ bir_middle <- read_csv(paste0(data_path, "5_Outputs/middle/births_future.csv"))
 bir_upper <- read_csv(paste0(data_path, "5_Outputs/upper/births_future.csv")) %>%
   mutate(scenario = uni_c[4])
 
+
+# births for birth versions
+bir_middle_birth_lower <- read_csv(paste0(data_path, "5_Outputs/middle_birth_lower/births_future.csv")) %>%
+  mutate(scenario = uni_cb[2])
+
+bir_middle_birth_upper <- read_csv(paste0(data_path, "5_Outputs/middle_birth_upper/births_future.csv")) %>%
+  mutate(scenario = uni_cb[4])
+
+
 # demographic processes
 dem_lower <- read_csv(paste0(data_path, "5_Outputs/lower/demographic-processes_future.csv")) %>%
   mutate(scenario = uni_c[2])
@@ -203,9 +224,9 @@ nat_future <- nat_lower %>%
   bind_rows(nat_middle) %>%
   bind_rows(nat_upper)
 
-# # population --------------------------------------------------------------
-# 
-# past and future
+# population --------------------------------------------------------------
+
+# past and future (scenarios)
 pop <- pop_past %>%
   bind_rows(pop_lower) %>%
   bind_rows(pop_middle) %>%
@@ -216,6 +237,19 @@ pop <- pop_past %>%
     origin = factor(origin, levels = uni_o)
   )
 
+# past and future (birth versions)
+pop_birth_versions <- pop_past %>%
+  bind_rows(pop_middle_birth_lower) %>%
+  bind_rows(pop_middle) %>%
+  bind_rows(pop_middle_birth_upper) %>%
+  mutate(
+    district = factor(district, uni_d),
+    sex = factor(sex, levels = uni_s),
+    origin = factor(origin, levels = uni_o)
+  )
+
+
+
 # selected years (e.g. for population pyramids)
 y_sel1 <- c(date_end, scen_end_public)
 y_sel2 <- c(date_end, rev(seq(scen_end, scen_begin, by = -10)))
@@ -326,6 +360,26 @@ text_pop_dy_prep %>%
 
 # plot 1509
 
+# population (children) for the scenarios 
+pop_yac_children <- pop |> 
+  filter(age < age_5[3]) |> 
+  left_join(look_a5, by = "age") |> 
+  group_by(year, scenario, age_5) |> 
+    summarize(pop = sum_NA(pop), .groups = "drop")
+
+# plot 1509a
+
+# population (children) for the birth versions 
+pop_yav_children <- pop_birth_versions |> 
+  filter(age < age_5[3]) |> 
+  mutate(scenario = factor(scenario, levels = uni_cb)) |> 
+  left_join(look_a5, by = "age") |> 
+  group_by(year, scenario, age_5) |> 
+    summarize(pop = sum_NA(pop), .groups = "drop")
+
+# plot 1509b
+
+
 # population: new and previous scenarios ----------------------------------
 
 # total population
diff --git a/1_Code/1500_Model/1503_output_dwh.r b/1_Code/1500_Model/1503_output_dwh.r
index 3e14a0e2..1f116ebf 100644
--- a/1_Code/1500_Model/1503_output_dwh.r
+++ b/1_Code/1500_Model/1503_output_dwh.r
@@ -11,16 +11,6 @@ params <- init("middle")
 dwh_path <- paste0(data_path, "7_DWH/")
 dir_ex_create(dwh_path)
 
-# population data ---------------------------------------------------------
-
-# get list of model output files as input data for DWH
-files_output <- paste0(paste0(data_path, "5_Outputs/"),
-                       list.files(path = paste0(data_path, "5_Outputs/"), recursive = TRUE))
-
-# read birth data and adapt structure -------------------------------------
-
-dwh_path <- paste0(data_path, "7_DWH/")
-dir_ex_create(dwh_path)
 
 # population data ---------------------------------------------------------
 
@@ -35,7 +25,7 @@ bir_past <- read_csv(bir_od, lazy = FALSE) %>%
   left_join(look_dis, by = "QuarCd") %>%
   rename("Jahr" = EreignisDatJahr,
          "district" = distr) %>%
-  mutate(BasisSzenarienCd = basis_fact, # de facto value from past
+  mutate(BasisSzenarienCd = 1, # de facto value from past
          AlterVCd = 0) %>%
   group_by(Jahr,
            SexCd,
@@ -48,19 +38,37 @@ bir_past <- read_csv(bir_od, lazy = FALSE) %>%
 
 # scenario data
 read_files <- files_output[str_detect(files_output, "births_future")]
-stop_3(read_files)
+stop_5(read_files)
+
+# VersionArtCd (in all publication years)
+# 1: lower scenario
+# 2: middle scenario
+# 3: upper scenario
+
+# VersionArtCd (as of publication year 2024)
+# 4: lower birth version
+# 5: upper birth versions
 
 births <-
-  # lower scenario (VersionArtCd = 1)
+  # lower scenario
   read_csv(read_files[1], lazy = FALSE) %>%
   mutate(VersionArtCd = 1) %>%
   # middle scenario
   bind_rows(read_csv(read_files[2], lazy = FALSE) %>%
               mutate(VersionArtCd = 2)) %>%
   # upper scenario
-  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+  bind_rows(read_csv(read_files[5], lazy = FALSE) %>%
               mutate(VersionArtCd = 3)) %>%
-  mutate(BasisSzenarienCd = basis_scen,  # calculated scenario value
+  # lower birth version
+  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+              mutate(VersionArtCd = 4)) %>%  
+  # upper birth version
+  bind_rows(read_csv(read_files[4], lazy = FALSE) %>%
+              mutate(VersionArtCd = 5)) %>%  
+  # aggregate (without age of mother)
+  group_by(district, year, sex, origin, VersionArtCd) %>% 
+    summarize(bir = sum_NA(bir), groups. = "drop") %>%   
+  mutate(BasisSzenarienCd = 2,  # calculated scenario value
          AlterVCd = 0) %>%
   # get codes instead of text
   left_join(tibble(sex = levels(uni_s),
@@ -76,9 +84,14 @@ births <-
               mutate(VersionArtCd = 2)) %>%
   bind_rows(bir_past %>%
               mutate(VersionArtCd = 3)) %>%
+  bind_rows(bir_past %>%
+              mutate(VersionArtCd = 4)) %>%
+  bind_rows(bir_past %>%
+              mutate(VersionArtCd = 5)) %>%
   select(Jahr, BasisSzenarienCd, VersionArtCd, AlterVCd, SexCd,
          HerkunftCd, district, AnzGebuWir)
 
+
 # read population data and adapt structure --------------------------------
 
 # past data
@@ -86,7 +99,7 @@ pop_past <- read_csv(pop_od, lazy = FALSE) %>%
   left_join(look_dis, by = "QuarCd") %>%
   rename("Jahr" = StichtagDatJahr,
          "district" = distr) %>%
-  mutate(BasisSzenarienCd = basis_fact) %>%
+  mutate(BasisSzenarienCd = 1) %>%
   group_by(Jahr,
            AlterVCd,
            SexCd,
@@ -98,7 +111,7 @@ pop_past <- read_csv(pop_od, lazy = FALSE) %>%
 
 #scenario data
 read_files <- files_output[str_detect(files_output, "population_future")]
-stop_3(read_files)
+stop_5(read_files)
 
 pop <-
   # lower scenario
@@ -108,9 +121,15 @@ pop <-
   bind_rows(read_csv(read_files[2], lazy = FALSE) %>%
               mutate(VersionArtCd = 2)) %>%
   # upper scenario
-  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+  bind_rows(read_csv(read_files[5], lazy = FALSE) %>%
               mutate(VersionArtCd = 3)) %>%
-  mutate(BasisSzenarienCd = basis_scen) %>%
+  # lower birth version
+  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+              mutate(VersionArtCd = 4)) %>%  
+  # upper birth version
+  bind_rows(read_csv(read_files[4], lazy = FALSE) %>%
+              mutate(VersionArtCd = 5)) %>%    
+  mutate(BasisSzenarienCd = 2) %>%
   # get codes instead of text
   left_join(tibble(sex = levels(uni_s),
                    SexCd = as.numeric(labels(uni_s)))) %>%
@@ -126,6 +145,10 @@ pop <-
               mutate(VersionArtCd = 2)) %>%
   bind_rows(pop_past %>%
               mutate(VersionArtCd = 3)) %>%
+  bind_rows(pop_past %>%
+              mutate(VersionArtCd = 4)) %>%  
+  bind_rows(pop_past %>%
+              mutate(VersionArtCd = 5)) %>%  
   select(Jahr, BasisSzenarienCd, VersionArtCd, AlterVCd, SexCd, 
          HerkunftCd, district, AnzBestWir)
 
@@ -137,7 +160,7 @@ nat_past <- read_csv(nat_od, lazy = FALSE) %>%
   left_join(look_dis, by = "QuarCd") %>%
   rename("Jahr" = EreignisDatJahr,
          "district" = distr) %>%
-  mutate(BasisSzenarienCd = basis_fact) %>%
+  mutate(BasisSzenarienCd = 1) %>%
   group_by(Jahr,
            AlterVCd,
            SexCd,
@@ -149,7 +172,7 @@ nat_past <- read_csv(nat_od, lazy = FALSE) %>%
 
 # scenario data 
 read_files <- files_output[str_detect(files_output, "naturalization_future")]
-stop_3(read_files)
+stop_5(read_files)
 
 nat <-
   # lower scenario
@@ -159,9 +182,15 @@ nat <-
   bind_rows(read_csv(read_files[2], lazy = FALSE) %>%
               mutate(VersionArtCd = 2)) %>%
   # upper scenario
-  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+  bind_rows(read_csv(read_files[5], lazy = FALSE) %>%
               mutate(VersionArtCd = 3)) %>%
-  mutate(BasisSzenarienCd = basis_scen, 
+  # lower birth version
+  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+              mutate(VersionArtCd = 4)) %>%  
+  # upper birth version
+  bind_rows(read_csv(read_files[4], lazy = FALSE) %>%
+              mutate(VersionArtCd = 5)) %>%    
+  mutate(BasisSzenarienCd = 2, 
          HerkunftCd = 1) %>%
   # get codes instead of text
   left_join(tibble(sex = levels(uni_s),
@@ -176,6 +205,10 @@ nat <-
               mutate(VersionArtCd = 2)) %>%
   bind_rows(nat_past %>%
               mutate(VersionArtCd = 3)) %>%
+  bind_rows(nat_past %>%
+              mutate(VersionArtCd = 4)) %>%
+  bind_rows(nat_past %>%
+              mutate(VersionArtCd = 5)) %>%  
   select(Jahr, BasisSzenarienCd, VersionArtCd, AlterVCd, SexCd, 
          HerkunftCd, district, AnzEinbWir)
 
@@ -193,7 +226,7 @@ demo_past <- read_csv(dea_od, lazy = FALSE) %>%
   left_join(look_dis, by = "QuarCd") %>%
   rename("Jahr" = EreignisDatJahr,
          "district" = distr) %>%
-  mutate(BasisSzenarienCd = basis_fact) %>%
+  mutate(BasisSzenarienCd = 1) %>%
   group_by(Jahr,
            AlterVCd,
            SexCd,
@@ -207,7 +240,7 @@ demo_past <- read_csv(dea_od, lazy = FALSE) %>%
 
 # scenario data
 read_files <- files_output[str_detect(files_output, "demographic")]
-stop_3(read_files)
+stop_5(read_files)
 
 demo <-
   # lower scenario
@@ -217,9 +250,15 @@ demo <-
   bind_rows(read_csv(read_files[2], lazy = FALSE) %>%
               mutate(VersionArtCd = 2)) %>%
   # upper scenario
-  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+  bind_rows(read_csv(read_files[5], lazy = FALSE) %>%
               mutate(VersionArtCd = 3)) %>%
-  mutate(BasisSzenarienCd = basis_scen) %>%
+  # lower birth version
+  bind_rows(read_csv(read_files[3], lazy = FALSE) %>%
+              mutate(VersionArtCd = 4)) %>%  
+  # upper birth version
+  bind_rows(read_csv(read_files[4], lazy = FALSE) %>%
+              mutate(VersionArtCd = 5)) %>%    
+  mutate(BasisSzenarienCd = 2) %>%
   # get codes instead of text
   left_join(tibble(sex = levels(uni_s),
                    SexCd = as.numeric(labels(uni_s)))) %>%
@@ -237,6 +276,10 @@ demo <-
               mutate(VersionArtCd = 2)) %>%
   bind_rows(demo_past %>%
               mutate(VersionArtCd = 3)) %>%
+  bind_rows(demo_past %>%
+              mutate(VersionArtCd = 4)) %>%  
+  bind_rows(demo_past %>%
+              mutate(VersionArtCd = 5)) %>%  
   select(Jahr, BasisSzenarienCd, VersionArtCd, AlterVCd, SexCd, 
          HerkunftCd, district, AnzZuzuWir, AnzWezuWir, AnzSterWir)
 
@@ -354,38 +397,51 @@ files_rate <- paste0(paste0(data_path, "4_Rates/"),
 
 # consumption rate
 read_files <- files_rate[str_detect(files_rate, "living-space_future")]
-stop_3(read_files)
+stop_5(read_files)
 
 consumption <- read_csv(read_files[1]) %>%
   mutate(VersionArtCd = 1) %>%
   add_row(read_csv(read_files[2]) %>%
             mutate(VersionArtCd = 2)) %>%
-  add_row(read_csv(read_files[3]) %>%
+  add_row(read_csv(read_files[5]) %>%
             mutate(VersionArtCd = 3)) %>%
+  add_row(read_csv(read_files[3]) %>%
+            mutate(VersionArtCd = 4)) %>%  
+  add_row(read_csv(read_files[4]) %>%
+            mutate(VersionArtCd = 5)) %>%    
   rename(WohnungsflProPers = spa_dyw)
 
 # occupancy rate 
 read_files <- files_rate[str_detect(files_rate, "allocation_future")]
-stop_3(read_files)
+stop_5(read_files)
 
 occupancy <- read_csv(read_files[1]) %>%
   mutate(VersionArtCd = 1) %>%
   add_row(read_csv(read_files[2]) %>%
             mutate(VersionArtCd = 2)) %>%
-  add_row(read_csv(read_files[3]) %>%
+  add_row(read_csv(read_files[5]) %>%
             mutate(VersionArtCd = 3)) %>%
+  add_row(read_csv(read_files[3]) %>%
+            mutate(VersionArtCd = 4)) %>%  
+  add_row(read_csv(read_files[4]) %>%
+            mutate(VersionArtCd = 5)) %>%    
   rename(PersProWhg = aca_dyw)
 
 # population data (needed for calculation of area and apartments)
 read_files <- files_rate[str_detect(files_rate, "housing_model_population_dw")]
-stop_3(read_files)
+stop_5(read_files)
 
 pop_dw <- read_csv(read_files[1]) %>%
   mutate(VersionArtCd = 1) %>%
   add_row(read_csv(read_files[2]) %>%
             mutate(VersionArtCd = 2)) %>%
-  add_row(read_csv(read_files[3]) %>%
+  add_row(read_csv(read_files[5]) %>%
             mutate(VersionArtCd = 3))
+  add_row(read_csv(read_files[3]) %>%
+            mutate(VersionArtCd = 4))
+  add_row(read_csv(read_files[4]) %>%
+            mutate(VersionArtCd = 5))
+  
 
 # area data
 join_cond <- c("year", "VersionArtCd", "district", "owner")
@@ -402,29 +458,21 @@ apartments <- occupancy %>%
 consumption %>%
   sszplot(aes_x = "year", aes_y = "WohnungsflProPers", aes_col = "VersionArtCd", aes_ltyp = "owner",
           labs_x = "year", labs_y = "m2/person", labs_col = "Scenario",
-          quotes = quote(scale_color_manual(labels = c("lower", "middle", "upper"),
-                                            values = c("blue", "green", "red"))),
           wrap = "district")
 
 occupancy %>%
   sszplot(aes_x = "year", aes_y = "PersProWhg ", aes_col = "VersionArtCd", aes_ltyp = "owner",
           labs_x = "year", labs_y = "persons/apartment",
-          quotes = quote(scale_color_manual(labels = c("lower", "middle", "upper"),
-                                            values = c("blue", "green", "red"))),
           wrap = "district")
 
 area %>%
   sszplot(aes_x = "year", aes_y = "BruttoGeschFlaeche ", aes_col = "VersionArtCd", aes_ltyp = "owner",
           labs_x = "year", labs_y = "area (ha)",
-          quotes = quote(scale_color_manual(labels = c("lower", "middle", "upper"),
-                                            values = c("blue", "green", "red"))),
           wrap = "district")
 
 apartments %>%
   sszplot(aes_x = "year", aes_y = "AnzWhgStat ", aes_col = "VersionArtCd", aes_ltyp = "owner",
           labs_x = "year", labs_y = "apartments",
-          quotes = quote(scale_color_manual(labels = c("lower", "middle", "upper"),
-                                            values = c("blue", "green", "red"))),
           wrap = "district")
 
 # combine datasets and write output
@@ -433,7 +481,7 @@ area %>%
   left_join(look_reg, by = "district") %>%
   left_join(look_own, by = "owner") %>%
   rename(Jahr = year) %>%
-  mutate(BasisSzenarienCd = if_else(Jahr < scen_begin, basis_fact, basis_scen), # calculated scenario value
+  mutate(BasisSzenarienCd = if_else(Jahr < scen_begin, 1, 2), # calculated scenario value
          PublJahr = scen_begin,
          QuarCd = distnum)  %>%
   mutate(BruttoGeschFlaeche = round(BruttoGeschFlaeche, round_rate),
diff --git a/2_Data/3_Parameter/parameter.csv b/2_Data/3_Parameter/parameter.csv
index a87dd3bf..77ba07a6 100644
--- a/2_Data/3_Parameter/parameter.csv
+++ b/2_Data/3_Parameter/parameter.csv
@@ -1,202 +1,196 @@
-category;parameter;parameter_old;lower;middle;upper;unit;relevance;description
-time;date_start;daten.beginn;1993;1993;1993;year;no parameter (strictly speaking);begin of data
-time;date_end;daten.ende;2022;2022;2022;year;no parameter (strictly speaking);end of data
-time;scen_begin;szen.beginn;2023;2023;2023;year;no parameter (strictly speaking);future: begin
-time;scen_end;szen.ende;2050;2050;2050;year;no parameter (strictly speaking);future: end
-time;scen_end_public;no parameter in previous model;2045;2045;2045;year;no parameter (strictly speaking);future: end (in publication)
-time;bir_base_begin;geb.basis.beginn;2010;2010;2010;year;medium;base period (in the past) for birth rate, begin
-time;bir_base_end;geb.basis.ende;2022;2022;2022;year;medium;base period (in the past) for birth rate, end
-time;bir_cha_base_begin;no parameter in previous model;2007;2007;2007;year;low;base period (in the past) for origin change (mother-baby), begin
-time;bir_cha_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (in the past) for origin change (mother-baby), end
-time;bir_sex_ratio_begin;no parameter in previous model;1993;1993;1993;year;low;base period (in the past) for sex ratio calculation, begin
-time;bir_sex_ratio_end;no parameter in previous model;2022;2022;2022;year;low;base period (in the past) for sex ratio calculation, begin
-time;dea_fso_date_start;no parameter in previous model;1993;1993;1993;year;low;fso mortality data of the past: begin
-time;dea_fso_date_end;no parameter in previous model;2021;2021;2021;year;low;fso mortality data of the past: end
-time;dea_base_begin;tod.basis.beginn;2012;2012;2012;year;low;base period (in the past) for death rate, begin (data should be available for both Zurich and Switzerland)
-time;dea_base_end;tod.basis.ende;2021;2021;2021;year;low;base period (in the past) for death rate, end (data should be available for both Zurich and Switzerland)
-time;ims_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for immigration* rate, begin
-time;ims_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for immigration* rate, end
-time;ims_so_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of sex and origin in immigration*, begin
-time;ims_so_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of sex and origin in immigration*, end
-time;ims_age_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of age in immigration*, begin
-time;ims_age_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of age in immigration*, end
-time;ems_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for emigration* rate, begin
-time;ems_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for emigration* rate, end
-time;ems_so_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of sex and origin in emigration*, begin
-time;ems_so_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of sex and origin in emigration*, end
-time;ems_age_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of age in emigration*, begin
-time;ems_age_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of age in emigration*, end
-time;rei_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for relocation based on immigration* (proportion), begin
-time;rei_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for relocation based on immigration* (proportion), end
-time;ree_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for relocation based on emigration* (proportion), begin
-time;ree_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for relocation based on emigration* (proportion), end
-time;nat_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for naturalization, begin
-time;nat_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for naturalization, end
-time;spa_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for living space, begin
-time;spa_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for living space, end
-time;spa_base_begin_52p;no parameter in previous model;2015;2015;2015;year;low;"base period (not the future) for living space, begin, district 'Escher Wyss' (district ID = 52; since empty apartments increased the mean living space values; before 2015)"
-time;aca_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for allocation, begin
-time;aca_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for allocation, end
-time;aca_base_begin_52p;no parameter in previous model;2015;2015;2015;year;low;"base period (not the future) for allocation, begin, district 'Escher Wyss' (district ID = 52; since empty apartments decreased the mean allocation values; before 2015)"
-time;pro_begin;map.wohnbau.beginn;2023;2023;2023;year;low;"first year (of consolidated project list; reasonable: first scenario year)"
-time;pro_end;map.wohnbau.ende;2035;2035;2035;year;low;last year (of consolidated project list)
-time;own_base_begin;no parameter in previous model;2015;2015;2015;year;low;base period (not the future) for ownership calculation, begin
-time;own_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for ownership calculation, end
-age;age_min;no parameter in previous model;0;0;0;years (age);low;minimum age in the model 
-age;age_max;no parameter in previous model;120;120;120;years (age);low;maximum age in the model (should be clearly higher than maximum age in the population)
-general;round_rate;no parameter in previous model;4;4;4;digits (rate in percent per year);no parameter;rounding of rates (digits, rate in percent per year)
-general;round_prop;no parameter in previous model;4;4;4;digits (proportion in percent);no parameter;rounding of proportion (digits, proportion in percent)
-general;round_area;no parameter in previous model;4;4;4;digits (area);no parameter;rounding of area (e.g. ha in the capacity and reserve module)
-general;round_people;no parameter in previous model;0;0;0;digits (people);no parameter;rounding of people
-general;round_aca;no parameter in previous model;4;4;4;digits (people per apartment);no parameter;rounding of allocation (people per apartment)
-general;round_people_scen;no parameter in previous model;100;100;100;people (not digits);no parameter;"rounding of people (for future values; scenarios)"
-general;round_prop_scen;no parameter in previous model;0;0;0;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios)"
-general;round_prop_scen_a;no parameter in previous model;1;1;1;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios): comparison to previous scenarios by age"
-birth;bir_age_begin;geb.alter.beginn;15;15;15;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): begin
-birth;bir_age_end;geb.alter.ende;49;49;49;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): end
-birth;bir_thres_origin;geb.anz.rate.heimat;100;100;100;persons;low;"age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the rate based on origin (but not district) is used.
+category;parameter;parameter_old;lower;middle;middle_birth_lower;middle_birth_upper;upper;unit;relevance;description
+time;date_start;daten.beginn;1993;1993;1993;1993;1993;year;no parameter (strictly speaking);begin of data
+time;date_end;daten.ende;2023;2023;2023;2023;2023;year;no parameter (strictly speaking);end of data
+time;scen_begin;szen.beginn;2024;2024;2024;2024;2024;year;no parameter (strictly speaking);future: begin
+time;scen_end;szen.ende;2050;2050;2050;2050;2050;year;no parameter (strictly speaking);future: end
+time;scen_end_public;no parameter in previous model;2045;2045;2045;2045;2045;year;no parameter (strictly speaking);future: end (in publication)
+time;bir_base_begin;geb.basis.beginn;2022;2022;2022;2022;2022;year;medium;base period (in the past) for birth rate, begin
+time;bir_base_end;geb.basis.ende;2023;2023;2023;2023;2023;year;medium;base period (in the past) for birth rate, end
+time;bir_cha_base_begin;no parameter in previous model;2007;2007;2007;2007;2007;year;low;base period (in the past) for origin change (mother-baby), begin
+time;bir_cha_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (in the past) for origin change (mother-baby), end
+time;bir_sex_ratio_begin;no parameter in previous model;1993;1993;1993;1993;1993;year;low;base period (in the past) for sex ratio calculation, begin
+time;bir_sex_ratio_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (in the past) for sex ratio calculation, begin
+time;dea_fso_date_start;no parameter in previous model;1993;1993;1993;1993;1993;year;low;fso mortality data of the past: begin
+time;dea_fso_date_end;no parameter in previous model;2022;2022;2022;2022;2022;year;low;fso mortality data of the past: end
+time;dea_base_begin;tod.basis.beginn;2013;2013;2013;2013;2013;year;low;base period (in the past) for death rate, begin (data should be available for both Zurich and Switzerland)
+time;dea_base_end;tod.basis.ende;2022;2022;2022;2022;2022;year;low;base period (in the past) for death rate, end (data should be available for both Zurich and Switzerland)
+time;ims_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for immigration* rate, begin
+time;ims_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for immigration* rate, end
+time;ims_so_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for proportion of sex and origin in immigration*, begin
+time;ims_so_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future)  for proportion of sex and origin in immigration*, end
+time;ims_age_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for proportion of age in immigration*, begin
+time;ims_age_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future)  for proportion of age in immigration*, end
+time;ems_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for emigration* rate, begin
+time;ems_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for emigration* rate, end
+time;ems_so_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for proportion of sex and origin in emigration*, begin
+time;ems_so_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future)  for proportion of sex and origin in emigration*, end
+time;ems_age_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for proportion of age in emigration*, begin
+time;ems_age_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future)  for proportion of age in emigration*, end
+time;rei_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for relocation based on immigration* (proportion), begin
+time;rei_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for relocation based on immigration* (proportion), end
+time;ree_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for relocation based on emigration* (proportion), begin
+time;ree_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for relocation based on emigration* (proportion), end
+time;nat_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for naturalization, begin
+time;nat_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for naturalization, end
+time;spa_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for living space, begin
+time;spa_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for living space, end
+time;spa_base_begin_52p;no parameter in previous model;2015;2015;2015;2015;2015;year;low;"base period (not the future) for living space, begin, district 'Escher Wyss' (district ID = 52; since empty apartments increased the mean living space values; before 2015)"
+time;aca_base_begin;no parameter in previous model;2014;2014;2014;2014;2014;year;low;base period (not the future) for allocation, begin
+time;aca_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for allocation, end
+time;aca_base_begin_52p;no parameter in previous model;2015;2015;2015;2015;2015;year;low;"base period (not the future) for allocation, begin, district 'Escher Wyss' (district ID = 52; since empty apartments decreased the mean allocation values; before 2015)"
+time;pro_begin;map.wohnbau.beginn;2024;2024;2024;2024;2024;year;low;"first year (of consolidated project list; reasonable: first scenario year)"
+time;pro_end;map.wohnbau.ende;2035;2035;2035;2035;2035;year;low;last year (of consolidated project list)
+time;own_base_begin;no parameter in previous model;2015;2015;2015;2015;2015;year;low;base period (not the future) for ownership calculation, begin
+time;own_base_end;no parameter in previous model;2023;2023;2023;2023;2023;year;low;base period (not the future) for ownership calculation, end
+age;age_min;no parameter in previous model;0;0;0;0;0;years (age);low;minimum age in the model 
+age;age_max;no parameter in previous model;120;120;120;120;120;years (age);low;maximum age in the model (should be clearly higher than maximum age in the population)
+general;round_rate;no parameter in previous model;4;4;4;4;4;digits (rate in percent per year);no parameter;rounding of rates (digits, rate in percent per year)
+general;round_prop;no parameter in previous model;4;4;4;4;4;digits (proportion in percent);no parameter;rounding of proportion (digits, proportion in percent)
+general;round_area;no parameter in previous model;4;4;4;4;4;digits (area);no parameter;rounding of area (e.g. ha in the capacity and reserve module)
+general;round_people;no parameter in previous model;0;0;0;0;0;digits (people);no parameter;rounding of people
+general;round_aca;no parameter in previous model;4;4;4;4;4;digits (people per apartment);no parameter;rounding of allocation (people per apartment)
+general;round_people_scen;no parameter in previous model;100;100;100;100;100;people (not digits);no parameter;"rounding of people (for future values; scenarios)"
+general;round_prop_scen;no parameter in previous model;0;0;0;0;0;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios)"
+general;round_prop_scen_a;no parameter in previous model;1;1;1;1;1;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios): comparison to previous scenarios by age"
+birth;bir_age_begin;geb.alter.beginn;15;15;15;15;15;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): begin
+birth;bir_age_end;geb.alter.ende;49;49;49;49;49;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): end
+birth;bir_thres_origin;geb.anz.rate.heimat;100;100;100;100;100;persons;low;"age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the rate based on origin (but not district) is used.
 "
-birth;bir_thres_overall;geb.anz.rate.stadt;50;50;50;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the overall rate is used (e.g not the rate based on origin and district).
-birth;bir_thres_const;geb.anz.rate.const;25;25;25;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then a constant rate is used (e.g not the rate based on origin and district). The constant rate is set by the parameter bir_thres_value.
-birth;bir_thres_value;geb.wert.rate.const;0;0;0;birth per woman and year in %;low;birth rate: if there are less women than a certain threshold (bir_thres_const)  a constant fertility rate is used. The values is chosen with this parameter.
-birth;bir_fer_span;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
-birth;bir_prop_trend;geb.anteil.trend;20;20;20;percent;medium;"trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_thres_percent;geb.grenze.prozent;20;20;20;percent;low;"It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
-birth;bir_window_thres;geb.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
-birth;bir_lower_thres;no parameter in previous model;0;0;0;percent per year;low;lower threshold for the rate (e.g. the birth rate should not be negative), NA if there is no lower threshold
-birth;bir_upper_thres;no parameter in previous model;NA;NA;NA;percent per year;low;upper threshold for the rate, NA if there is no upper threshold
-birth;bir_plot_lim;no parameter in previous model;30;30;30;percent per year;no parameter;"the fertility rates of the past are compared with the predictions; the y-axis is limited (since the fertility rate of the past can be very large due to low population)"
-birth;bir_fer_span_pred;no parameter in previous model;0.15;0.15;0.15;no unit;low;"after the prediction: proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
-birth;bir_cha_trend;geb.heimat.anteil.trend;0;0;0;percent;low;"origin change (mother-baby): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_cha_prop_trend;no parameter in previous model;20;20;20;percent;medium;"origin change (mother-baby): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_cha_thres_percent;no parameter in previous model;20;20;20;percent;low;"origin change (mother-baby): It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
-birth;bir_cha_window_thres;no parameter in previous model;13;13;13;years;low;origin change (mother-baby): filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
-birth;bir_cha_lower_thres;no parameter in previous model;0;0;0;percent;low;origin change (mother-baby): lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-birth;bir_cha_upper_thres;no parameter in previous model;100;100;100;percent;low;origin change (mother-baby): upper threshold for the rate (here: not more than 100 %), NA if there is no lower threshold
-death;dea_lower;tod.jung.alter.ende.schweiz, tod.jung.alter.ende.ausland;30;30;30;years (age);low;"lower age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
-death;dea_upper;tod.alt.alter.beginn.schweiz, tod.alt.alter.beginn.ausland;99;99;99;years (age);low;"upper age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
-death;dea_radix;no parameter in previous model;100000;100000;100000;persons;no;radix (starting population) for life expectancy calcuation (no effect on result)
-death;dea_age_at;no parameter in previous model;0;0;0;years (age);no;"life expectancy at certain age (usually at birth; i.e. age zero)"
-death;dea_age_max_le;tod.alt.alter.ende;120;120;120;years (age);low;maximum age for the estimation of the life expectancy
-death;dea_fso_cat_past;no parameter in previous model;2;2;2;no unit;no parameter;Category in the FSO data: (smoothed) data of the past
-death;dea_fso_cat_future;no parameter in previous model;3;3;3;no unit;no parameter;Category in the FSO data: data of the future
-death;dea_qx_NA_le;no parameter in previous model;0.8;0.8;0.8;per year;low;if there is no one at a certain age in the population (e.g. no 96 year old men), then qx (probability to die between age x and x+1) is NA. However, a value is need to multiply the subsequent survival probabilities
-death;dea_mor_span;no parameter in previous model;0.2;0.2;0.2;no unit;low;"proportion of data points used in the LOESS regressions of mortality by age; groups: sex, region (City of Zurich, Switzerland)"
-immigration*;ims_rate_prop_trend;zuz.rate.anteil.trend;20;20;20;percent;low;"immigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_rate_thres_percent;zuz.rate.grenze.prozent;20;20;20;percent;low;"It is not realistic that the immigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-immigration*;ims_rate_window_thres;zuz.rate.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future immigration* rates: imm_rate_thres_percent).
-immigration*;ims_rate_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-immigration*;ims_so_prop_trend;zuz.ghvert.anteil.trend;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_so_thres_percent;zuz.ghvert.grenze.prozent;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+birth;bir_thres_overall;geb.anz.rate.stadt;50;50;50;50;50;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the overall rate is used (e.g not the rate based on origin and district).
+birth;bir_thres_const;geb.anz.rate.const;25;25;25;25;25;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then a constant rate is used (e.g not the rate based on origin and district). The constant rate is set by the parameter bir_thres_value.
+birth;bir_thres_value;geb.wert.rate.const;0;0;0;0;0;birth per woman and year in %;low;birth rate: if there are less women than a certain threshold (bir_thres_const)  a constant fertility rate is used. The values is chosen with this parameter.
+birth;bir_fer_span;no parameter in previous model;0.3;0.3;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
+birth;bir_prop_trend;geb.anteil.trend;0;0;0;0;0;percent;medium;"trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+birth;bir_thres_percent;geb.grenze.prozent;20;20;20;20;20;percent;low;"It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
+birth;bir_window_thres;geb.window.grenz;13;13;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
+birth;bir_lower_thres;no parameter in previous model;0;0;0;0;0;percent per year;low;lower threshold for the rate (e.g. the birth rate should not be negative), NA if there is no lower threshold
+birth;bir_upper_thres;no parameter in previous model;NA;NA;NA;NA;NA;percent per year;low;upper threshold for the rate, NA if there is no upper threshold
+birth;bir_plot_lim;no parameter in previous model;30;30;30;30;30;percent per year;no parameter;"the fertility rates of the past are compared with the predictions; the y-axis is limited (since the fertility rate of the past can be very large due to low population)"
+birth;bir_fer_span_pred;no parameter in previous model;0.15;0.15;0.15;0.15;0.15;no unit;low;"after the prediction: proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
+birth;bir_mult_begin;no parameter in previous model;0.97;0.97;0.92;1.02;0.97;no unit;medium;"birth rate multiplier at the begin of the prediction period; the future birth rates are multiplied by this factor"
+birth;bir_mult_end;no parameter in previous model;0.90;0.90;0.70;1.10;0.90;no unit;medium;"birth rate multiplier at the end of the prediction period; the future birth rates are multiplied by this factor "
+birth;bir_mult_exp;no parameter in previous model;0.10;0.10;0.10;0.10;0.10;no unit;medium;exponent of the multiplier/year relation (linear model): multiplier ~ (year - scen-begin + 1) ^ bir_mult_exp
+birth;bir_cha_prop_trend;no parameter in previous model;20;20;20;20;20;percent;medium;"origin change (mother-baby): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+birth;bir_cha_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"origin change (mother-baby): It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
+birth;bir_cha_window_thres;no parameter in previous model;13;13;13;13;13;years;low;origin change (mother-baby): filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
+birth;bir_cha_lower_thres;no parameter in previous model;0;0;0;0;0;percent;low;origin change (mother-baby): lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
+birth;bir_cha_upper_thres;no parameter in previous model;100;100;100;100;100;percent;low;origin change (mother-baby): upper threshold for the rate (here: not more than 100 %), NA if there is no lower threshold
+death;dea_lower;tod.jung.alter.ende.schweiz, tod.jung.alter.ende.ausland;30;30;30;30;30;years (age);low;"lower age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
+death;dea_upper;tod.alt.alter.beginn.schweiz, tod.alt.alter.beginn.ausland;99;99;99;99;99;years (age);low;"upper age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
+death;dea_radix;no parameter in previous model;100000;100000;100000;100000;100000;persons;no;radix (starting population) for life expectancy calcuation (no effect on result)
+death;dea_age_at;no parameter in previous model;0;0;0;0;0;years (age);no;"life expectancy at certain age (usually at birth; i.e. age zero)"
+death;dea_age_max_le;tod.alt.alter.ende;120;120;120;120;120;years (age);low;maximum age for the estimation of the life expectancy
+death;dea_fso_cat_past;no parameter in previous model;2;2;2;2;2;no unit;no parameter;Category in the FSO data: (smoothed) data of the past
+death;dea_fso_cat_future;no parameter in previous model;3;3;3;3;3;no unit;no parameter;Category in the FSO data: data of the future
+death;dea_qx_NA_le;no parameter in previous model;0.8;0.8;0.8;0.8;0.8;per year;low;if there is no one at a certain age in the population (e.g. no 96 year old men), then qx (probability to die between age x and x+1) is NA. However, a value is need to multiply the subsequent survival probabilities
+death;dea_mor_span;no parameter in previous model;0.2;0.2;0.2;0.2;0.2;no unit;low;"proportion of data points used in the LOESS regressions of mortality by age; groups: sex, region (City of Zurich, Switzerland)"
+immigration*;ims_rate_prop_trend;zuz.rate.anteil.trend;20;20;20;20;20;percent;low;"immigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+immigration*;ims_rate_thres_percent;zuz.rate.grenze.prozent;20;20;20;20;20;percent;low;"It is not realistic that the immigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
+immigration*;ims_rate_window_thres;zuz.rate.window.grenz;13;13;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future immigration* rates: imm_rate_thres_percent).
+immigration*;ims_rate_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
+immigration*;ims_so_prop_trend;zuz.ghvert.anteil.trend;20;20;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+immigration*;ims_so_thres_percent;zuz.ghvert.grenze.prozent;20;20;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
 immigration*;ims_so_window_thres;"zuz.ghvert.window.grenz
-";13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
-immigration*;ims_so_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-immigration*;ims_span_y;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the loess regressions of proportion by year; groups: district, age, sex, origin (immigration* by district, year, age, sex, origin)"
-immigration*;ims_span_a;no parameter in previous model;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
-immigration*;ims_age_prop_trend;no parameter in previous model;20;20;20;percent;low;"age distribution of immigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_age_thres_percent;no parameter in previous model;20;20;20;percent;low;"age distribution of immigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-immigration*;ims_age_window_thres;no parameter in previous model;13;13;13;years;low;age distribution of immigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
-immigration*;ims_age_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;age distribution of immigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-emigration*;ems_rate_prop_trend;weg.rate.anteil.trend;20;20;20;percent;low;"emigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_rate_thres_percent;weg.rate.grenze.prozent;20;20;20;percent;low;"It is not realistic that the emigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-emigration*;ems_rate_window_thres;weg.rate.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future emigration* rates: imm_rate_thres_percent).
-emigration*;ems_rate_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-emigration*;ems_so_prop_trend;weg.ghvert.anteil.trend;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_so_thres_percent;weg.ghvert.grenze.prozent;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+";13;13;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
+immigration*;ims_so_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+immigration*;ims_span_y;no parameter in previous model;0.3;0.3;0.3;0.3;0.3;no unit;low;"proportion of data points used in the loess regressions of proportion by year; groups: district, age, sex, origin (immigration* by district, year, age, sex, origin)"
+immigration*;ims_span_a;no parameter in previous model;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
+immigration*;ims_age_prop_trend;no parameter in previous model;20;20;20;20;20;percent;low;"age distribution of immigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+immigration*;ims_age_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"age distribution of immigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+immigration*;ims_age_window_thres;no parameter in previous model;13;13;13;13;13;years;low;age distribution of immigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
+immigration*;ims_age_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;age distribution of immigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+emigration*;ems_rate_prop_trend;weg.rate.anteil.trend;20;20;20;20;20;percent;low;"emigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+emigration*;ems_rate_thres_percent;weg.rate.grenze.prozent;20;20;20;20;20;percent;low;"It is not realistic that the emigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
+emigration*;ems_rate_window_thres;weg.rate.window.grenz;13;13;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future emigration* rates: imm_rate_thres_percent).
+emigration*;ems_rate_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
+emigration*;ems_so_prop_trend;weg.ghvert.anteil.trend;20;20;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+emigration*;ems_so_thres_percent;weg.ghvert.grenze.prozent;20;20;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
 emigration*;ems_so_window_thres;"weg.ghvert.window.grenz
-";13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
-emigration*;ems_so_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-emigration*;ems_span_y;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of proportion by year; groups: district, age, sex, origin (emigration* by district, year, age, sex, origin)"
-emigration*;ems_span_a;no parameter in previous model;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
-emigration*;ems_age_prop_trend;no parameter in previous model;20;20;20;percent;low;"age distribution of emigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_age_thres_percent;no parameter in previous model;20;20;20;percent;low;"age distribution of emigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-emigration*;ems_age_window_thres;no parameter in previous model;13;13;13;years;low;age distribution of emigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
-emigration*;ems_age_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;age distribution of emigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and immigration*;rei_age_max;umz.alter.max;70;70;70;years;low;as of this age: one single proportion (relocation on immigration*)
-relocation and immigration*;rei_ims_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, year, origin"
-relocation and immigration*;rei_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
-relocation and immigration*;rei_ims_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, origin"
-relocation and immigration*;rei_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
-relocation and immigration*;rei_ims_thres_y;similar to umz.anz.rate.stadt;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
-relocation and immigration*;rei_prop_span;similar to umz.window.alter;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
-relocation and immigration*;rei_prop_trend;umz.anteil.trend;20;20;20;percent;low;"relocation proportion on immigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-relocation and immigration*;rei_thres_percent;no parameter in previous model;20;20;20;percent;low;"relocation proportion on immigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-relocation and immigration*;rei_window_thres;no parameter in previous model;13;13;13;years;low;relocation proportion on immigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
-relocation and immigration*;rei_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and immigration*;rei_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-relocation and immigration*;rei_pred_span;similar to umz.window.alter;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions of the prediction (proportion) by age; groups: district, year, origin"
-relocation and emigration*;ree_age_max;umz.alter.max;70;70;70;years;low;as of this age: one single proportion (relocation on emigration*)
-relocation and emigration*;ree_ems_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, year, origin"
-relocation and emigration*;ree_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
-relocation and emigration*;ree_ems_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, origin"
-relocation and emigration*;ree_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
-relocation and emigration*;ree_ems_thres_y;similar to umz.anz.rate.heimat;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
-relocation and emigration*;ree_prop_span;similar to umz.window.alter;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
-relocation and emigration*;ree_prop_trend;umz.anteil.trend;20;20;20;percent;low;"relocation proportion on emigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-relocation and emigration*;ree_thres_percent;no parameter in previous model;20;20;20;percent;low;"relocation proportion on emigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-relocation and emigration*;ree_window_thres;no parameter in previous model;13;13;13;years;low;relocation proportion on emigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
-relocation and emigration*;ree_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and emigration*;ree_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-relocation and emigration*;ree_pred_span;similar to umz.window.alter;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions the prediction (proportion) by age; groups: district, year, origin"
-naturalization;nat_pop_span_das;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: district, sex"
-naturalization;nat_nat_span_das;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: district, sex"
-naturalization;nat_pop_thres;similar to brw.rate.anz.null;0.5;0.5;0.5;persons;low;if less persons in the (smoothed) foreign population than this threshold: then the naturalization rate is set to nat_pop_value
-naturalization;nat_pop_value;no parameter in previous model;0;0;0;percent per year;low;value of the naturalization rate, if the foreign population is below nat_pop_thres
-naturalization;nat_rate_span_das;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, sex"
-naturalization;nat_pop_span_ya;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: year (base years only)"
-naturalization;nat_nat_span_ya;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: year (base years only)"
-naturalization;nat_rate_span_ya;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: year (base years only)"
-naturalization;nat_prop_trend;brw.anteil.trend;20;20;20;percent;low;"naturalization: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-naturalization;nat_thres_percent;no parameter in previous model;20;20;20;percent;low;"naturalization: It is not realistic that the rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-naturalization;nat_window_thres;no parameter in previous model;13;13;13;years;low;naturalization: filter over years (to smooth a potential break in the curve due to range of future proportions
-naturalization;nat_lower_thres;no parameter in previous model;0;0;0;percent;low;naturalization: lower threshold for the proportions (here: the naturalization rates should not be negative), NA if there is no lower threshold
-naturalization;nat_pop_span_a;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; no groups (base years only)"
-naturalization;nat_nat_span_a;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; no groups (base years only)"
-naturalization;nat_rate_span_a;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; no groups (base years only)"
-naturalization;nat_factor_thres;no parameter in previous model;0.5;0.5;0.5;naturalizations per year;low;if the naturalization rate is below this threshold: then the trend factor ist set to nat_factor_value
-naturalization;nat_factor_value;no parameter in previous model;0;0;0;no unit;low;value of the trend factor, if the naturalization rate is below nat_factor_thres
-naturalization;nat_rate_span_dyas;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, year, sex"
-capacity and reserves;car_sc;kareb.vf;25;25;25;percent;medium;"Proportion of staircases; for the conversion from total area to living area."
-capacity and reserves;car_resi;kareb.wohnant;-30;10;50;percent;high;Residence portion ('slider'): -100% = minimum residential share according to the building regulation (BZO), 0% = real residential share, +100% = maximum residential share according to building regulation.
-capacity and reserves;car_plot;kareb.areal;0;50;100;percent;high;"Proportion of plot construction ('slider'); 0% = without plot construction, 100% = with plot construction"
-capacity and reserves;car_uti_input;no parameter in previous model;85;85;85;percent;high;degree of utilization used to calculate the input data
-capacity and reserves;car_uti;kareb.ausbau;75;85;95;percent;high;"degree of utilization; linear influence on the capacity"
-capacity and reserves;car_pp;kareb.ina.prozentpunkte;0;0;0;percent points;medium;usage: less or more percentage points (parameter between -100 and +100)
-capacity and reserves;car_y;kareb.ina.jahr;2048;2048;2048;year;low;Reference year of the usage values
-capacity and reserves;car_lambda;kareb.ina.lambda;-0.04;-0.04;-0.04;per year;high;"lambda value of an exponential function exp(lambda * time since start of scenario); proportion of utilization per year"
-living space;spa_apart;wf.anzwohn;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
-living space;spa_prop_trend;wf.anteil.trend;0;20;50;percent;high;"living space: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-living space;spa_thres_percent;wf.grenze.prozent;20;20;20;percent;low;"living space: It is not realistic that the living space changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
-living space;spa_window_thres;wf.window.szen;7;7;7;years;low;living space: filter over years (to smooth a potential break in the curve due to range of future proportions
-living space;spa_prop_trend_52p;no parameter in previous model;0;0;0;percent;low;"living space in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-allocation;aca_apart;bq.anzwohn;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
-allocation;aca_prop_trend;bq.anteil.trend;0;20;50;percent;high;"allocation: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-allocation;aca_thres_percent;bq.grenze.prozent;20;20;20;percent;low;"allocation: It is not realistic that the allocation values change dramatically (i.e. tenfold ncrease). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
-allocation;aca_window_thres;bq.window.szen;7;7;7;years;low;allocation: filter over years (to smooth a potential break in the curve due to range of future proportions
-allocation;aca_prop_trend_52p;no parameter in previous model;0;0;0;percent;low;"allocation in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-projects;pro_lambda_begin;map.lambda.beginn;3;3;3;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the beginning of the time period (of the consolidated project list)."
-projects;pro_lambda_end;map.lambda.ende;0.5;0.5;0.5;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the end of the time period (of the consolidated project list)."
-projects;pro_transfo;no parameter in previous model;5;5;5;no unit;low;transformation parameter (non-linear curve from pro_lambda_begin to pro_lambda_end)
-projects;pro_max_delay;no parameter in previous model;5;5;5;years;low;maximum delay
-projects;pro_not_scheduled_ip;part of map.nicht1 (projektiert);30;30;30;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, from Infoplan
-projects;pro_not_scheduled_other;part of map.nicht1 (projektiert);20;20;20;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, other
-projects;pro_not_submitted;map.nicht2 (eingereicht);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: submitted
-projects;pro_not_approved;map.nicht3 (bewilligt);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: approved
-projects;pro_not_started;map.nicht4 (Bau begonnen);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: started
-projects;pro_not_completed;map.nicht5 (fertiggestellt);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: completed
-projects;pro_not_onhold;map.nicht6 (sistiert);10;10;10;percent;high;Percentage of projects (i.e. apartments) that are not realized: on hold
-ownership;own_prop_trend;ea.anteil.trend;50;50;50;percent;low;"ownership: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-ownership;own_thres_percent;ea.grenze.prozent;20;20;20;percent;low;"ownership: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-ownership;own_window_thres;ea.window.szen;7;7;7;years;low;ownership: filter over years (to smooth a potential break in the curve due to range of future proportions
-ownership;own_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;ownership: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-ownership;own_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;ownership: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-housing model;car_coop;similar to wohn.modell.typ;100;100;100;percent;low;Percentage of cooperative housing can be used from capacity/reserves (car_coop = 100), or from the trends in the districts (car_coop = 0) or a mixture of both data sources. In other words: car_coop = percentage used from capacity/reserves.
-housing model;car_trust;no parameter in previous model;0;0;0;percent;high;If the amount of people according to the project list exceeds the population (in a given year!) according to capacity/reserves: trust the capacity/reserves number (parameter = 100%)? Or the project list (parameter = 0%)?
-housing model;car_max_trust;no parameter in previous model;100;100;100;percent;high;If the amount of people exceeds the maximum (over all years!) according to capacity/reserves: trust the capacity/reserves population (parameter = 100%)? Or the calculated population (parameter = 0%)? Idea: not more people than population according to reserves usage
-housing model;empty_coop;similar to wohn.modell.anteil.leerwhg;0;0;0;percent;low;Percentage of empty apartments: cooperative housing
-housing model;empty_private;similar to wohn.modell.anteil.leerwhg;0;0;0;percent;low;Percentage of empty apartments: private housing
-demography and housing model;less_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);75;75;75;percent;high;less living space available (according to reserves, compared to trend calcuation). Then immigration* is decreased, and emigration* increased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
-demography and housing model;more_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);50;50;50;percent;high;more living space available (according to reserves, compared to trend calcuation). Then immigration* is increased, and emigration* decreased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
-demography and housing model;deh_span;no parameter in previous model;0.04;0.04;0.04;no unit;medium;"in future population: proportion of data points used in the LOESS regressions by age; groups: district, year, sex, origin"
-output;basis_fact;no parameter in previous model;1;1;1;no unit;no parameter;value for field BasisSzenarienCd in DWH for factual values from the past
-output;basis_scen;no parameter in previous model;2;2;2;no unit;no parameter;value for field BasisSzenarienCd in DWH for calculated scenario values
+";13;13;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
+emigration*;ems_so_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+emigration*;ems_span_y;no parameter in previous model;0.3;0.3;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of proportion by year; groups: district, age, sex, origin (emigration* by district, year, age, sex, origin)"
+emigration*;ems_span_a;no parameter in previous model;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
+emigration*;ems_age_prop_trend;no parameter in previous model;20;20;20;20;20;percent;low;"age distribution of emigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+emigration*;ems_age_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"age distribution of emigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+emigration*;ems_age_window_thres;no parameter in previous model;13;13;13;13;13;years;low;age distribution of emigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
+emigration*;ems_age_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;age distribution of emigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+relocation and immigration*;rei_age_max;umz.alter.max;70;70;70;70;70;years;low;as of this age: one single proportion (relocation on immigration*)
+relocation and immigration*;rei_ims_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, year, origin"
+relocation and immigration*;rei_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
+relocation and immigration*;rei_ims_span_dao;similar to umz.window.alter;0.12;0.12;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, origin"
+relocation and immigration*;rei_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
+relocation and immigration*;rei_ims_thres_y;similar to umz.anz.rate.stadt;0.4;0.4;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
+relocation and immigration*;rei_prop_span;similar to umz.window.alter;0.3;0.3;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
+relocation and immigration*;rei_prop_trend;umz.anteil.trend;20;20;20;20;20;percent;low;"relocation proportion on immigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+relocation and immigration*;rei_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"relocation proportion on immigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+relocation and immigration*;rei_window_thres;no parameter in previous model;13;13;13;13;13;years;low;relocation proportion on immigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
+relocation and immigration*;rei_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+relocation and immigration*;rei_upper_thres;no parameter in previous model;100;100;100;100;100;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
+relocation and immigration*;rei_pred_span;similar to umz.window.alter;0.15;0.15;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions of the prediction (proportion) by age; groups: district, year, origin"
+relocation and emigration*;ree_age_max;umz.alter.max;70;70;70;70;70;years;low;as of this age: one single proportion (relocation on emigration*)
+relocation and emigration*;ree_ems_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, year, origin"
+relocation and emigration*;ree_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
+relocation and emigration*;ree_ems_span_dao;similar to umz.window.alter;0.12;0.12;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, origin"
+relocation and emigration*;ree_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
+relocation and emigration*;ree_ems_thres_y;similar to umz.anz.rate.heimat;0.4;0.4;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
+relocation and emigration*;ree_prop_span;similar to umz.window.alter;0.3;0.3;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
+relocation and emigration*;ree_prop_trend;umz.anteil.trend;20;20;20;20;20;percent;low;"relocation proportion on emigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+relocation and emigration*;ree_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"relocation proportion on emigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
+relocation and emigration*;ree_window_thres;no parameter in previous model;13;13;13;13;13;years;low;relocation proportion on emigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
+relocation and emigration*;ree_lower_thres;no parameter in previous model;0;0;0;0;0;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
+relocation and emigration*;ree_upper_thres;no parameter in previous model;100;100;100;100;100;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
+relocation and emigration*;ree_pred_span;similar to umz.window.alter;0.15;0.15;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions the prediction (proportion) by age; groups: district, year, origin"
+naturalization;nat_pop_span_das;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: district, sex"
+naturalization;nat_nat_span_das;similar to brw.window.alter;0.15;0.15;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: district, sex"
+naturalization;nat_pop_thres;similar to brw.rate.anz.null;0.5;0.5;0.5;0.5;0.5;persons;low;if less persons in the (smoothed) foreign population than this threshold: then the naturalization rate is set to nat_pop_value
+naturalization;nat_pop_value;no parameter in previous model;0;0;0;0;0;percent per year;low;value of the naturalization rate, if the foreign population is below nat_pop_thres
+naturalization;nat_rate_span_das;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, sex"
+naturalization;nat_pop_span_ya;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: year (base years only)"
+naturalization;nat_nat_span_ya;similar to brw.window.alter;0.15;0.15;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: year (base years only)"
+naturalization;nat_rate_span_ya;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: year (base years only)"
+naturalization;nat_prop_trend;brw.anteil.trend;20;20;20;20;20;percent;low;"naturalization: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+naturalization;nat_thres_percent;no parameter in previous model;20;20;20;20;20;percent;low;"naturalization: It is not realistic that the rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
+naturalization;nat_window_thres;no parameter in previous model;13;13;13;13;13;years;low;naturalization: filter over years (to smooth a potential break in the curve due to range of future proportions
+naturalization;nat_lower_thres;no parameter in previous model;0;0;0;0;0;percent;low;naturalization: lower threshold for the proportions (here: the naturalization rates should not be negative), NA if there is no lower threshold
+naturalization;nat_pop_span_a;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; no groups (base years only)"
+naturalization;nat_nat_span_a;similar to brw.window.alter;0.15;0.15;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; no groups (base years only)"
+naturalization;nat_rate_span_a;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; no groups (base years only)"
+naturalization;nat_factor_thres;no parameter in previous model;0.5;0.5;0.5;0.5;0.5;naturalizations per year;low;if the naturalization rate is below this threshold: then the trend factor ist set to nat_factor_value
+naturalization;nat_factor_value;no parameter in previous model;0;0;0;0;0;no unit;low;value of the trend factor, if the naturalization rate is below nat_factor_thres
+naturalization;nat_rate_span_dyas;similar to brw.window.alter;0.1;0.1;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, year, sex"
+capacity and reserves;car_sc;kareb.vf;25;25;25;25;25;percent;medium;"Proportion of staircases; for the conversion from total area to living area."
+capacity and reserves;car_resi;kareb.wohnant;-30;10;10;10;50;percent;high;Residence portion ('slider'): -100% = minimum residential share according to the building regulation (BZO), 0% = real residential share, +100% = maximum residential share according to building regulation.
+capacity and reserves;car_plot;kareb.areal;0;50;50;50;100;percent;high;"Proportion of plot construction ('slider'); 0% = without plot construction, 100% = with plot construction"
+capacity and reserves;car_uti_input;no parameter in previous model;85;85;85;85;85;percent;high;degree of utilization used to calculate the input data
+capacity and reserves;car_uti;kareb.ausbau;75;85;85;85;95;percent;high;"degree of utilization; linear influence on the capacity"
+capacity and reserves;car_pp;kareb.ina.prozentpunkte;0;0;0;0;0;percent points;medium;usage: less or more percentage points (parameter between -100 and +100)
+capacity and reserves;car_y;kareb.ina.jahr;2049;2049;2049;2049;2049;year;low;Reference year of the usage values
+capacity and reserves;car_lambda;kareb.ina.lambda;-0.04;-0.04;-0.04;-0.04;-0.04;per year;high;"lambda value of an exponential function exp(lambda * time since start of scenario); proportion of utilization per year"
+living space;spa_apart;wf.anzwohn;500;500;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
+living space;spa_prop_trend;wf.anteil.trend;0;20;20;20;50;percent;high;"living space: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+living space;spa_thres_percent;wf.grenze.prozent;20;20;20;20;20;percent;low;"living space: It is not realistic that the living space changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
+living space;spa_window_thres;wf.window.szen;7;7;7;7;7;years;low;living space: filter over years (to smooth a potential break in the curve due to range of future proportions
+living space;spa_prop_trend_52p;no parameter in previous model;0;0;0;0;0;percent;low;"living space in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+allocation;aca_apart;bq.anzwohn;500;500;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
+allocation;aca_prop_trend;bq.anteil.trend;0;20;20;20;50;percent;high;"allocation: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+allocation;aca_thres_percent;bq.grenze.prozent;20;20;20;20;20;percent;low;"allocation: It is not realistic that the allocation values change dramatically (i.e. tenfold ncrease). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
+allocation;aca_window_thres;bq.window.szen;7;7;7;7;7;years;low;allocation: filter over years (to smooth a potential break in the curve due to range of future proportions
+allocation;aca_prop_trend_52p;no parameter in previous model;0;0;0;0;0;percent;low;"allocation in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
+projects;pro_lambda_begin;map.lambda.beginn;0.9;1.1;1.1;1.1;1.3;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the beginning of the time period (of the consolidated project list)."
+projects;pro_lambda_end;map.lambda.ende;0.1;0.1;0.1;0.1;0.1;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the end of the time period (of the consolidated project list)."
+projects;pro_transfo;no parameter in previous model;10;10;10;10;10;no unit;low;transformation parameter (non-linear curve from pro_lambda_begin to pro_lambda_end)
+projects;pro_max_delay;no parameter in previous model;5;5;5;5;5;years;low;maximum delay
+projects;pro_not_scheduled_ip;part of map.nicht1 (projektiert);40;40;40;40;40;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, from Infoplan
+projects;pro_not_scheduled_other;part of map.nicht1 (projektiert);30;30;30;30;30;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, other
+projects;pro_not_submitted;map.nicht2 (eingereicht);20;20;20;20;20;percent;high;Percentage of projects (i.e. apartments) that are not realized: submitted
+projects;pro_not_approved;map.nicht3 (bewilligt);10;10;10;10;10;percent;high;Percentage of projects (i.e. apartments) that are not realized: approved
+projects;pro_not_started;map.nicht4 (Bau begonnen);0;0;0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: started
+projects;pro_not_completed;map.nicht5 (fertiggestellt);0;0;0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: completed
+projects;pro_not_onhold;map.nicht6 (sistiert);50;50;50;50;50;percent;high;Percentage of projects (i.e. apartments) that are not realized: on hold
+housing model;car_trust;no parameter in previous model;0;0;0;0;0;percent;high;If the amount of people according to the project list exceeds the population (in a given year!) according to capacity/reserves: trust the capacity/reserves number (parameter = 100%)? Or the project list (parameter = 0%)?
+housing model;car_max_trust;no parameter in previous model;100;100;100;100;100;percent;high;If the amount of people exceeds the maximum (over all years!) according to capacity/reserves: trust the capacity/reserves population (parameter = 100%)? Or the calculated population (parameter = 0%)? Idea: not more people than population according to reserves usage
+housing model;empty_coop;similar to wohn.modell.anteil.leerwhg;0;0;0;0;0;percent;low;Percentage of empty apartments: cooperative housing
+housing model;empty_private;similar to wohn.modell.anteil.leerwhg;0;0;0;0;0;percent;low;Percentage of empty apartments: private housing
+demography and housing model;less_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);75;75;75;75;75;percent;high;less living space available (according to reserves, compared to trend calcuation). Then immigration* is decreased, and emigration* increased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
+demography and housing model;more_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);50;50;50;50;50;percent;high;more living space available (according to reserves, compared to trend calcuation). Then immigration* is increased, and emigration* decreased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
+demography and housing model;deh_span;no parameter in previous model;0.04;0.04;0.04;0.04;0.04;no unit;medium;"in future population: proportion of data points used in the LOESS regressions by age; groups: district, year, sex, origin"
diff --git a/2_Data/3_Parameter/variables.yml b/2_Data/3_Parameter/variables.yml
index e31dd47a..26e5349c 100644
--- a/2_Data/3_Parameter/variables.yml
+++ b/2_Data/3_Parameter/variables.yml
@@ -69,6 +69,7 @@ text_i: ["new", "removed"]
 
 # past and scenarios
 text_c: ["past", "lower", "middle", "upper"]
+text_cb: ["past", "middle_birth_lower", "middle", "middle_birth_upper"]
 
 
 # categories (t = text) and associated lookup tables ----------------------
@@ -88,6 +89,10 @@ age_3t: ["0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "
 age_4: !expr seq(0, 80, by = 20)
 age_4t: ["0-19", "20-39", "40-59", "60-79", "80+"]
 
+# age category 5
+age_5: [5, 10, 15]
+age_5t: ["0-4", "5-9", "10-14", "15+"]
+
 
 # colors, graphics --------------------------------------------------------
 # colors (e.g. for sex, origin)
diff --git a/Plots/01_birth.qmd b/Plots/01_birth.qmd
index 01fb10a7..2cea28c8 100644
--- a/Plots/01_birth.qmd
+++ b/Plots/01_birth.qmd
@@ -7,7 +7,7 @@
 # source general functions
 if (!exists("i_scen")) {
   source(paste0(here::here(), "/1_code/0000_general/general_init.R"))
-  params <- init("middle")
+  params <- init("middle_birth_lower")
 }
 
 # run the scenario
@@ -143,7 +143,7 @@ sszplot(tfr_ya2o,
   labs_y = "TFR", i_x = "5",
   geom = c("line", "point"),
   name = plot_name("0105_TFR_by-year-age2"),
-  width = 16, height = 5
+  width = 20, height = 5
 )
 ```
 
@@ -282,7 +282,7 @@ sszplot(fer_pred,
   wrap = "district",
   labs_y = "fertility rate (in % per year)",
   name = plot_name("0140_fertility-prediction_by-district"),
-  width = 12, height = 14,
+  width = 20, height = 24,
   multi = uni_o,
   quotes = quote(ylim(c(0, bir_plot_lim)))
 )
@@ -303,7 +303,7 @@ sel_age <- uni_age[(uni_age %% 5) == 0]
 
 age_dat <- filter(fer_pred, (origin == uni_o[1]) & (age %in% sel_age))
 sszplot(age_dat,
-  aes_x = "year", aes_y = "fer_fit",
+  aes_x = "year", aes_y = "fer_all",
   labs_y = "fertility rate (in % per year)",
   wrap = "age", ncol = 4,
   scale_y = c(0, bir_plot_lim),
@@ -332,7 +332,7 @@ foreign
 # plot: foreign
 age_dat <- filter(fer_pred, (origin == uni_o[2]) & (age %in% sel_age))
 sszplot(age_dat,
-  aes_x = "year", aes_y = "fer_fit",
+  aes_x = "year", aes_y = "fer_all",
   labs_y = "fertility rate (in % per year)",
   wrap = "age", ncol = 4,
   scale_y = c(0, bir_plot_lim),
@@ -362,8 +362,8 @@ sel_years <- uniy_scen[(uniy_scen %% 10) == 0]
 sel_lev <- c("initial", "smoothed")
 
 sel_dat <- pred_fit %>%
-  pivot_longer(c(pred_fit, pred_roll), names_to = "category", values_to = "fer") %>%
-  mutate(cat = fact_if(category, sel_lev, "pred_roll")) %>%
+  pivot_longer(c(pred_fit, fer_all), names_to = "category", values_to = "fer") %>%
+  mutate(cat = fact_if(category, sel_lev, "fer_all")) %>%
   filter(year %in% sel_years)
 
 # plot
diff --git a/Plots/02_death.qmd b/Plots/02_death.qmd
index 416cee71..e502b3dd 100644
--- a/Plots/02_death.qmd
+++ b/Plots/02_death.qmd
@@ -65,7 +65,7 @@ sszplot(filter(mor_yasr, year %in% year_5),
 #| fig-cap: "mortality by year, age, sex, region"
 
 # age
-age_select <- c(0, 20, 40, 50, 60, 70, 80)
+age_select <- c(0, 20, 40, 50, 60, 70, 75, 80, 85)
 
 sszplot(filter(mor_yasr, age %in% age_select),
   aes_x = "year", aes_y = "mor_yasr", aes_col = "sex",
@@ -234,6 +234,33 @@ sszplot(mor_zh_yas_past_future,
 )
 ```
 
+### Zurich and Switzerland: future mortality rate
+
+
+```{r}
+#| label: fig-0210a
+#| fig-cap: mortality by year, age, sex, region
+
+# age
+age_select <- c(0, 20, 40, 50, 60, 70, 75, 80, 85, 90)
+
+sszplot(filter(mor_zh_ch_yas_past_future, age %in% age_select),
+  aes_x = "year", aes_y = "mor_yasr", aes_col = "sex",
+  labs_y = "mortality rate (in % per year)",
+  scale_y = "log",
+  grid = c("region", "age"),
+  name = plot_name("0210a_mortality_by-year-age-sex-region_focus-year-sex"),
+  width = 14, height = 7,
+  angle = 90
+)
+
+
+
+```
+
+
+
+
 ### Zurich: life expectancy (including the model data)
 
 #### focus: differences by sex
@@ -260,7 +287,6 @@ le_ysr_model <- filter(le_ysr, region != "Zurich") %>%
   bind_rows(le_ys_ZH) %>%
   arrange(year, sex, region)
 
-
 sszplot(le_ysr_model,
   aes_x = "year", aes_y = "le_ysr", aes_col = "sex",
   labs_y = "life expectancy at birth",
@@ -285,5 +311,65 @@ sszplot(le_ysr_model,
   grid = c(".", "sex"),
   name = plot_name("0212_life-expectancy-at-birth_by-year-sex-region_focus-region_model"),
   width = 8, height = 4.5
-)
+) 
 ```
+
+
+### Zurich: life expectancy at different age
+
+```{r}
+#| label: fig-0213
+#| fig-cap: "life expectancy at different age (0 and 65), by year, region, sex"
+#| fig-width: 8
+#| fig-height: 7
+
+# mortality preparation (CH)
+mor_prep_ch <- mor_yasr |> 
+  filter(region == uni_r[2])
+
+# mortality preparation (ZH/CH, past/future)
+mor_prep <- mor_zh_yas_past_future |> 
+  mutate(region = factor(text_r[1], uni_r)) |> 
+  rename(mor_yasr = mor_yas) |> 
+  select(year, age, sex, region, mor_yasr) |> 
+  bind_rows(mor_prep_ch)
+
+# life expectancy at different age (0 and 65)
+le_ysr_a0 <- life_exp(
+  data = mor_prep, mor = "mor_yasr",
+  age = "age", group_cols = c("year", "sex", "region"),
+  age_max = dea_age_max_le, qx_NA = dea_qx_NA_le,
+  age_at = 0, radix = dea_radix
+) |> 
+  mutate(age = 0)
+
+le_ysr_a65 <- life_exp(
+  data = mor_prep, mor = "mor_yasr",
+  age = "age", group_cols = c("year", "sex", "region"),
+  age_max = dea_age_max_le, qx_NA = dea_qx_NA_le,
+  age_at = 65, radix = dea_radix
+) |> 
+  mutate(age = 65)
+
+# life expectancy for all combinations (ZH/CH, past/future, sex, region)
+
+# correction for Switzerland: latest values not available yet
+# the same correction as before (see 0200_death.r)
+le_yasr <- le_ysr_a0 |> 
+  bind_rows(le_ysr_a65) |> 
+  mutate(le_yasr = if_else((region == uni_r[2]) & 
+           !(year %in% c(dea_fso_date_start:dea_fso_date_end, 
+                         scen_begin:scen_end)),
+      NA_real_, life_exp))
+
+sszplot(le_yasr,
+  aes_x = "year", aes_y = "le_yasr", aes_col = "age",
+  labs_y = "life expectancy",
+  grid = c("region", "sex"),
+  name = plot_name("0213_life-expectancy-at-different-age_by-year-sex-region"),
+  width = 8, height = 7
+) 
+
+```
+
+
diff --git a/Plots/12_ownership.qmd b/Plots/12_ownership.qmd
deleted file mode 100644
index 5cdca1f2..00000000
--- a/Plots/12_ownership.qmd
+++ /dev/null
@@ -1,62 +0,0 @@
-
-# Ownership
-
-```{r}
-#| label: prep
-#| output: false
-
-# source general functions
-if (!exists("i_scen")) {
-  source(paste0(here::here(), "/1_code/0000_general/general_init.R"))
-  params <- init("middle")
-}
-
-# source utility functions
-run_scen(i_scen, "own")
-
-```
-
-## proportion of cooperative housing
-
-### proportions (based on apartments and people)
-
-```{r}
-#| label: fig-1200
-#| fig-cap: proportion of cooperative housing
-#| fig-width: 12
-#| fig-height: 14
-
-sszplot(own_prop,
-  aes_x = "year", aes_y = "prop", aes_col = "category",
-  labs_y = "proportion of cooperative housing (in %)",
-  wrap = "district", ncol = 4,
-  scale_y = c(0, NA),
-  name = plot_name("1200_proportion-cooperative-housing"),
-  width = 12, height = 14
-)
-```
-
-## prediction
-category: people
-WHY? prediction is based on different sources
-projects: apartments
-reserves: m2
-however, the combined prediction is for persons
-therefore, the proportion based on people
-
-```{r}
-#| label: fig-1201
-#| fig-cap: prediction of proportion of cooperative housing
-#| fig-width: 12
-#| fig-height: 14
-
-sszplot(own_past_pred,
-  aes_x = "year", aes_y = "own_all",
-  labs_y = "proportion of cooperative housing (in %)",
-  wrap = "district", ncol = 4,
-  i_x = c(own_base_begin, own_base_end),
-  scale_y = c(0, NA),
-  name = plot_name("1201_proportion-cooperative-housing_prediction"),
-  width = 12, height = 14
-)
-```
diff --git a/Plots/13_housing.qmd b/Plots/13_housing.qmd
index 63249385..c99c0115 100644
--- a/Plots/13_housing.qmd
+++ b/Plots/13_housing.qmd
@@ -52,27 +52,27 @@ sszplot(pop_with_past,
 #| fig-width: 12
 #| fig-height: 14
 
-prop_coop %>%
-  pivot_longer(
-    cols = c("prop_car", "prop_trend"),
-    names_prefix = "prop_",
-    names_to = "category", values_to = "prop"
-  ) %>%
-  mutate(
-    cat = if_else(category == "car",
-      "capacity/reserves", "district trends"
-    ),
-    district = factor(district, levels = uni_d)
-  ) %>%
-  sszplot(
-    aes_x = "year", aes_y = "prop", aes_col = "cat",
-    labs_y = "proportion of cooperative housing (in %)",
-    wrap = "district", ncol = 4,
-    scale_y = c(0, NA),
-    i_x = c(NA, date_end),
-    name = plot_name("1301_proportion-cooperative-housing_data-sources"),
-    width = 12, height = 14
-  )
+# prop_coop %>%
+#   pivot_longer(
+#     cols = c("prop_car", "prop_trend"),
+#     names_prefix = "prop_",
+#     names_to = "category", values_to = "prop"
+#   ) %>%
+#   mutate(
+#     cat = if_else(category == "car",
+#       "capacity/reserves", "district trends"
+#     ),
+#     district = factor(district, levels = uni_d)
+#   ) %>%
+#   sszplot(
+#     aes_x = "year", aes_y = "prop", aes_col = "cat",
+#     labs_y = "proportion of cooperative housing (in %)",
+#     wrap = "district", ncol = 4,
+#     scale_y = c(0, NA),
+#     i_x = c(NA, date_end),
+#     name = plot_name("1301_proportion-cooperative-housing_data-sources"),
+#     width = 12, height = 14
+#   )
 ```
 
 ## projects, capcity and reserves
@@ -275,6 +275,7 @@ pro_car_dw %>%
   )
 ```
 
+
 ### total proportion by district
 
 ```{r}
@@ -302,3 +303,4 @@ pro_car_dw %>%
     width = 8, height = 8
   )
 ```
+
diff --git a/Plots/15_model_output.qmd b/Plots/15_model_output.qmd
index c2c8c4c8..654ccf6f 100644
--- a/Plots/15_model_output.qmd
+++ b/Plots/15_model_output.qmd
@@ -225,6 +225,50 @@ pop %>%
   )
 ```
 
+### year, age, scenario (children)
+
+```{r}
+
+#| label: fig-1509a
+#| fig-cap: year, age, scenario (children)
+#| fig-width: 8
+#| fig-height: 5
+
+sszplot(pop_yac_children,
+  aes_x = "year", aes_y = "pop", aes_col = "scenario",
+  labs_y = "population",
+  grid = c(".", "age_5"),
+  scale_y = c(0, NA),
+  name = plot_name("1509a_pop_yac_children")
+)
+
+
+
+```
+
+
+### year, age, birth version (children)
+
+```{r}
+
+#| label: fig-1509a
+#| fig-cap: year, age, birth version (children)
+#| fig-width: 8
+#| fig-height: 5
+
+sszplot(pop_yav_children,
+  aes_x = "year", aes_y = "pop", aes_col = "scenario",
+  labs_y = "population",
+  grid = c(".", "age_5"),
+  scale_y = c(0, NA),
+  name = plot_name("1509b_pop_yac_children")
+)
+
+
+
+```
+
+
 ## population: new and previous scenarios
 
 ### by year and scenario
@@ -394,7 +438,7 @@ pop_dy_new_sel %>%
 
 ## birth
 
-### by year and scenario
+### by year and scenario (main scenarios)
 
 ```{r}
 #| label: fig-1520
@@ -419,6 +463,7 @@ bir %>%
   )
 ```
 
+
 ### by year, origin and scenario
 
 ```{r}
@@ -437,6 +482,100 @@ bir %>%
   )
 ```
 
+### births by year and birth versions (middle scenario)
+
+```{r}
+#| label: fig-1521a
+#| fig-cap: births by year and birth version (middle scenario)
+
+# birth: past and future (birth versions)
+bir_ver <- bir_past |> 
+  bind_rows(bir_middle) |> 
+  bind_rows(bir_middle_birth_lower) |> 
+  bind_rows(bir_middle_birth_upper) |> 
+  mutate(scenario = factor(scenario, levels = uni_cb),
+         sex = factor(sex, levels = uni_s),
+         origin = factor(origin, levels = uni_o))
+
+bir_ver |> 
+  group_by(year, scenario) |> 
+  summarize(bir = sum(bir), .groups = "drop") |> 
+  sszplot(aes_x = "year", aes_y = "bir", aes_col = "scenario",
+          labs_y = "births per year",
+          scale_y = c(0, NA),
+          name = plot_name("1521a_bir_ver_yc")
+  )
+
+```
+
+
+### TFR by year and birth versions (middle scenario)
+
+```{r}
+#| label: fig-1521b
+#| fig-cap: TFR by year and birth version (middle scenario)
+
+# population: past and future (for birth versions)
+pop_ver_yac <- pop_past |> 
+  bind_rows(pop_middle) |> 
+  bind_rows(pop_middle_birth_lower) |> 
+  bind_rows(pop_middle_birth_upper) |> 
+  filter((sex == uni_s[2]) & (age >= bir_age_begin) & (age <= bir_age_end)) |> 
+  mutate(scenario = factor(scenario, levels = uni_cb)) |> 
+  group_by(year, age, scenario) |> 
+  summarize(pop = sum_NA(pop), .groups = "drop")
+
+# births (for birth versions)
+bir_ver_yac <- bir_ver |> 
+  rename(age = age_mother) |> 
+  group_by(year, age, scenario) |> 
+  summarize(bir = sum_NA(bir), .groups = "drop")  
+
+
+# TFR  
+pop_bir_ver_yac <- pop_ver_yac |> 
+  left_join(bir_ver_yac, by = c("year", "age", "scenario")) |> 
+  replace_na(list(bir = 0))  
+ 
+pop_bir_ver_yac |>   
+  mutate(fer_yac = bir / pop) |> 
+  group_by(year, scenario) |> 
+  summarize(tfr = sum_NA(fer_yac), .groups = "drop") |>   
+  sszplot(
+    aes_x = "year", aes_y = "tfr", aes_col = "scenario",
+    labs_y = "TFR", 
+    scale_y = c(0, NA),
+    name = plot_name("1521b_tfr_ver_yc")
+  )
+
+
+
+
+```
+
+### fertility rate by year and birth versions (middle scenario)
+
+```{r}
+
+#| label: fig-1521c
+#| fig-cap: fertility rate by year and birth version (middle scenario)
+
+# fertility rate
+pop_bir_ver_yac |> 
+  group_by(year, scenario) |> 
+  summarize(bir = sum_NA(bir), pop = sum_NA(pop), .groups = "drop") |> 
+  mutate(fer_yc = bir / pop * 100) |> 
+  sszplot(
+    aes_x = "year", aes_y = "fer_yc", aes_col = "scenario",
+    labs_y = "fertility rate (in % per year)", 
+    scale_y = c(0, NA),
+    name = plot_name("1521c_fer_ver_yc")
+  )
+
+```
+
+
+
 ### by district, year and origin
 
 ```{r}
@@ -612,6 +751,35 @@ imm %>%
   )
 ```
 
+### by year, age (children) and scenario
+
+```{r}
+#| label: fig-1541a
+#| fig-cap: immigration by year, age (children) and scenario
+
+imm_a5 <- imm |> 
+  filter(age < age_5[3]) |> 
+  left_join(look_a5, by = "age") |>   
+  group_by(year, age_5, scenario) %>%
+  summarize(imm = sum(imm), .groups = "drop")
+
+imm_a5 |> 
+  sszplot(aes_x = "year", aes_y = "imm", aes_col = "scenario",
+          labs_y = "immigration per year",
+          wrap = "age_5", ncol = 3,
+          scale_y = c(0, NA),
+          i_x = scen_begin,
+          width = 12, height = 6,
+          name = plot_name("1541a_imm_ya")
+  )
+
+
+```
+
+
+
+
+
 ### by year, age and origin
 
 ```{r}
@@ -714,7 +882,7 @@ emi %>%
 emi %>%
   filter(scenario %in% uni_c[c(1, 3)]) %>%
   group_by(year, age_3) %>%
-  summarize(emi = sum(emi), .groups = "drop") %>%
+  summarize(emi = sum(emi), .groups = "drop") |> 
   sszplot(aes_x = "year", aes_y = "emi",
           labs_y = "emigration per year",
           wrap = "age_3", ncol = 5,
@@ -724,6 +892,30 @@ emi %>%
           name = plot_name("1551_emi_ya")
   )
 ```
+### by year, age (children) and scenario
+
+```{r}
+#| label: fig-1551a
+#| fig-cap: emigration by year, age (children) and scenario
+
+emi_a5 <- emi |> 
+  filter(age < age_5[3]) |> 
+  left_join(look_a5, by = "age") |>   
+  group_by(year, age_5, scenario) %>%
+  summarize(emi = sum(emi), .groups = "drop") 
+  
+emi_a5 |> 
+  sszplot(aes_x = "year", aes_y = "emi", aes_col = "scenario",
+          labs_y = "emigration per year",
+          wrap = "age_5", ncol = 3,
+          scale_y = c(0, NA),
+          i_x = scen_begin,
+          width = 12, height = 6,
+          name = plot_name("1551a_imm_ya")
+  )
+
+
+```
 
 ### by year, age and origin
 
@@ -851,6 +1043,27 @@ foreign proportion: not possible to calculate for net migration
 cannot be applied here
 e.g. low values in the denominator if negative and positive value
 
+### by year, age (children) and scenario
+
+```{r}
+#| label: fig-1562
+#| fig-cap: net migration by year and age (children)
+
+imm_a5 |> 
+  left_join(emi_a5, by = c("year", "age_5", "scenario")) |> 
+  mutate(net = imm - emi) |> 
+  sszplot(aes_x = "year", aes_y = "net", aes_col = "scenario",
+          labs_y = "net migration per year",
+          wrap = "age_5", ncol = 3,
+          scale_y = c(0, NA),
+          i_x = scen_begin,
+          width = 12, height = 6,
+          name = plot_name("1562_net_ya")
+  )
+
+```
+
+
 ## naturalization
 
 ### by year and scenario
diff --git a/Plots/_quarto.yml b/Plots/_quarto.yml
index 43c2cdd0..f31eb685 100644
--- a/Plots/_quarto.yml
+++ b/Plots/_quarto.yml
@@ -24,7 +24,6 @@ book:
     - 09_living_space.qmd
     - 10_allocation.qmd
     - 11_projects.qmd
-    - 12_ownership.qmd
     - 13_housing.qmd
     - 14_dem_house.qmd
     - 15_model_output.qmd
diff --git a/README.md b/README.md
index 11743d61..46d4b6e5 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,8 @@ A log file is being created at [2_Data/6_Log](2_Data/6_Log).
 
 ## Documentation:
 
+Verification charts may be found on [Github Pages.](https://statistikstadtzuerich.github.io/BevSzen/)
+
 [Documentation (german)](https://www.stadt-zuerich.ch/prd/de/index/statistik/themen/bevoelkerung/bevoelkerungsentwicklung/bevoelkerungsszenarien.html#dokumentation)
 
 ## Author:
diff --git a/data_freeze/2_Data/.gitignore b/data_freeze/2_Data/.gitignore
deleted file mode 100644
index 668f5194..00000000
--- a/data_freeze/2_Data/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-1_Input
-4_Rates
-5_Outputs
-6_Log
-7_DWH
-8_requests
\ No newline at end of file
diff --git a/data_freeze/2_Data/2_Lookup/lookupDistrict.csv b/data_freeze/2_Data/2_Lookup/lookupDistrict.csv
deleted file mode 100644
index 37ecfb60..00000000
--- a/data_freeze/2_Data/2_Lookup/lookupDistrict.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-QuarCd;QuarLang;distr
-011;Rathaus;Kreis 1
-012;Hochschulen;Kreis 1
-013;Lindenhof;Kreis 1
-014;City;Kreis 1
-021;Wollishofen;Wollishofen
-023;Leimbach;Leimbach
-024;Enge;Enge
-031;Alt-Wiedikon;Alt-Wiedikon
-033;Friesenberg;Friesenberg
-034;Sihlfeld;Sihlfeld
-041;Werd;Werd
-042;Langstrasse;Langstrasse
-044;Hard;Hard
-051;Gewerbeschule;Gewerbeschule
-052;Escher Wyss;Escher Wyss
-061;Unterstrass;Unterstrass
-063;Oberstrass;Oberstrass
-071;Fluntern;Fluntern
-072;Hottingen;Hottingen
-073;Hirslanden;Hirslanden
-074;Witikon;Witikon
-081;Seefeld;Seefeld
-082;Mühlebach;Muehlebach
-083;Weinegg;Weinegg
-091;Albisrieden;Albisrieden
-092;Altstetten;Altstetten
-101;Höngg;Hoengg
-102;Wipkingen;Wipkingen
-111;Affoltern;Affoltern
-115;Oerlikon;Oerlikon
-119;Seebach;Seebach
-121;Saatlen;Saatlen
-122;Schwamendingen-Mitte;Schwamendingen-Mitte
-123;Hirzenbach;Hirzenbach
diff --git a/data_freeze/2_Data/2_Lookup/lookupDistrict.xlsx b/data_freeze/2_Data/2_Lookup/lookupDistrict.xlsx
deleted file mode 100644
index 94754e94..00000000
Binary files a/data_freeze/2_Data/2_Lookup/lookupDistrict.xlsx and /dev/null differ
diff --git a/data_freeze/2_Data/3_Parameter/parameter.csv b/data_freeze/2_Data/3_Parameter/parameter.csv
deleted file mode 100644
index a87dd3bf..00000000
--- a/data_freeze/2_Data/3_Parameter/parameter.csv
+++ /dev/null
@@ -1,202 +0,0 @@
-category;parameter;parameter_old;lower;middle;upper;unit;relevance;description
-time;date_start;daten.beginn;1993;1993;1993;year;no parameter (strictly speaking);begin of data
-time;date_end;daten.ende;2022;2022;2022;year;no parameter (strictly speaking);end of data
-time;scen_begin;szen.beginn;2023;2023;2023;year;no parameter (strictly speaking);future: begin
-time;scen_end;szen.ende;2050;2050;2050;year;no parameter (strictly speaking);future: end
-time;scen_end_public;no parameter in previous model;2045;2045;2045;year;no parameter (strictly speaking);future: end (in publication)
-time;bir_base_begin;geb.basis.beginn;2010;2010;2010;year;medium;base period (in the past) for birth rate, begin
-time;bir_base_end;geb.basis.ende;2022;2022;2022;year;medium;base period (in the past) for birth rate, end
-time;bir_cha_base_begin;no parameter in previous model;2007;2007;2007;year;low;base period (in the past) for origin change (mother-baby), begin
-time;bir_cha_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (in the past) for origin change (mother-baby), end
-time;bir_sex_ratio_begin;no parameter in previous model;1993;1993;1993;year;low;base period (in the past) for sex ratio calculation, begin
-time;bir_sex_ratio_end;no parameter in previous model;2022;2022;2022;year;low;base period (in the past) for sex ratio calculation, begin
-time;dea_fso_date_start;no parameter in previous model;1993;1993;1993;year;low;fso mortality data of the past: begin
-time;dea_fso_date_end;no parameter in previous model;2021;2021;2021;year;low;fso mortality data of the past: end
-time;dea_base_begin;tod.basis.beginn;2012;2012;2012;year;low;base period (in the past) for death rate, begin (data should be available for both Zurich and Switzerland)
-time;dea_base_end;tod.basis.ende;2021;2021;2021;year;low;base period (in the past) for death rate, end (data should be available for both Zurich and Switzerland)
-time;ims_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for immigration* rate, begin
-time;ims_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for immigration* rate, end
-time;ims_so_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of sex and origin in immigration*, begin
-time;ims_so_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of sex and origin in immigration*, end
-time;ims_age_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of age in immigration*, begin
-time;ims_age_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of age in immigration*, end
-time;ems_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for emigration* rate, begin
-time;ems_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for emigration* rate, end
-time;ems_so_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of sex and origin in emigration*, begin
-time;ems_so_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of sex and origin in emigration*, end
-time;ems_age_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for proportion of age in emigration*, begin
-time;ems_age_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future)  for proportion of age in emigration*, end
-time;rei_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for relocation based on immigration* (proportion), begin
-time;rei_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for relocation based on immigration* (proportion), end
-time;ree_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for relocation based on emigration* (proportion), begin
-time;ree_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for relocation based on emigration* (proportion), end
-time;nat_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for naturalization, begin
-time;nat_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for naturalization, end
-time;spa_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for living space, begin
-time;spa_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for living space, end
-time;spa_base_begin_52p;no parameter in previous model;2015;2015;2015;year;low;"base period (not the future) for living space, begin, district 'Escher Wyss' (district ID = 52; since empty apartments increased the mean living space values; before 2015)"
-time;aca_base_begin;no parameter in previous model;2013;2013;2013;year;low;base period (not the future) for allocation, begin
-time;aca_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for allocation, end
-time;aca_base_begin_52p;no parameter in previous model;2015;2015;2015;year;low;"base period (not the future) for allocation, begin, district 'Escher Wyss' (district ID = 52; since empty apartments decreased the mean allocation values; before 2015)"
-time;pro_begin;map.wohnbau.beginn;2023;2023;2023;year;low;"first year (of consolidated project list; reasonable: first scenario year)"
-time;pro_end;map.wohnbau.ende;2035;2035;2035;year;low;last year (of consolidated project list)
-time;own_base_begin;no parameter in previous model;2015;2015;2015;year;low;base period (not the future) for ownership calculation, begin
-time;own_base_end;no parameter in previous model;2022;2022;2022;year;low;base period (not the future) for ownership calculation, end
-age;age_min;no parameter in previous model;0;0;0;years (age);low;minimum age in the model 
-age;age_max;no parameter in previous model;120;120;120;years (age);low;maximum age in the model (should be clearly higher than maximum age in the population)
-general;round_rate;no parameter in previous model;4;4;4;digits (rate in percent per year);no parameter;rounding of rates (digits, rate in percent per year)
-general;round_prop;no parameter in previous model;4;4;4;digits (proportion in percent);no parameter;rounding of proportion (digits, proportion in percent)
-general;round_area;no parameter in previous model;4;4;4;digits (area);no parameter;rounding of area (e.g. ha in the capacity and reserve module)
-general;round_people;no parameter in previous model;0;0;0;digits (people);no parameter;rounding of people
-general;round_aca;no parameter in previous model;4;4;4;digits (people per apartment);no parameter;rounding of allocation (people per apartment)
-general;round_people_scen;no parameter in previous model;100;100;100;people (not digits);no parameter;"rounding of people (for future values; scenarios)"
-general;round_prop_scen;no parameter in previous model;0;0;0;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios)"
-general;round_prop_scen_a;no parameter in previous model;1;1;1;digits (proportion in percent);no parameter;"rounding of proportion (for future values; scenarios): comparison to previous scenarios by age"
-birth;bir_age_begin;geb.alter.beginn;15;15;15;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): begin
-birth;bir_age_end;geb.alter.ende;49;49;49;year;no parameter (strictly speaking);fertility rate: fertile age (fixed expression): end
-birth;bir_thres_origin;geb.anz.rate.heimat;100;100;100;persons;low;"age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the rate based on origin (but not district) is used.
-"
-birth;bir_thres_overall;geb.anz.rate.stadt;50;50;50;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then the overall rate is used (e.g not the rate based on origin and district).
-birth;bir_thres_const;geb.anz.rate.const;25;25;25;persons;low;age distribution of women (population): if there are less women than this threshold in the tails (cumulative measure), then a constant rate is used (e.g not the rate based on origin and district). The constant rate is set by the parameter bir_thres_value.
-birth;bir_thres_value;geb.wert.rate.const;0;0;0;birth per woman and year in %;low;birth rate: if there are less women than a certain threshold (bir_thres_const)  a constant fertility rate is used. The values is chosen with this parameter.
-birth;bir_fer_span;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
-birth;bir_prop_trend;geb.anteil.trend;20;20;20;percent;medium;"trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_thres_percent;geb.grenze.prozent;20;20;20;percent;low;"It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
-birth;bir_window_thres;geb.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
-birth;bir_lower_thres;no parameter in previous model;0;0;0;percent per year;low;lower threshold for the rate (e.g. the birth rate should not be negative), NA if there is no lower threshold
-birth;bir_upper_thres;no parameter in previous model;NA;NA;NA;percent per year;low;upper threshold for the rate, NA if there is no upper threshold
-birth;bir_plot_lim;no parameter in previous model;30;30;30;percent per year;no parameter;"the fertility rates of the past are compared with the predictions; the y-axis is limited (since the fertility rate of the past can be very large due to low population)"
-birth;bir_fer_span_pred;no parameter in previous model;0.15;0.15;0.15;no unit;low;"after the prediction: proportion of data points used in the LOESS regressions of fertility by age; groups: district, year, origin"
-birth;bir_cha_trend;geb.heimat.anteil.trend;0;0;0;percent;low;"origin change (mother-baby): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_cha_prop_trend;no parameter in previous model;20;20;20;percent;medium;"origin change (mother-baby): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-birth;bir_cha_thres_percent;no parameter in previous model;20;20;20;percent;low;"origin change (mother-baby): It is not realistic that the fertility rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential future fertility rates (change in percent of mean; e.g. +/- 20%)."
-birth;bir_cha_window_thres;no parameter in previous model;13;13;13;years;low;origin change (mother-baby): filter over years (to smooth a potential break in the curve due to range of future fertility rates: bir_thres_percent).
-birth;bir_cha_lower_thres;no parameter in previous model;0;0;0;percent;low;origin change (mother-baby): lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-birth;bir_cha_upper_thres;no parameter in previous model;100;100;100;percent;low;origin change (mother-baby): upper threshold for the rate (here: not more than 100 %), NA if there is no lower threshold
-death;dea_lower;tod.jung.alter.ende.schweiz, tod.jung.alter.ende.ausland;30;30;30;years (age);low;"lower age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
-death;dea_upper;tod.alt.alter.beginn.schweiz, tod.alt.alter.beginn.ausland;99;99;99;years (age);low;"upper age limit of age-dependent ratio (death rates of Zurich and entire Switzerland); comment: limit not included in range with age-dependence"
-death;dea_radix;no parameter in previous model;100000;100000;100000;persons;no;radix (starting population) for life expectancy calcuation (no effect on result)
-death;dea_age_at;no parameter in previous model;0;0;0;years (age);no;"life expectancy at certain age (usually at birth; i.e. age zero)"
-death;dea_age_max_le;tod.alt.alter.ende;120;120;120;years (age);low;maximum age for the estimation of the life expectancy
-death;dea_fso_cat_past;no parameter in previous model;2;2;2;no unit;no parameter;Category in the FSO data: (smoothed) data of the past
-death;dea_fso_cat_future;no parameter in previous model;3;3;3;no unit;no parameter;Category in the FSO data: data of the future
-death;dea_qx_NA_le;no parameter in previous model;0.8;0.8;0.8;per year;low;if there is no one at a certain age in the population (e.g. no 96 year old men), then qx (probability to die between age x and x+1) is NA. However, a value is need to multiply the subsequent survival probabilities
-death;dea_mor_span;no parameter in previous model;0.2;0.2;0.2;no unit;low;"proportion of data points used in the LOESS regressions of mortality by age; groups: sex, region (City of Zurich, Switzerland)"
-immigration*;ims_rate_prop_trend;zuz.rate.anteil.trend;20;20;20;percent;low;"immigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_rate_thres_percent;zuz.rate.grenze.prozent;20;20;20;percent;low;"It is not realistic that the immigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-immigration*;ims_rate_window_thres;zuz.rate.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future immigration* rates: imm_rate_thres_percent).
-immigration*;ims_rate_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-immigration*;ims_so_prop_trend;zuz.ghvert.anteil.trend;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_so_thres_percent;zuz.ghvert.grenze.prozent;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-immigration*;ims_so_window_thres;"zuz.ghvert.window.grenz
-";13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
-immigration*;ims_so_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-immigration*;ims_span_y;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the loess regressions of proportion by year; groups: district, age, sex, origin (immigration* by district, year, age, sex, origin)"
-immigration*;ims_span_a;no parameter in previous model;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
-immigration*;ims_age_prop_trend;no parameter in previous model;20;20;20;percent;low;"age distribution of immigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-immigration*;ims_age_thres_percent;no parameter in previous model;20;20;20;percent;low;"age distribution of immigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-immigration*;ims_age_window_thres;no parameter in previous model;13;13;13;years;low;age distribution of immigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
-immigration*;ims_age_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;age distribution of immigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-emigration*;ems_rate_prop_trend;weg.rate.anteil.trend;20;20;20;percent;low;"emigration rate*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_rate_thres_percent;weg.rate.grenze.prozent;20;20;20;percent;low;"It is not realistic that the emigration* rates changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-emigration*;ems_rate_window_thres;weg.rate.window.grenz;13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future emigration* rates: imm_rate_thres_percent).
-emigration*;ems_rate_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the rate (here: the rate should not be negative), NA if there is no lower threshold
-emigration*;ems_so_prop_trend;weg.ghvert.anteil.trend;20;20;20;percent;low;"proportion of sex and origin: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_so_thres_percent;weg.ghvert.grenze.prozent;20;20;20;percent;low;"It is not realistic that the proportions of sex and origin change dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-emigration*;ems_so_window_thres;"weg.ghvert.window.grenz
-";13;13;13;years;low;filter over years (to smooth a potential break in the curve due to range of future proportions
-emigration*;ems_so_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-emigration*;ems_span_y;no parameter in previous model;0.3;0.3;0.3;no unit;low;"proportion of data points used in the LOESS regressions of proportion by year; groups: district, age, sex, origin (emigration* by district, year, age, sex, origin)"
-emigration*;ems_span_a;no parameter in previous model;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, sex, origin (age proportion by district, year, sex, origin)"
-emigration*;ems_age_prop_trend;no parameter in previous model;20;20;20;percent;low;"age distribution of emigrations*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-emigration*;ems_age_thres_percent;no parameter in previous model;20;20;20;percent;low;"age distribution of emigrations*: It is not realistic that the proportion of age changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-emigration*;ems_age_window_thres;no parameter in previous model;13;13;13;years;low;age distribution of emigrations*: filter over years (to smooth a potential break in the curve due to range of future proportions
-emigration*;ems_age_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;age distribution of emigrations*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and immigration*;rei_age_max;umz.alter.max;70;70;70;years;low;as of this age: one single proportion (relocation on immigration*)
-relocation and immigration*;rei_ims_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, year, origin"
-relocation and immigration*;rei_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
-relocation and immigration*;rei_ims_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of immigration* by age; groups: district, origin"
-relocation and immigration*;rei_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
-relocation and immigration*;rei_ims_thres_y;similar to umz.anz.rate.stadt;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
-relocation and immigration*;rei_prop_span;similar to umz.window.alter;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
-relocation and immigration*;rei_prop_trend;umz.anteil.trend;20;20;20;percent;low;"relocation proportion on immigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-relocation and immigration*;rei_thres_percent;no parameter in previous model;20;20;20;percent;low;"relocation proportion on immigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-relocation and immigration*;rei_window_thres;no parameter in previous model;13;13;13;years;low;relocation proportion on immigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
-relocation and immigration*;rei_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and immigration*;rei_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;relocation proportion on immigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-relocation and immigration*;rei_pred_span;similar to umz.window.alter;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions of the prediction (proportion) by age; groups: district, year, origin"
-relocation and emigration*;ree_age_max;umz.alter.max;70;70;70;years;low;as of this age: one single proportion (relocation on emigration*)
-relocation and emigration*;ree_ems_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, year, origin"
-relocation and emigration*;ree_rel_span_dyao;similar to umz.window.alter;0.2;0.2;0.2;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, year, origin"
-relocation and emigration*;ree_ems_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of emigration* by age; groups: district, origin"
-relocation and emigration*;ree_rel_span_dao;similar to umz.window.alter;0.12;0.12;0.12;no unit;medium;"proportion of data points used in the LOESS regressions of relocation by age; groups: district, origin"
-relocation and emigration*;ree_ems_thres_y;similar to umz.anz.rate.heimat;0.4;0.4;0.4;persons per year;low;when immigration* is above this threshold: proportion with year (and district, age, origin), below without year (but with district, age, origin).
-relocation and emigration*;ree_prop_span;similar to umz.window.alter;0.3;0.3;0.3;no unit;medium;"proportion of data points used in the LOESS regressions of proportion by age; groups: district, year, origin"
-relocation and emigration*;ree_prop_trend;umz.anteil.trend;20;20;20;percent;low;"relocation proportion on emigration*: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-relocation and emigration*;ree_thres_percent;no parameter in previous model;20;20;20;percent;low;"relocation proportion on emigration*: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-relocation and emigration*;ree_window_thres;no parameter in previous model;13;13;13;years;low;relocation proportion on emigration*: filter over years (to smooth a potential break in the curve due to range of future proportions
-relocation and emigration*;ree_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-relocation and emigration*;ree_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;relocation proportion on emigration*: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-relocation and emigration*;ree_pred_span;similar to umz.window.alter;0.15;0.15;0.15;no unit;medium;"after expansion of the prediction to age beyond age threshold: proportion of data points used in the LOESS regressions the prediction (proportion) by age; groups: district, year, origin"
-naturalization;nat_pop_span_das;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: district, sex"
-naturalization;nat_nat_span_das;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: district, sex"
-naturalization;nat_pop_thres;similar to brw.rate.anz.null;0.5;0.5;0.5;persons;low;if less persons in the (smoothed) foreign population than this threshold: then the naturalization rate is set to nat_pop_value
-naturalization;nat_pop_value;no parameter in previous model;0;0;0;percent per year;low;value of the naturalization rate, if the foreign population is below nat_pop_thres
-naturalization;nat_rate_span_das;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, sex"
-naturalization;nat_pop_span_ya;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; groups: year (base years only)"
-naturalization;nat_nat_span_ya;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; groups: year (base years only)"
-naturalization;nat_rate_span_ya;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: year (base years only)"
-naturalization;nat_prop_trend;brw.anteil.trend;20;20;20;percent;low;"naturalization: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-naturalization;nat_thres_percent;no parameter in previous model;20;20;20;percent;low;"naturalization: It is not realistic that the rate changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential rates (change in percent of mean; e.g. +/- 20%)."
-naturalization;nat_window_thres;no parameter in previous model;13;13;13;years;low;naturalization: filter over years (to smooth a potential break in the curve due to range of future proportions
-naturalization;nat_lower_thres;no parameter in previous model;0;0;0;percent;low;naturalization: lower threshold for the proportions (here: the naturalization rates should not be negative), NA if there is no lower threshold
-naturalization;nat_pop_span_a;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of foreign population by age; no groups (base years only)"
-naturalization;nat_nat_span_a;similar to brw.window.alter;0.15;0.15;0.15;no unit;low;"proportion of data points used in the LOESS regressions of naturalization by age; no groups (base years only)"
-naturalization;nat_rate_span_a;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; no groups (base years only)"
-naturalization;nat_factor_thres;no parameter in previous model;0.5;0.5;0.5;naturalizations per year;low;if the naturalization rate is below this threshold: then the trend factor ist set to nat_factor_value
-naturalization;nat_factor_value;no parameter in previous model;0;0;0;no unit;low;value of the trend factor, if the naturalization rate is below nat_factor_thres
-naturalization;nat_rate_span_dyas;similar to brw.window.alter;0.1;0.1;0.1;no unit;low;"proportion of data points used in the LOESS regressions of the rate by age; groups: district, year, sex"
-capacity and reserves;car_sc;kareb.vf;25;25;25;percent;medium;"Proportion of staircases; for the conversion from total area to living area."
-capacity and reserves;car_resi;kareb.wohnant;-30;10;50;percent;high;Residence portion ('slider'): -100% = minimum residential share according to the building regulation (BZO), 0% = real residential share, +100% = maximum residential share according to building regulation.
-capacity and reserves;car_plot;kareb.areal;0;50;100;percent;high;"Proportion of plot construction ('slider'); 0% = without plot construction, 100% = with plot construction"
-capacity and reserves;car_uti_input;no parameter in previous model;85;85;85;percent;high;degree of utilization used to calculate the input data
-capacity and reserves;car_uti;kareb.ausbau;75;85;95;percent;high;"degree of utilization; linear influence on the capacity"
-capacity and reserves;car_pp;kareb.ina.prozentpunkte;0;0;0;percent points;medium;usage: less or more percentage points (parameter between -100 and +100)
-capacity and reserves;car_y;kareb.ina.jahr;2048;2048;2048;year;low;Reference year of the usage values
-capacity and reserves;car_lambda;kareb.ina.lambda;-0.04;-0.04;-0.04;per year;high;"lambda value of an exponential function exp(lambda * time since start of scenario); proportion of utilization per year"
-living space;spa_apart;wf.anzwohn;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
-living space;spa_prop_trend;wf.anteil.trend;0;20;50;percent;high;"living space: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-living space;spa_thres_percent;wf.grenze.prozent;20;20;20;percent;low;"living space: It is not realistic that the living space changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
-living space;spa_window_thres;wf.window.szen;7;7;7;years;low;living space: filter over years (to smooth a potential break in the curve due to range of future proportions
-living space;spa_prop_trend_52p;no parameter in previous model;0;0;0;percent;low;"living space in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-allocation;aca_apart;bq.anzwohn;500;500;500;apartments;low;"Amount of apartments per district and owner; if less apartments, then the value of the entire city (by owner) is used"
-allocation;aca_prop_trend;bq.anteil.trend;0;20;50;percent;high;"allocation: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-allocation;aca_thres_percent;bq.grenze.prozent;20;20;20;percent;low;"allocation: It is not realistic that the allocation values change dramatically (i.e. tenfold ncrease). Therefore, this threshold regulates the range of potential changes (change in percent of mean; e.g. +/- 20%)."
-allocation;aca_window_thres;bq.window.szen;7;7;7;years;low;allocation: filter over years (to smooth a potential break in the curve due to range of future proportions
-allocation;aca_prop_trend_52p;no parameter in previous model;0;0;0;percent;low;"allocation in the Escher Wyss district (district-ID = 52): trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-projects;pro_lambda_begin;map.lambda.beginn;3;3;3;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the beginning of the time period (of the consolidated project list)."
-projects;pro_lambda_end;map.lambda.ende;0.5;0.5;0.5;per year;low;"the time delay of the projects (consolidated project list) is determined by means of an exponential function (with lambda); value at the end of the time period (of the consolidated project list)."
-projects;pro_transfo;no parameter in previous model;5;5;5;no unit;low;transformation parameter (non-linear curve from pro_lambda_begin to pro_lambda_end)
-projects;pro_max_delay;no parameter in previous model;5;5;5;years;low;maximum delay
-projects;pro_not_scheduled_ip;part of map.nicht1 (projektiert);30;30;30;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, from Infoplan
-projects;pro_not_scheduled_other;part of map.nicht1 (projektiert);20;20;20;percent;high;Percentage of projects (i.e. apartments) that are not realized: scheduled, other
-projects;pro_not_submitted;map.nicht2 (eingereicht);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: submitted
-projects;pro_not_approved;map.nicht3 (bewilligt);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: approved
-projects;pro_not_started;map.nicht4 (Bau begonnen);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: started
-projects;pro_not_completed;map.nicht5 (fertiggestellt);0;0;0;percent;high;Percentage of projects (i.e. apartments) that are not realized: completed
-projects;pro_not_onhold;map.nicht6 (sistiert);10;10;10;percent;high;Percentage of projects (i.e. apartments) that are not realized: on hold
-ownership;own_prop_trend;ea.anteil.trend;50;50;50;percent;low;"ownership: trend in addition to the mean; 20% means, 20% of the difference between trend and mean is added to the mean."
-ownership;own_thres_percent;ea.grenze.prozent;20;20;20;percent;low;"ownership: It is not realistic that the proportion changes dramatically (i.e. tenfold increase). Therefore, this threshold regulates the range of potential proportions (change in percent of mean; e.g. +/- 20%)."
-ownership;own_window_thres;ea.window.szen;7;7;7;years;low;ownership: filter over years (to smooth a potential break in the curve due to range of future proportions
-ownership;own_lower_thres;no parameter in previous model;0;0;0;percent;no parameter;ownership: lower threshold for the proportions (here: the proportions should not be negative), NA if there is no lower threshold
-ownership;own_upper_thres;no parameter in previous model;100;100;100;percent;no parameter;ownership: lower threshold for the proportions (here: the proportions should not be more than 100 percent), NA if there is no lower threshold
-housing model;car_coop;similar to wohn.modell.typ;100;100;100;percent;low;Percentage of cooperative housing can be used from capacity/reserves (car_coop = 100), or from the trends in the districts (car_coop = 0) or a mixture of both data sources. In other words: car_coop = percentage used from capacity/reserves.
-housing model;car_trust;no parameter in previous model;0;0;0;percent;high;If the amount of people according to the project list exceeds the population (in a given year!) according to capacity/reserves: trust the capacity/reserves number (parameter = 100%)? Or the project list (parameter = 0%)?
-housing model;car_max_trust;no parameter in previous model;100;100;100;percent;high;If the amount of people exceeds the maximum (over all years!) according to capacity/reserves: trust the capacity/reserves population (parameter = 100%)? Or the calculated population (parameter = 0%)? Idea: not more people than population according to reserves usage
-housing model;empty_coop;similar to wohn.modell.anteil.leerwhg;0;0;0;percent;low;Percentage of empty apartments: cooperative housing
-housing model;empty_private;similar to wohn.modell.anteil.leerwhg;0;0;0;percent;low;Percentage of empty apartments: private housing
-demography and housing model;less_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);75;75;75;percent;high;less living space available (according to reserves, compared to trend calcuation). Then immigration* is decreased, and emigration* increased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
-demography and housing model;more_ims;similar to mod.ant.zuz (in the previous model only one parameter for both situations: less or more living space available);50;50;50;percent;high;more living space available (according to reserves, compared to trend calcuation). Then immigration* is increased, and emigration* decreased. This parameter determines how much of the difference is compensated by changes of immigration* (the remainder is corrected with emigration*)
-demography and housing model;deh_span;no parameter in previous model;0.04;0.04;0.04;no unit;medium;"in future population: proportion of data points used in the LOESS regressions by age; groups: district, year, sex, origin"
-output;basis_fact;no parameter in previous model;1;1;1;no unit;no parameter;value for field BasisSzenarienCd in DWH for factual values from the past
-output;basis_scen;no parameter in previous model;2;2;2;no unit;no parameter;value for field BasisSzenarienCd in DWH for calculated scenario values
diff --git a/data_freeze/2_Data/3_Parameter/variables.yml b/data_freeze/2_Data/3_Parameter/variables.yml
deleted file mode 100644
index e31dd47a..00000000
--- a/data_freeze/2_Data/3_Parameter/variables.yml
+++ /dev/null
@@ -1,134 +0,0 @@
-# plots -----------------------------------------------------------
-pdf_output: FALSE
-
-
-# Paths -----------------------------------------------------------
-# path for code
-code_path: !expr paste0(here::here(), "/1_Code/")
-
-# path for data
-data_path: !expr paste0(here::here(), "/2_Data/")
-
-# log file
-log_file: !expr paste0(here::here(), "/2_Data/6_Log/log.txt")
-
-# temporary path (since data not on open data yet)
-car_path: !expr paste0(here::here(), "/2_Data/1_Input/KaReB.csv")
-
-# path for results (graphics)
-res_path: !expr paste0(here::here(), "/3_Results/")
-
-# path for exports (rates)
-exp_path: "4_Rates/"
-
-# path for outputs (future: population and demographic processes)
-out_path: "5_Outputs/"
-
-# district file
-dis_file: "/2_Data/2_Lookup/lookupDistrict.csv"
-
-# parameter file
-para_file: "/2_Data/3_Parameter/parameter.csv"
-
-# output path for quarto book
-book_path: "/3_Results/book"
-
-
-# values -----------------------------------------------------------
-# capacity, reserves (car)
-car_initial: ["Kapazitaet", "Bestand", "Reserve", "Inanspruchnahme"]
-car_category: ["capacity", "buildings", "reserve", "usage"]
-
-# project status
-pro_initial: ["projektiert, Infoplan", "projektiert, andere", "eingereicht", "bewilligt", "Bau begonnen", "fertiggestellt", "sistiert"]
-pro_category: ["scheduled, Infoplan", "scheduled, other", "submitted", "approved", "construction started", "completed", "on hold"]
-
-
-# unique levels -----------------------------------------------------------
-
-# sex
-text_s: ["male", "female"]
-
-# origin
-text_o: ["Swiss", "foreign"]
-
-# region
-text_r: ["Zurich", "Switzerland"]
-
-# residence portion
-text_e: ["minimum portion", "real portion", "maximum portion"]
-
-# plot construction
-text_p: ["with plot construction", "without plot construction"]
-
-# property owner
-text_w: ["cooperative housing", "private housing"]
-
-# indicator (new or removed apartments)8
-text_i: ["new", "removed"]
-
-# past and scenarios
-text_c: ["past", "lower", "middle", "upper"]
-
-
-# categories (t = text) and associated lookup tables ----------------------
-# age category 1
-age_1: [30, 40]
-age_1t: ["0-29", "30-39", "40-49"]
-
-# age category 2
-age_2: !expr seq(25, 40, by = 5)
-age_2t: ["15-24", "25-29", "30-34", "35-39", "40-49"]
-
-# age category 3
-age_3: !expr seq(0, 90, by = 10)
-age_3t: ["0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+"]
-
-# age category 4
-age_4: !expr seq(0, 80, by = 20)
-age_4t: ["0-19", "20-39", "40-59", "60-79", "80+"]
-
-
-# colors, graphics --------------------------------------------------------
-# colors (e.g. for sex, origin)
-col_6: ["#005CA9", "#83072A", "#EB5E04", "#FBBA00", "#007229", "#9B9B9B"]
-
-# grey
-col_grey: "grey90"
-
-
-# open data (od) ----------------------------------------------------------
-
-# population (pop)
-pop_od: "https://data.stadt-zuerich.ch/dataset/80d5c8af-b389-41d2-b6d8-d0deb1639f00/resource/b2abdef7-3e3f-4883-8033-6787a1561987/download/bev390od3903.csv"
-
-# birth (bir)
-bir_od: "https://data.stadt-zuerich.ch/dataset/aef0654e-1691-49a2-b5fd-2fb220b78bfd/resource/6b066954-c9ce-4438-be0a-6ab01b3e525b/download/bev570od5702.csv"
-
-# death (dea)
-dea_od: "https://data.stadt-zuerich.ch/dataset/bev_todesfaelle_jahr_alter_geschlecht_herkunft_quartier_od5703/download/BEV570OD5703.csv"
-
-# death (dea, data of the Federal Statistical Office FSO)
-dea_fso_od: "https://data.stadt-zuerich.ch/dataset/bfs_bev_sterberaten_jahr_alter_geschlecht_herkunft_od5708/download/BEV570OD5708.csv"
-
-# immigration (imm)
-# migration in the City of Zurich; across the city border
-imm_od: "https://data.stadt-zuerich.ch/dataset/bev_zuz_jahr_quartier_alter_geschlecht_herkunft_od5704/download/BEV570OD5704.csv"
-
-# emigration (emi)
-# migration out of the City of Zurich; across the city border
-emi_od: "https://data.stadt-zuerich.ch/dataset/bev_wegz_jahr_quartier_alter_geschlecht_herkunft_od5705/download/BEV570OD5705.csv"
-
-# relocation (rel)
-# migration within the City of Zurich; inside the city border
-rel_od: "https://data.stadt-zuerich.ch/dataset/bev_umzuege_jahr_quartier_alter_geschlecht_herkunft_od5706/download/BEV570OD5706.csv"
-
-# naturalization (nat)
-# on the open data platform the data consist both of naturalization and denaturalization
-nat_od: "https://data.stadt-zuerich.ch/dataset/bev_brw_jahr_alter_geschlecht_herkunft_quartier_od5707/download/BEV570OD5707.csv"
-
-# living space (spa)
-spa_od: "https://data.stadt-zuerich.ch/dataset/bau_best_whg_geb_gebmwhg_wfl_pers_statzone_jahr_od6981/download/BAU698OD6981.csv"
-
-# population scenarios on open data (sce)
-sce_od: "https://data.stadt-zuerich.ch/dataset/bev_szenarien_od3440/download/BEV344OD3440.csv"