Adding in IMPLAN results

ARMA_Pop.r

@@ -4,59 +4,66 @@ library(lmtest)
 
 source("Scripts/Functions.r")
 #source("Scripts/Load_Wyoming_Web_Data.r")
-
 DF <- FRED_GET('WYLINC3POP','LN_POP') %>% select(-YEAR)
-
 TS <- 1000*ts(DF,start=c(1970),end=c(2024),frequency=1)
 BC <- BoxCox.lambda(TS)
 MODEL <- auto.arima(TS, lambda = BC)
 forecast(MODEL,h=20)
-ARMA_POP <- forecast(MODEL,h=35,fan=TRUE,bootstrap = TRUE,npaths=100000,biasadj=FALSE)
+ARMA_POP <- forecast(MODEL,h=35,fan=TRUE,bootstrap = FALSE,biasadj=FALSE)
-ARMA_POP
 plot(ARMA_POP,main="Lincoln County Population Forecast",xlab="Year",ylab="Population")
-help("forecast")
-####Employment to pop ratio
-EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP)
-2900/3300
-EMP <- EMP %>% mutate(RATIO=LN_POP/EMP)
-ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line()
-AVG_POP_RATIO <- mean(EMP$RATIO)
-SD_POP_RATIO <- sd(EMP$RATIO)
 #####City level
 	#See data http://eadiv.state.wy.us/pop/
-KEM <-  c(3273,3523,3688,3667,3626,3637,3611,3388,3156,3040,3020,3029,2989,2959,2976,2963,2910,2807,2729,2690,2657,2608,2575,2561,2574,2579,2603,2640,2679,2692,2642,2597,2551,2575,2578,2554,2544,2499,2457,2435,2413,2445,2445,2404,2378)
+	KEM <-  c(3273,3523,3688,3667,3626,3637,3611,3388,3156,3040,3020,3029,2989,2959,2976,2963,2910,2807,2729,2690,2657,2608,2575,2561,2574,2579,2603,2640,2679,2692,2642,2597,2551,2575,2578,2554,2544,2499,2457,2435,2413,2445,2445,2404,2378)
-DIAMOND <- c(1000,1070,1114,1101,1082,1078,1063,991,916,876,864,863,847,835,835,827,808,774,748,732,705,695,690,689,695,700,710,723,738,745,731,704,677,667,652,629,613,586,559,540,523,526,527,521,517)
+	DIAMOND <- c(1000,1070,1114,1101,1082,1078,1063,991,916,876,864,863,847,835,835,827,808,774,748,732,705,695,690,689,695,700,710,723,738,745,731,704,677,667,652,629,613,586,559,540,523,526,527,521,517)
-AREA_POP <- KEM+DIAMOND
+	AREA_POP <- KEM+DIAMOND
-LN <- c(12177,13254,14031,14110,14111,14319,14384,13658,12875,12552,12625,12975,13124,13329,13759,14073,14206,14099,14114,14338,14621,14697,14858,15117,15539,15917,16429,17013,17629,18082,18083,17946,17822,18148,18346,18473,18766,18899,19042,19379,19658,20174,20690,20909,21000)
+	LN <- c(12177,13254,14031,14110,14111,14319,14384,13658,12875,12552,12625,12975,13124,13329,13759,14073,14206,14099,14114,14338,14621,14697,14858,15117,15539,15917,16429,17013,17629,18082,18083,17946,17822,18148,18346,18473,18766,18899,19042,19379,19658,20174,20690,20909,21000)
-NO_CITY <-  c(10095,10392,10747,10944,11043)
+	NO_CITY <-  c(10095,10392,10747,10944,11043)
-YEAR <- 1980:2024
+	YEAR <- 1980:2024
-DATA <- cbind(YEAR,LN,AREA_POP) %>% as_tibble %>% rename("Lincoln County"=LN,"Kemmerer & Diamondvile"=AREA_POP)
+####Old data addtion:Period Ends in 1970
-
-
-#Old daata:Period Ends in 1970
 	#See in part http://eadiv.state.wy.us/demog_data/cntycity_hist.htm
-LN_OLD <- c(12487,10894,10286,9023,9018,8640)
+	LN_OLD <- c(12487,10894,10286,9023,9018,8640)
-LN_OLD  <- cbind(seq(1920,1970,by=10),LN_OLD) %>% as_tibble
+	KEM_OLD <- c(843,1517,1884,2026,1667,2028,2292)
-colnames(LN_OLD)  <- c("YEAR","LN_POP")
+	DIAMOND_OLD <- c(696,726,812,586,415,398,485)
-
+	AREA_OLD <- KEM_OLD+DIAMOND_OLD
-KEM_OLD <- c(843,1517,1884,2026,1667,2028,2292)
+	AREA2 <- c(AREA_OLD,AREA_POP)
-KEM_OLD  <- cbind(seq(1910,1970,by=10),KEM_OLD) %>% as_tibble
+	LN2  <- c(NA,LN_OLD,LN)
-colnames(KEM_OLD)  <- c("YEAR","KEM_POP")
+	YEAR2 <-  c(seq(1910,1980,by=10),1981:2024)
-
+	A <- cbind(YEAR2,LN2) %>% as_tibble %>% rename(Population=LN2) %>% mutate(Region='Lincoln County')
-DIAMOND_OLD <- c(696,726,812,586,415,398,485)
+	B <- cbind(YEAR2,AREA2) %>% as_tibble %>% rename(Population=AREA2) %>% mutate(Region='Kemmerer & Diamondvile')
-DIAMOND_OLD <- cbind(seq(1910,1970,by=10),DIAMOND_OLD) %>% as_tibble
+	DATA <- rbind(A,B) %>% rename(Year=YEAR2)
-colnames(DIAMOND_OLD)  <- c("YEAR","DIA_POP")
+	ggplot(aes(x=Year,y=Population,group=Region,color=Region),data=DATA2) +geom_line(linewidth=1.5)
-OLD_DATA <- inner_join(KEM_OLD,DIAMOND_OLD) %>% full_join(LN_OLD)
+###Kemmerer ARMA
+	KEM_TS <- DATA %>% filter(Year>=1980,Region=='Kemmerer & Diamondvile') %>% pull(Population) %>% ts(start=c(1980),end=c(2024),frequency=1)
+	BC <- BoxCox.lambda(KEM_TS)
+	MODEL2 <- auto.arima(KEM_TS, lambda = BC)
+	ARMA_KEM  <- 	forecast(MODEL2,fan=TRUE,h=40,biasadj=FALSE)
+	plot(ARMA_KEM,main="Kemmerer & Diamondvile Population",xlab="Year",ylab="Population",ylim=c(0,6000))
+###IMPLAN adjustment
+	#TerraPower: Commuting SAM
+LOW_EMP <- 310.75 #Employees
+	#TerraPower: No commuting
+HIGH_EMP <- 325 #Employees
+	#Employment in Licolin
+LIN_EMP <- 625
+	#Regional (zip code level) ratios
+EMPLOYMENT <-	2425.84
+HOUSEHOLDS <- 1850.20 
+POPULATION <- 4038 
+	#Half of Lincolin Employment in some low industried can be added to Kemmerer effect. For example restruants may be scaled based on current output, when new ones will be built. 
+ADJ_LOCAL_EMP <- 78.15 
 
 
+#85% commute from OUTSIDE Kemmer
+#15% local labor
 
-
+RATIO  <- POPULATION/EMPLOYMENT
-GRAPH_DATA <- ts(DATA %>% select(-YEAR),start=c(1980),end=c(2024),frequency=1)
+#Note that number in household will be much larger. This acounts for average commuting rates by looking at population compared to employment
-png("Population.png")
+LOW_GROWTH <- RATIO*LOW_EMP
-plot(GRAPH_DATA ,main="Regional Population Trends",type="b",lwd=4,col="blue")
+RATIO*HIGH_EMP
-dev.off()
+MID_GROWTH <- RATIO*(LOW_EMP+ADJ_LOCAL_EMP)
-lines(GRAPH_DATA,col="blue")
+HIGH_GROWTH <- RATIO*(HIGH_EMP+ADJ_LOCAL_EMP)
-?plot
+MID_GROWTH
+HIGH_GROWTH
 
 ##############
 TS <- EMP %>% select(EMP) %>% ts(start=c(1990),end=c(2024),frequency=1)