Working on TerraPower Impacts

1B_Run_Full_Simulation.r

@@ -13,6 +13,7 @@ source("Scripts/Load_Custom_Functions/Migration_Simulation_Functions.r")
 source("Scripts/Load_Custom_Functions/Birth_Simulation_Functions.r")
 source("Scripts/Load_Custom_Functions/Increment_Data_Year.r")
 source("Scripts/Load_Custom_Functions/Single_Age_Mortality_Trend_Simulation.r")
+source("Scripts/Load_Custom_Functions/Induced_Migration_Functions.r")
 
 #######Preliminary Model Inputs
 YEARS_AHEAD <- 43
@@ -26,6 +27,11 @@ BIRTH_DATA <- readRDS("Data/Intermediate_Inputs/Birth_Regressions/Regression_Dat
 
 MIGRATION_ARIMA <- readRDS("Data/Intermediate_Inputs/Migration_ARIMA_Models/Kemmerer_Diamondville_Net_Migration_ARIMA.Rds")
 MIGRATION_ODDS <- readRDS("Data/Intermediate_Inputs/Migration_Trends/Migration_Age_Probability_Zero_to_85.Rds")
+####
+OPERATORS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Operating_Worker_Related_Migration.Rds")
+CONSTRUCTION <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Construction_Related_Migration.Rds")
+INDUCED_MIGRATION_MULTIPLIERS <- readRDS("Data/Cleaned_Data/TerraPower_Impact/Induced_Jobs.Rds")
+
 ##############
 	#Data for death rate trends
 	SINGLE_AGE_MODS <- readRDS("Data/Intermediate_Inputs/Mortality_Regression_Data/Single_Sex_Age_Time_Series_Regression.Rds")
@@ -83,12 +89,24 @@ TOTAL_POP  <-  sum(DEMO)
 return(list(DEMO,BIRTH_DATA,c(TOTAL_POP,TOTAL_BIRTHS,TOTAL_DEATHS,TOTAL_MIGRATION)))
 }
 MIGRATION_ARIMA_MODEL <- MIGRATION_ARIMA
-SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL){
+SINGLE_SIM <- function(DEMO,BIRTH_DATA,ST_YEAR,YEARS_AHEAD,MIGRATION_ARIMA_MODEL,OPERATOR_TOTAL,CONSTRUCTION_TOTAL,MIGRATION_MULTIPLIERS ){
-	TERRA_POWER_EFFECT <-	rep(0,YEARS_AHEAD)
+
-	POP_WORK_RATIO <-3716/1920.54 #Total population of Kemmerer in 2024 divided total employment both are found in IMPLAN region details for zip code 83101
-	TERRA_POWER_EFFECT[3:7] <- POP_WORK_RATIO*310.75/5 #Total IMPLAN job estimate times adjusted for families and spread over five years
 	
-	MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL)+runif(1,-55,0))+TERRA_POWER_EFFECT)
+	TERRA_POWER_EFFECT <-	rep(0,YEARS_AHEAD)
+	OPERATOR_MIGRATION <-OPERATOR_TOTAL%>% pull("Operator_Emp_Migrated")
+	CONSTRUCTION_MIGRATION <- CONSTRUCTION_TOTAL%>% pull("Construction_Emp_Migrated")
+	OPERATOR_MIGRATION <- LOCAL_WORK_ADJ(OPERATOR_MIGRATION ,0.85) #Assume between 85%-100% operators live in Kemmerer
+	CONSTRUCTION_MIGRATION <- LOCAL_WORK_ADJ(CONSTRUCTION_MIGRATION,0.41) #Assume between 41%-100% operators live in Kemmerer
+
+	OPERATOR_MIGRATION <- OPERATOR_MIGRATION %>% pull("Operator_Emp_Migrated")
+	CONSTRUCTION_MIGRATION <- CONSTRUCTION_MIGRATION %>% pull("Construction_Emp_Migrated")
+	CONSTRUCTION_POPULATION_ADDED <- cumsum(CONSTRUCTION_MIGRATION)
+
+	PERMANENT_TERRAPOWER_MIGRATION <- INDUCED_SIMULATION(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,RES)+OPERATOR_MIGRATION
+TEMP_TERRAPOWER_MIGRATION_<- TERRA_POWER_EFFECT+CONSTRUCTION_MIGRATION 
+
+	
+	MIGRATION_SIM_VALUES <- round(as.vector(simulate(nsim=YEARS_AHEAD,MIGRATION_ARIMA_MODEL)+runif(1,-55,0))+PERMANENT_TERRAPOWER_MIGRATION)
 	#The runif applies a downshift ranging from the historic decline rate all the way to the Lincoln rate applied in the model
 	
 	FINAL_REPORT_VALUES <- matrix(NA,ncol=6,nrow=YEARS_AHEAD)

Data/Raw_Data/Mortality_Rates_Over_Time/README_MORTALITY_DATA.txt

@@ -10,4 +10,4 @@ Data is manually gathered from CDC WONDER data queries.
  4) The world pandemic uncertainty index as collected from FRED which is used to account for pandemics in the regression, making the age time series stationary.
  
  These are used to project mortality trends over time. In the case of the age adjusted data, this has local trends that can be compared to the national average. The single age-sex data is only at a national level but can be imparted to local levels as a general trend in the distribution of deaths 
---- Run Date: 2025-12-03 14:11:57 ---
+--- Run Date: 2025-12-05 19:32:50 ---

Prelim_Process.sh

@@ -5,6 +5,8 @@ Rscript "./Scripts/1C_Download_and_Process_Demographic_Data.r"
 Rscript "./Scripts/1D_Use_ACS_Census_Data_to_Estimate_Kemmerer_Demographics.r"
 Rscript "./Scripts/1E_Process_WONDER_Mortality_Data.r" 
 Rscript "./Scripts/1F_Process_WONDER_Single_Age_Sex_Mortality_Data.r"
+Rscript "./Scripts/1G_Terra_Power_Migration_Rates.r"
+
 
 #Create data sets used in later simulations, produce some results for the report when related to this process.
 Rscript "./Scripts/2A_Birth_Rate_Regression_and_Impart_Kemmerer_Births.r"

Scripts/1G_Terra_Power_Migration_Rates.r

@@ -0,0 +1,82 @@
+library(tidyverse)
+#setwd("../")
+PERM <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Operations_Workforce.csv")
+NUM_YEARS <- ceiling(nrow(PERM)/12)
+DATES <- as.Date(paste0(2025,"-",8:12,"-",18))
+for(YEAR in 2026:(2026+NUM_YEARS)){
+	DATES <- c(DATES,as.Date(paste0(YEAR,"-",1:12,"-",18)))
+}
+PERM$Date <- DATES[1:nrow(PERM)]
+PERM$Year <- year(PERM$Date)
+PERM$Add_Emp <- c(0,diff(PERM$In_Migration))
+PERM$Perm_Migration_Including_Families <- PERM[,"In_Migration"]*0.8*3.05 #TerraPower assumes 80% have families and the average family is 3.2 people, but in wyoming the average is stated as 3.05
+OPERATOR <- PERM %>% group_by(Year) %>% summarize(Operator_Emp_Average =mean(In_Migration),Operator_Emp_Migrated=sum(Add_Emp),Total_Op_and_Fam_Migration=max(Perm_Migration_Including_Families))
+OPERATOR[,"Total_Op_and_Fam_Migration"] <- c(0,diff(round(pull(OPERATOR,"Total_Op_and_Fam_Migration"))))
+
+
+TEMP <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Construction_Workforce.csv")
+TEMP$Date <- DATES[1:nrow(TEMP)]
+TEMP$Year <- year(TEMP$Date)
+TEMP[,1] <- 0.41*TEMP[,1] #TerraPower assumes 41% migrate into Lincoln
+TEMP$Add_Emp <- c(0,diff(TEMP$In_Migration))
+TEMP <-	TEMP %>% group_by(Year) %>% summarize(Temp_Emp_Migration=round(sum(Add_Emp)),Average_Temp_Workers=round(mean(In_Migration) ))
+TEMP[,"Total_Migration"] <- round(TEMP[,2]+TEMP[,2]*0.37*3.05) #TerraPower assumes 37% will bring families and the average family is 3.2 people, but states Wyoming averages 3.05 family save
+CONSTRUCTION <-TEMP %>% select(Year,Construction_Emp_Average=Average_Temp_Workers,Construction_Emp_Migrated=Temp_Emp_Migration,Total_Con_and_Fam_Migration=Total_Migration)
+TEMP$End_Year_Temp_Workers <- c(cumsum(TEMP$Temp_Emp_Migration))
+
+#Set the total to zero
+	#Employment
+CONSTRUCTION[4:7,3] <- CONSTRUCTION[4:7,3] -1
+CONSTRUCTION[6:7,3] <- CONSTRUCTION[6:7,3] -1
+	#Family Migration
+CONSTRUCTION[4:7,4] <- CONSTRUCTION[4:7,4] -3
+#colSums(CONSTRUCTION[,3:4])
+#colSums(OPERATOR[,3:4])
+if(!exists("SAVE_LOC")){SAVE_LOC <-"./Data/Cleaned_Data/TerraPower_Impact/"}
+	dir.create(SAVE_LOC, recursive = TRUE, showWarnings = FALSE)
+saveRDS(CONSTRUCTION,paste0(SAVE_LOC,"Construction_Related_Migration.Rds"))
+saveRDS(OPERATOR,paste0(SAVE_LOC,"Operating_Worker_Related_Migration.Rds"))
+#####Double check that Kemmere can house all people
+#1,451 tota units required acording to TerraPower
+REQUIRED_IN_LIN <- 1451*0.41
+TOTAL_IN_KEM <- 333 #According to ACS data
+EXPECTED_NEW_IN_KEM <- 750 #According to TerraPower Kemmerer is agressively zoning and will conservativley add 750 houses available to the project
+(0.75*TOTAL_IN_KEM+750)/REQUIRED_IN_LIN #Shows a surplus of houses in Kemmerer
+############################################################TerraPower IMPLAN estimates
+CLEAN_IMPLAN <- function(DF){
+	DF <- DF[,-1]
+	COLNAMES <- c("Industry","Direct","Indirect","Induced","Total")
+	colnames(DF) <- COLNAMES 
+	DF<- DF %>% filter(!is.na(Industry))
+	DF[,1] <- trimws(str_replace_all(str_replace_all(t(DF[,1]), "[:digit:]|-|  |A |B ", ""),"  ",""))
+	return(DF)
+}
+
+KEM_CON_IMPLAN <- CLEAN_IMPLAN(read_csv("Data/Raw_Data/IMPLAN_Employment_Outputs_2_Difit_NAICS/Kemmerer_100_Construction_Workers.csv"))
+LIN_CON_IMPLAN <- CLEAN_IMPLAN(read_csv("Data/Raw_Data/IMPLAN_Employment_Outputs_2_Difit_NAICS/Lincoln_100_Construction_Workers.csv"))
+CON_DIFF <- LIN_CON_IMPLAN %>% mutate(LIN_CON_TOTAL=Total-Direct) %>% select(Industry,LIN_CON_TOTAL) %>% full_join(KEM_CON_IMPLAN %>% mutate(KEM_CON_TOTAL=Total-Direct)  %>% select(Industry,KEM_CON_TOTAL))
+CON_DIFF[,"Gap"] <- CON_DIFF[,2]-CON_DIFF[,3]
+CON_DIFF %>% print(n=100)
+CON_DIFF$Adjustable_Industry <- CON_DIFF$Industry %in% c('Accommodation and Food Services','Retail Trade','Health Care and Social Assistance','Real Estate and Rental and Leasing','Arts, Entertainment, and Recreation','Manufacturing','Other Services (except Public Administration)')
+CON_DIFF[which(CON_DIFF$Adjustable_Industry),]
+CONSTRUCTION_ADDED_JOBS <- CON_DIFF %>%  mutate(KEM_POSSIBLE_INDUCED=Gap*Adjustable_Industry) %>% select(KEM_INDUCED= KEM_CON_TOTAL,KEM_POSSIBLE_INDUCED)
+CONSTRUCTION_ADDED_JOBS <-  colSums(CONSTRUCTION_ADDED_JOBS)/100
+
+
+KEM_OP_IMPLAN <- CLEAN_IMPLAN(read_csv("Data/Raw_Data/IMPLAN_Employment_Outputs_2_Difit_NAICS/Kemmerer_100_Nuclear_Operators.csv"))
+LIN_OP_IMPLAN <- CLEAN_IMPLAN(read_csv("Data/Raw_Data/IMPLAN_Employment_Outputs_2_Difit_NAICS/Lincoln_100_Nuclear_Operators.csv"))
+OP_DIFF <- LIN_OP_IMPLAN %>% mutate(LIN_OP_TOTAL=Total-Direct) %>% select(Industry,LIN_OP_TOTAL) %>% full_join(KEM_OP_IMPLAN %>% mutate(KEM_OP_TOTAL=Total-Direct)  %>% select(Industry,KEM_OP_TOTAL))
+OP_DIFF 
+OP_DIFF[,"Gap"] <- OP_DIFF[,2]-OP_DIFF[,3]
+OP_DIFF$Adjustable_Industry <- OP_DIFF$Industry %in% c('Accommodation and Food Services','Retail Trade','Health Care and Social Assistance','Real Estate and Rental and Leasing','Arts, Entertainment, and Recreation','Manufacturing','Other Services (except Public Administration)')
+OP_DIFF 
+OPERATION_ADDED_JOBS <- OP_DIFF %>%  mutate(KEM_POSSIBLE_INDUCED=Gap*Adjustable_Industry) %>% select(KEM_INDUCED= KEM_OP_TOTAL,KEM_POSSIBLE_INDUCED) 
+OPERATION_ADDED_JOBS <-  colSums(OPERATION_ADDED_JOBS )/100
+
+
+RES <- rbind(CONSTRUCTION_ADDED_JOBS,OPERATION_ADDED_JOBS) %>% as_tibble
+POP_WORK_RATIO <-3716/1920.54 #Total population of Kemmerer in 2024 divided total employment both are found in IMPLAN region details for zip code 83101
+RES*POP_WORK_RATIO #Total family included migration, converted per person (rather than per 100 jobs)
+RES$Job_Type <- c("Construction","Operator")
+RES <- RES[,c(3,1:2)]
+saveRDS(RES,paste0(SAVE_LOC,"Induced_Jobs.Rds"))

Scripts/2A_Birth_Rate_Regression_and_Impart_Kemmerer_Births.r

@@ -97,17 +97,19 @@ try(etable(MOD_VIEW_BIRTHS,MOD_VIEW_BIRTHS_2016,MOD_VIEW_BIRTHS_1985,headers=HEA
 
 
 
-
+TEMP <- REG_REDUCED_DATA
+TEMP$RESID <- resid(MOD_BIRTHS )
 #Kemmerer ACF/PACF
-C_TEMP <- TEMP %>% filter(Region=='Kemmerer & Diamondville') %>% arrange(Year)
+C_TEMP <- TEMP %>% filter(KEM==1) %>% arrange(Year)
     png(paste0(SAVE_FIG_LOC,"/Kemmerer_ACF.png"), width = 12, height = 8, units = "in", res = 600)
 	acf(C_TEMP$RESID,main='ACF of Kemmerer & Diamondville Birth Estimate Residuals',xlab="Lag (Years)")
+
      dev.off()
      png(paste0(SAVE_FIG_LOC,"/Kemmerer_PACF.png"), width = 12, height = 8, units = "in", res = 600)
 	pacf(C_TEMP$RESID,main='PACF of Kemmerer & Diamondville Birth Estimate Residuals',xlab="Lag (Years)")
      dev.off()
 #Lincoln total ACF/PACF
-C_TEMP <- TEMP %>% filter(Region=='Lincoln') %>% arrange(Year)
+C_TEMP <- TEMP %>% filter(Region=='Lincoln',KEM==0) %>% arrange(Year)
     png(paste0(SAVE_FIG_LOC,"/Lincoln_ACF.png"), width = 12, height = 8, units = "in", res = 600)
 	acf(C_TEMP$RESID,main='ACF of Lincoln County Birth Estimate Residuals',xlab="Lag (Years)")
     dev.off()
@@ -115,7 +117,7 @@ C_TEMP <- TEMP %>% filter(Region=='Lincoln') %>% arrange(Year)
 	pacf(C_TEMP$RESID,main='PACF of Lincoln County Birth Estimate Residuals',xlab="Lag (Years)")
     dev.off()
 #Lincoln Other (Not Kemmerer) ACF/PACF
-C_TEMP <- TEMP %>% filter(Region=='Lincoln_Other') %>% arrange(Year)
+C_TEMP <- TEMP %>% filter(Region=='Lincoln_Other',KEM==0) %>% arrange(Year)
     png(paste0(SAVE_FIG_LOC,"/Lincoln_Other_Areas_ACF.png"), width = 12, height = 8, units = "in", res = 600)
 	acf(C_TEMP$RESID,main='ACF of Other Parts of Lincoln County Birth Estimate Residuals',xlab="Lag (Years)")
     dev.off()
@@ -124,7 +126,8 @@ C_TEMP <- TEMP %>% filter(Region=='Lincoln_Other') %>% arrange(Year)
     dev.off()
 
 ####Create data stubs to start a simulation. That is predict the births from this most recent year. Include records from various years of potential interest
-ST_REG_DATA <- REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==max(Year)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Kemmerer & Diamondville') %>% filter(Year==max(Year))) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln_Other') %>% filter(Year==max(Year))) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Kemmerer & Diamondville') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln_Other') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==1985)) %>% rbind(REG_REDUCED_DATA %>% filter(KEM==1) %>% filter(Year==max(Year)))
+
+ST_REG_DATA <- REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==max(Year)) %>% rbind(REG_REDUCED_DATA %>% filter(KEM==1) %>% filter(Year==max(Year))) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln_Other') %>% filter(Year==max(Year))) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Kemmerer & Diamondville') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln_Other') %>% filter(Year==2016)) %>% rbind(REG_REDUCED_DATA %>% filter(Region=='Lincoln') %>% filter(Year==1985)) %>% rbind(REG_REDUCED_DATA %>% filter(KEM==1) %>% filter(Year==max(Year)))  %>% unique %>% arrange(KEM,Year)
 
   if(!exists("SAVE_REG_LOC")){SAVE_REG_LOC <- "Data/Intermediate_Inputs/Birth_Regressions"}
  dir.create(SAVE_REG_LOC , recursive = TRUE, showWarnings = FALSE)

Scripts/Load_Custom_Functions/Induced_Migration_Functions.r

@@ -0,0 +1,20 @@
+#Takes the added jobs table and downshift for people living outside of Kemmerer but in Lincoln
+LOCAL_WORK_ADJ <- function(DF,MIN_LOCAL,MAX_LOCAL=1){
+
+	DF[,-1]<- runif(1,MIN_LOCAL,MAX_LOCAL)*DF[,-1]#Random range people choosing to live outside of Kemmerer  assumed to be between 85% and 100%
+	DF[,-1:-2] <-round(DF[,-1:-2])
+	return(DF)
+}
+#Find the expected total of new induced jobs from TerraPower (Includes construction entering and leaving, and operators entering)
+INDUCED_SIMULATION <- function(CONSTRUCTION_MIGRATION,OPERATOR_MIGRATION,INDUCED_MIGRATION_TABLE){
+	ADDED_FROM_BASELINE <- runif(1) #The percentage of the possible growth in industries like restaurants to add compared to the Kemmerer IMPLAN model which understates possible structural growth 
+
+	EST_CONST_INDUCED <- round(CONSTRUCTION_MIGRATION* as.numeric(INDUCED_MIGRATION_TABLE[INDUCED_MIGRATION_TABLE$Job_Type=="Construction",2]+ADDED_FROM_BASELINE *INDUCED_MIGRATION_TABLE[INDUCED_MIGRATION_TABLE$Job_Type=="Construction",3]))
+
+	EST_CONST_INDUCED[7] <-  EST_CONST_INDUCED[7]-sum(EST_CONST_INDUCED) #Make sure the sums are still zero after rounding takes place, if not make the last year make up the difference
+#########Induced migration from operating
+EST_OP_INDUCED <- round(OPERATOR_MIGRATION * as.numeric(INDUCED_MIGRATION_TABLE[INDUCED_MIGRATION_TABLE$Job_Type=="Operator",2]+ADDED_FROM_BASELINE*INDUCED_MIGRATION_TABLE[INDUCED_MIGRATION_TABLE$Job_Type=="Operator",3]))
+INDUCED <- EST_OP_INDUCED +EST_CONST_INDUCED
+return(INDUCED)
+}
+

TerraPower_Values.r

@@ -1,34 +0,0 @@
-library(tidyverse)
-PERM <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Operations_Workforce.csv")
-NUM_YEARS <- ceiling(nrow(PERM)/12)
-c(8:12,rep(1:12,ceiling(nrow(PERM)/12)))[1:nrow(PERM)]
-DATES <- as.Date(paste0(2025,"-",8:12,"-",18))
-for(YEAR in 2026:(2026+NUM_YEARS)){
-	DATES <- c(DATES,as.Date(paste0(YEAR,"-",1:12,"-",18)))
-}
-PERM$Date <- DATES[1:nrow(PERM)]
-PERM$Year <- year(PERM$Date)
-PERM$Add_Emp <- c(0,diff(PERM$In_Migration))
-PERM %>% group_by(Year) %>% mutate(Total_Wages=Average_Wages_Present_USD*In_Migration) %>% group_by(Year) %>% summarize(Total_Wages=sum(Total_Wages),Total_Wages=sum(ifelse(Year==2031,Total_Wages*3,Total_Wages)))
-%>% summarize(Perm_Emp_Migration=sum(Add_Emp),Total_Wages=sum(Average_Wages_Present_USD*In_Migration),Current_Perm_Workers=round(mean(In_Migration) ))
-
-PERM[7,3]<-PERM[7,3]*3 #4 months (1/3) year only seen in sample
-PERM[,"Total_Migration"] <- round(PERM[,2]+PERM[,2]*0.8*3.05) #TerraPower assumes 80% have families and the average family is 3.2 people, but in wyoming the average is stated as 3.05
-PERM
-TEST <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Operations_Workforce.csv")
-TEST[59,]*12
-
-TEMP <- read_csv("./Data/Raw_Data/TerraPower_Report_Data/Monthly_In_Migration_Construction_Workforce.csv")
-TEMP[,1] <- TEMP[,1]*0.41 #TerraPower assumes 41% migrate into Lincoln
-TEMP[,1] <- TEMP[,1]*0.80 #I assume 80% of lincoln will be in Kemmerer
-TEMP$Date <- DATES[1:nrow(TEMP)]
-TEMP$Year <- year(TEMP$Date)
-TEMP$Add_Emp <- c(0,diff(TEMP$In_Migration))
-TEMP <- TEMP %>% group_by(Year) %>% summarize(Temp_Emp_Migration=sum(Add_Emp),Total_Wages=sum(Average_Wages_Present_USD*In_Migration),Current_Temp_Workers=round(mean(In_Migration) ))
-TEMP[,"Total_Migration"] <- round(TEMP[,2]+TEMP[,2]*0.37*3.05) #TerraPower assumes 37% will bring families and the average family is 3.2 people, but states Wyoming averages 3.05 family save
-
-TEMP[,2] <- round(TEMP[,2])
-TEMP[4:7,2] <- TEMP[4:7,2]-1
-TEMP[7,2] <- TEMP[7,2]-1
-TEMP[4:7,5] <- TEMP[4:7,5] -2
-TEMP[7,5] <- TEMP[7,5] -1