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all_pov_script.R
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all_pov_script.R
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packages<-c("cowplot", "dplyr", "geosphere", "ggplot2", "ggExtra", "maps", "maptools", "readxl",
"rgdal", "rgeos", "sf", "sp", "spatialreg", "spdep", "stringr","tidyr", "viridis")
sapply(packages, require, character.only=T)
#The Data
se.data <- read.csv("./Data/childpov18_southfull.csv",
colClasses = c("character", "character", "character",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
#Format Variables
names(se.data)[names(se.data)=="X2016.child.poverty"] <- "child.pov.2016"
se.data$FIPS <- str_pad(se.data$FIPS, 5, "left", pad = 0)
#create subsets
satl.data <- subset(se.data, State %in% c("DE", "DC", "FL", "GA", "MD", "NC", "SC", "VA", "WV"))
esc.data <- subset(se.data, State %in% c("AL", "KY", "MS", "TN"))
wsc.data <- subset(se.data, State %in% c("AR", "LA", "OK", "TX"))
#creating counties from shapefiles
se.shape<-readOGR(dsn="./Shapefiles",layer="SE_Counties_2016", stringsAsFactors = FALSE)
se.shape@data <- se.shape@data[-c(6:54,57:63)]
names(se.shape@polygons) <- se.shape@data$FIPS
satl.shape <- subset(se.shape, STATE_NAME %in% c("Delaware", "District of Columbia", "Florida",
"Georgia", "Maryland", "North Carolina", "South Carolina",
"Virginia", "West Virginia"))
esc.shape <- subset(se.shape, STATE_NAME %in% c("Alabama", "Kentucky", "Mississippi",
"Tennessee"))
wsc.shape <- subset(se.shape, STATE_NAME %in% c("Arkansas", "Louisiana", "Oklahoma",
"Texas"))
#The FIPS
fips <- county.fips
fips$fips <- str_pad(fips$fips, 5, "left", pad = 0)
#The Polygons
world <- map_data("world")
states <- map_data("state")
counties <- map_data("county")
#The Join
counties$polyname <- paste(counties$region, counties$subregion, sep = ",")
counties <- counties %>% left_join(fips, by = c("polyname" = "polyname"))
counties <- counties %>% left_join(se.data, by = c("fips" = "FIPS"))
#Subsets
southern_states <- subset(states, region %in%
c("texas", "arkansas", "louisiana", "mississippi",
"alabama", "georgia", "florida", "north carolina",
"south carolina", "tennessee", "oklahoma",
"kentucky", "west virginia", "virginia",
"maryland", "delaware", "district of columbia"))
southern_counties <- subset(counties, region %in%
c("texas", "arkansas", "louisiana", "mississippi",
"alabama", "georgia", "florida", "north carolina",
"south carolina", "tennessee", "oklahoma",
"kentucky", "west virginia", "virginia",
"maryland", "delaware", "district of columbia"))
southern_cities <- subset(us.cities, country.etc %in%
c("TX", "AR", "LA", "MS", "AL", "GA", "FL", "NC",
"SC", "TN", "OK", "KY", "WV", "VA", "MD", "DE", "DC"))
southern_cities <- subset(southern_cities, pop >= 100000)
#Create neighbors
se.neighbors<-poly2nb(se.shape, queen=T, row.names = se.shape$FIPS)
names(se.neighbors) <- names(se.shape$FIPS)
esc.neighbors<-poly2nb(esc.shape, queen=T, row.names = esc.shape$FIPS)
names(esc.neighbors) <- names(esc.shape$FIPS)
satl.neighbors<-poly2nb(satl.shape, queen=T, row.names = satl.shape$FIPS)
names(satl.neighbors) <- names(satl.shape$FIPS)
wsc.neighbors<-poly2nb(wsc.shape, queen=T, row.names = wsc.shape$FIPS)
names(wsc.neighbors) <- names(wsc.shape$FIPS)
#Create list of neighbors
se.neighbors.list<-nb2listw(se.neighbors, style="W", zero.policy = TRUE,
row.names(names(se.neighbors)))
esc.neighbors.list<-nb2listw(esc.neighbors, style="W", zero.policy = TRUE,
row.names(names(esc.neighbors)))
satl.neighbors.list<-nb2listw(satl.neighbors, style="W", zero.policy = TRUE,
row.names(names(satl.neighbors)))
wsc.neighbors.list<-nb2listw(wsc.neighbors, style="W", zero.policy = TRUE,
row.names(names(wsc.neighbors)))
#Create xy for all counties
county.xy<-cbind(se.data$Longitude,se.data$Latitude)
colnames(county.xy) <- c("x","y")
rownames(county.xy)<-se.data$FIPS
esc.xy<-cbind(esc.data$Longitude,esc.data$Latitude)
colnames(esc.xy)<-c("x","y")
rownames(esc.xy)<-esc.data$FIPS
satl.xy<-cbind(satl.data$Longitude,satl.data$Latitude)
colnames(satl.xy)<-c("x","y")
rownames(satl.xy)<-satl.data$FIPS
wsc.xy<-cbind(wsc.data$Longitude,wsc.data$Latitude)
colnames(wsc.xy)<-c("x","y")
rownames(wsc.xy)<-wsc.data$FIPS
#Create distance centroid
county.k1 <-knn2nb(knearneigh(county.xy, k=1, longlat = TRUE))
esc.k5 <-knn2nb(knearneigh(esc.xy, k=5, longlat = TRUE))
satl.k2 <-knn2nb(knearneigh(satl.xy, k=2, longlat = TRUE))
wsc.k1 <-knn2nb(knearneigh(wsc.xy, k=1, longlat = TRUE))
#Determine max k distance value
county.max.k1 <-max(unlist(nbdists(county.k1, county.xy, longlat=TRUE)))
esc.max.k5 <-max(unlist(nbdists(esc.k5, esc.xy, longlat=TRUE)))
satl.max.k2 <-max(unlist(nbdists(satl.k2, satl.xy, longlat=TRUE)))
wsc.max.k1 <-max(unlist(nbdists(wsc.k1, wsc.xy, longlat=TRUE)))
#Calculate neighbors based on distance
county.dist.k1 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k1, longlat = TRUE)
esc.dist.k5 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k5, longlat = TRUE)
satl.dist.k2 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k2, longlat = TRUE)
wsc.dist.k1 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k1, longlat = TRUE)
#Create neighbor list
county.k1.neighbors <-nb2listw(county.dist.k1, style="W", zero.policy = TRUE)
esc.k5.neighbors <-nb2listw(esc.dist.k5, style="W", zero.policy = TRUE)
satl.k2.neighbors <-nb2listw(satl.dist.k2, style="W", zero.policy = TRUE)
wsc.k1.neighbors <-nb2listw(wsc.dist.k1, style="W", zero.policy = TRUE)
#Creating the equation
equation <- child.pov.2016 ~ rural + urban + lnmanufacturing + lnag +
lnretail + lnhealthss + lnconstruction + lnlesshs + lnunemployment +
lnsinglemom + lnblack + lnhispanic + lnuninsured + lnincome_ratio +
lnteenbirth + lnunmarried
options(scipen = 5)
#OLS
ols <- lm(equation, data=se.data)
summary(ols)
#Morans Test
cont.morans <- lm.morantest(ols, se.neighbors.list)
cont.morans #did not produce significant results
dist.morans <- spdep::lm.morantest(ols, county.k1.neighbors)
dist.morans
#LaGrange Multiplier
cont.lm.tests <- lm.LMtests(ols, se.neighbors.list, test="all")
cont.lm.tests #did not produce significant results
dist.lm.tests <- lm.LMtests(ols, county.k1.neighbors, test="all")
dist.lm.tests
#Morans with Monte Carlo
mc.morans <- spdep::moran.mc(se.data$child.pov.2016, county.k1.neighbors, nsim = 99)
mc.morans
lc.morans <- spdep::localmoran(se.data$child.pov.2016, county.k1.neighbors)
lc.morans
pov.rate <- scale(se.data$child.pov.2016) %>% as.vector()
pov.lag.rate <- lag.listw(county.k1.neighbors, pov.rate)
lisa.data <- se.data %>%
mutate(quad_sig = case_when(
pov.rate >= 0 & pov.lag.rate >= 0 & lc.morans[, 5] <= 0.05 ~ "high-high",
pov.rate <= 0 & pov.lag.rate <= 0 & lc.morans[, 5] <= 0.05 ~ "low-low",
pov.rate >= 0 & pov.lag.rate <= 0 & lc.morans[, 5] <= 0.05 ~ "high-low",
pov.rate <= 0 & pov.lag.rate >= 0 & lc.morans[, 5] <= 0.05 ~ "low-high",
lc.morans[, 5] > 0.05 ~ "not significant"
))
lisa <- lisa.data %>% fortify()
#Distance Lag Model
dist.lag.model <- spatialreg::lagsarlm(equation, data=se.data,
county.k1.neighbors)
dist.lag.summary <- summary(dist.lag.model, Nagelkerke = TRUE)
dist.lag.data <- cbind.data.frame(se.data$FIPS,
dist.lag.summary$fitted.values,
dist.lag.summary$residual,
se.data$child.pov.2016,
se.data$urban,
se.data$lnretail,
se.data$lnhealthss,
se.data$lnconstruction,
se.data$lnlesshs,
se.data$lnunemployment,
se.data$lnsinglemom,
se.data$lnhispanic,
se.data$lnuninsured,
se.data$lnincome_ratio,
se.data$lnunmarried,
stringsAsFactors = FALSE)
#Renaming columns
colnames(dist.lag.data) <- c("fips","fitted","resid","childpov", "urban","retail",
"healthcare","construction","less_hs","unemployed",
"single_mom","hispanic","uninsured","income_ratio","unmarried")
#quantiles
quantiles_fit <- dist.lag.data %>%
pull(fitted) %>%
quantile(probs = seq(0, 1, length.out = 4), na.rm = TRUE)
quantiles_pov <- dist.lag.data %>%
pull(childpov) %>%
quantile(probs = seq(0, 1, length.out = 4), na.rm = TRUE)
#ranks
fit_rank <- cut(dist.lag.data$fitted,
breaks= quantiles_fit,
labels=c("1", "2", "3"),
na.rm = TRUE,
include.lowest = TRUE)
pov_rank <- cut(dist.lag.data$childpov,
breaks= quantiles_pov,
labels=c("1", "2", "3"),
na.rm = TRUE,
include.lowest = TRUE)
#Join ranks and combined column to dataset
dist.lag.data$fit_score <- as.numeric(fit_rank)
dist.lag.data$pov_score <- as.numeric(pov_rank)
dist.lag.data$fit_pov <- paste(as.numeric(dist.lag.data$fit_score),
"-",
as.numeric(dist.lag.data$pov_score))
#legend
legend_colors <- tibble(
x = c(3,2,1,3,2,1,3,2,1),
y = c(3,3,3,2,2,2,1,1,1),
z = c("#574249", "#627f8c", "#64acbe", "#985356", "#ad9ea5", "#b0d5df", "#c85a5a", "#e4acac", "#e8e8e8"))
xlabel <- "Modeled,Low \u2192 High"
xlabel <- gsub(",", "\n", xlabel)
ylabel <- "Observed,Low \u2192 High"
ylabel <- gsub(",", "\n", ylabel)
legend <- ggplot(legend_colors, aes(x,y)) +
geom_tile(aes(fill=z)) +
theme_minimal() + theme(legend.position = "none") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
labs(x = xlabel, y = ylabel) +
scale_fill_identity() +
ggtitle("Legend") +
theme(axis.title.y = element_text(face = "italic", hjust = 0.5, size = 8)) +
theme(axis.title.x = element_text(face = "italic", hjust = 0.5, size = 8)) +
theme(plot.title = element_text(face="bold", hjust = 0.5, size = 10))
#attach data to counties
county.data <- southern_counties %>%
left_join(dist.lag.data, by = c("fips" = "fips")) %>% fortify
county.data <- county.data %>% left_join(lisa.data, by = c("fips" = "FIPS"))
#attach colors
bivariate_color_scale <- tibble(
"3 - 3" = "#574249",
"2 - 3" = "#627f8c",
"1 - 3" = "#64acbe",
"3 - 2" = "#985356",
"2 - 2" = "#ad9ea5",
"1 - 2" = "#b0d5df",
"3 - 1" = "#c85a5a",
"2 - 1" = "#e4acac",
"1 - 1" = "#e8e8e8") %>%
gather("group", "fill")
#lisa colors
lisa_colors <- tibble(
"high-high" = "#32C12C",
"low-low" = "#526EFF",
"high-low" = "#D40C00",
"low-high" = "#D40C00",
"not significant" = "#9E9E9E") %>%
gather("rate","lisa")
county.data <- county.data %>%
left_join(bivariate_color_scale, by = c("fit_pov" = "group"))
county.data <- county.data %>%
left_join(lisa_colors, by = c("quad_sig" = "rate"))
#fit map
fit_pov_map <- ggplot() +
geom_polygon(data = world, aes(x=long,y=lat, group=group), fill = "gray95", color = "gray30") +
geom_polygon(data = states, aes(x=long,y=lat, group=group), fill = "gray", color = "gray30") +
geom_polygon(data = county.data, aes(x=long, y=lat, group=group, fill = fill)) +
geom_polygon(data = southern_counties, aes(x=long,y=lat, group=group), fill = NA, color = "black", size = 0.05) +
geom_polygon(data = southern_states, aes(x=long,y=lat, group=group), fill = NA, color = "white") +
#geom_point(data = southern_cities, aes(x=long, y=lat), fill = "black", color = "black") +
coord_map("conic", lat0 = 30, xlim=c(-106,-77), ylim=c(24.45,40.5)) +
scale_fill_identity() +
theme_grey() + theme(legend.position="bottom") + theme(legend.title.align=0.5) +
theme(panel.background = element_rect(fill = 'deepskyblue'),
panel.grid.major = element_line(colour = NA)) +
labs(x = "Longitude", y = "Latitude", fill = "Child Poverty",
title = "Bivariate Map of Observed vs. Modeled Poverty Values") +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
#final map
final_map <- ggdraw() +
draw_plot(fit_pov_map, x = 0, y = 0, width = 1, height = 1) +
draw_plot(legend, x = 0.55, y = 0.075, width = 0.15, height = 0.25)
final_map
#Regional Analyses and other models
se.cont.lag.model <- spatialreg::lagsarlm(equation, data=se.data,
se.neighbors.list)
se.cont.lag.summary <- summary(se.cont.lag.model, Nagelkerke = TRUE)
se.cont.lag.summary
se.cont.err.model <- spatialreg::errorsarlm(equation, data=se.data,
se.neighbors.list)
se.cont.err.summary <- summary(se.cont.err.model, Nagelkerke = TRUE)
se.cont.err.summary
dist.err.model <- spatialreg::errorsarlm(equation, data=se.data,
county.k1.neighbors)
dist.err.summary <- summary(dist.err.model, Nagelkerke = TRUE)
dist.err.summary
#Original distance error models + lag
#ESC
esc.dist.lag.model <- spatialreg::lagsarlm(equation, data=esc.data,
esc.k5.neighbors)
esc.dist.lag.summary <- summary(esc.dist.lag.model, Nagelkerke = TRUE)
esc.dist.lag.summary
esc.dist.err.model <- spatialreg::errorsarlm(equation, data=esc.data,
esc.k5.neighbors)
esc.dist.err.summary <- summary(esc.dist.err.model, Nagelkerke = TRUE)
esc.dist.err.summary
#SATL
satl.dist.lag.model <- spatialreg::lagsarlm(equation, data=satl.data,
satl.k2.neighbors)
satl.dist.lag.summary <- summary(satl.dist.lag.model, Nagelkerke = TRUE)
satl.dist.lag.summary
satl.dist.err.model <- spatialreg::errorsarlm(equation, data=satl.data,
satl.k2.neighbors)
satl.dist.err.summary <- summary(satl.dist.err.model, Nagelkerke = TRUE)
satl.dist.err.summary
#WSC
wsc.dist.lag.model <- spatialreg::lagsarlm(equation, data=wsc.data,
wsc.k1.neighbors)
wsc.dist.lag.summary <- summary(wsc.dist.lag.model, Nagelkerke = TRUE)
wsc.dist.lag.summary
wsc.dist.err.model <- spatialreg::errorsarlm(equation, data=wsc.data,
wsc.k1.neighbors)
wsc.dist.err.summary <- summary(wsc.dist.err.model, Nagelkerke = TRUE)
wsc.dist.err.summary
#additional distance models
#Create distance centroid
county.k1 <-knn2nb(knearneigh(county.xy, k=1, longlat = TRUE))
county.k2 <-knn2nb(knearneigh(county.xy, k=2, longlat = TRUE))
county.k3 <-knn2nb(knearneigh(county.xy, k=3, longlat = TRUE))
county.k4 <-knn2nb(knearneigh(county.xy, k=4, longlat = TRUE))
county.k5 <-knn2nb(knearneigh(county.xy, k=5, longlat = TRUE))
esc.k1 <-knn2nb(knearneigh(esc.xy, k=1, longlat = TRUE))
esc.k2 <-knn2nb(knearneigh(esc.xy, k=2, longlat = TRUE))
esc.k3 <-knn2nb(knearneigh(esc.xy, k=3, longlat = TRUE))
esc.k4 <-knn2nb(knearneigh(esc.xy, k=4, longlat = TRUE))
esc.k5 <-knn2nb(knearneigh(esc.xy, k=5, longlat = TRUE))
satl.k1 <-knn2nb(knearneigh(satl.xy, k=1, longlat = TRUE))
satl.k2 <-knn2nb(knearneigh(satl.xy, k=2, longlat = TRUE))
satl.k3 <-knn2nb(knearneigh(satl.xy, k=3, longlat = TRUE))
satl.k4 <-knn2nb(knearneigh(satl.xy, k=4, longlat = TRUE))
satl.k5 <-knn2nb(knearneigh(satl.xy, k=5, longlat = TRUE))
wsc.k1 <-knn2nb(knearneigh(wsc.xy, k=1, longlat = TRUE))
wsc.k2 <-knn2nb(knearneigh(wsc.xy, k=2, longlat = TRUE))
wsc.k3 <-knn2nb(knearneigh(wsc.xy, k=3, longlat = TRUE))
wsc.k4 <-knn2nb(knearneigh(wsc.xy, k=4, longlat = TRUE))
wsc.k5 <-knn2nb(knearneigh(wsc.xy, k=5, longlat = TRUE))
#Determine max k distance value
county.max.k1 <-max(unlist(nbdists(county.k1, county.xy, longlat=TRUE)))
county.max.k2 <-max(unlist(nbdists(county.k2, county.xy, longlat=TRUE)))
county.max.k3 <-max(unlist(nbdists(county.k3, county.xy, longlat=TRUE)))
county.max.k4 <-max(unlist(nbdists(county.k4, county.xy, longlat=TRUE)))
county.max.k5 <-max(unlist(nbdists(county.k5, county.xy, longlat=TRUE)))
esc.max.k1 <-max(unlist(nbdists(esc.k1, esc.xy, longlat=TRUE)))
esc.max.k2 <-max(unlist(nbdists(esc.k2, esc.xy, longlat=TRUE)))
esc.max.k3 <-max(unlist(nbdists(esc.k3, esc.xy, longlat=TRUE)))
esc.max.k4 <-max(unlist(nbdists(esc.k4, esc.xy, longlat=TRUE)))
esc.max.k5 <-max(unlist(nbdists(esc.k5, esc.xy, longlat=TRUE)))
satl.max.k1 <-max(unlist(nbdists(satl.k1, satl.xy, longlat=TRUE)))
satl.max.k2 <-max(unlist(nbdists(satl.k2, satl.xy, longlat=TRUE)))
satl.max.k3 <-max(unlist(nbdists(satl.k3, satl.xy, longlat=TRUE)))
satl.max.k4 <-max(unlist(nbdists(satl.k4, satl.xy, longlat=TRUE)))
satl.max.k5 <-max(unlist(nbdists(satl.k5, satl.xy, longlat=TRUE)))
wsc.max.k1 <-max(unlist(nbdists(wsc.k1, wsc.xy, longlat=TRUE)))
wsc.max.k2 <-max(unlist(nbdists(wsc.k2, wsc.xy, longlat=TRUE)))
wsc.max.k3 <-max(unlist(nbdists(wsc.k3, wsc.xy, longlat=TRUE)))
wsc.max.k4 <-max(unlist(nbdists(wsc.k4, wsc.xy, longlat=TRUE)))
wsc.max.k5 <-max(unlist(nbdists(wsc.k5, wsc.xy, longlat=TRUE)))
#Calculate neighbors based on distance
county.dist.k1 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k1, longlat = TRUE)
county.dist.k2 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k2, longlat = TRUE)
county.dist.k3 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k3, longlat = TRUE)
county.dist.k4 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k4, longlat = TRUE)
county.dist.k5 <-dnearneigh(county.xy, d1=0, d2=1 * county.max.k5, longlat = TRUE)
esc.dist.k1 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k1, longlat = TRUE)
esc.dist.k2 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k2, longlat = TRUE)
esc.dist.k3 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k3, longlat = TRUE)
esc.dist.k4 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k4, longlat = TRUE)
esc.dist.k5 <-dnearneigh(esc.xy, d1=0, d2=1 * esc.max.k5, longlat = TRUE)
satl.dist.k1 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k1, longlat = TRUE)
satl.dist.k2 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k2, longlat = TRUE)
satl.dist.k3 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k3, longlat = TRUE)
satl.dist.k4 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k4, longlat = TRUE)
satl.dist.k5 <-dnearneigh(satl.xy, d1=0, d2=1 * satl.max.k5, longlat = TRUE)
wsc.dist.k1 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k1, longlat = TRUE)
wsc.dist.k2 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k2, longlat = TRUE)
wsc.dist.k3 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k3, longlat = TRUE)
wsc.dist.k4 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k4, longlat = TRUE)
wsc.dist.k5 <-dnearneigh(wsc.xy, d1=0, d2=1 * wsc.max.k5, longlat = TRUE)
#Create neighbor list
county.k1.neighbors <-nb2listw(county.dist.k1, style="W", zero.policy = TRUE)
county.k2.neighbors <-nb2listw(county.dist.k2, style="W", zero.policy = TRUE)
county.k3.neighbors <-nb2listw(county.dist.k3, style="W", zero.policy = TRUE)
county.k4.neighbors <-nb2listw(county.dist.k4, style="W", zero.policy = TRUE)
county.k5.neighbors <-nb2listw(county.dist.k5, style="W", zero.policy = TRUE)
esc.k1.neighbors <-nb2listw(esc.dist.k1, style="W", zero.policy = TRUE)
esc.k2.neighbors <-nb2listw(esc.dist.k2, style="W", zero.policy = TRUE)
esc.k3.neighbors <-nb2listw(esc.dist.k3, style="W", zero.policy = TRUE)
esc.k4.neighbors <-nb2listw(esc.dist.k4, style="W", zero.policy = TRUE)
esc.k5.neighbors <-nb2listw(esc.dist.k5, style="W", zero.policy = TRUE)
satl.k1.neighbors <-nb2listw(satl.dist.k1, style="W", zero.policy = TRUE)
satl.k2.neighbors <-nb2listw(satl.dist.k2, style="W", zero.policy = TRUE)
satl.k3.neighbors <-nb2listw(satl.dist.k3, style="W", zero.policy = TRUE)
satl.k4.neighbors <-nb2listw(satl.dist.k4, style="W", zero.policy = TRUE)
satl.k5.neighbors <-nb2listw(satl.dist.k5, style="W", zero.policy = TRUE)
wsc.k1.neighbors <-nb2listw(wsc.dist.k1, style="W", zero.policy = TRUE)
wsc.k2.neighbors <-nb2listw(wsc.dist.k2, style="W", zero.policy = TRUE)
wsc.k3.neighbors <-nb2listw(wsc.dist.k3, style="W", zero.policy = TRUE)
wsc.k4.neighbors <-nb2listw(wsc.dist.k4, style="W", zero.policy = TRUE)
wsc.k5.neighbors <-nb2listw(wsc.dist.k5, style="W", zero.policy = TRUE)
#Alter for each dist model
wsc.dist5.lag.model <- spatialreg::lagsarlm(equation, data=wsc.data,
wsc.k5.neighbors)
wsc.dist5.lag.summary <- summary(wsc.dist5.lag.model, Nagelkerke = TRUE)
wsc.dist5.lag.summary
wsc.dist5.err.model <- spatialreg::errorsarlm(equation, data=wsc.data,
wsc.k5.neighbors)
wsc.dist5.err.summary <- summary(wsc.dist5.err.model, Nagelkerke = TRUE)
wsc.dist5.err.summary
#Regional Cont Models
old.wsc.cont.lag.model <- spatialreg::lagsarlm(old.equation, data=old.wsc.data,
wsc.neighbors.list)
old.wsc.cont.lag.summary <- summary(old.wsc.cont.lag.model, Nagelkerke = TRUE)
old.wsc.cont.lag.summary
old.wsc.cont.err.model <- spatialreg::errorsarlm(old.equation, data=old.wsc.data,
wsc.neighbors.list)
old.wsc.cont.err.summary <- summary(old.wsc.cont.err.model, Nagelkerke = TRUE)
old.wsc.cont.err.summary
# #Regular OLS
old.wsc.ols <- lm(old.equation, data=old.wsc.data)
summary(old.wsc.ols)
#Previous Analysis
#Import old data
old.se.data <- read.csv("./Data/chpov_south_jan17.csv",
colClasses = c("character", "character", "character",
"numeric", "numeric", "integer",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"integer", "numeric", "numeric",
"numeric", "numeric"))
#create subsets for old data
old.satl.data <- subset(old.se.data, state %in% c("DE", "DC", "FL", "GA", "MD", "NC", "SC", "VA", "WV"))
old.esc.data <- subset(old.se.data, state %in% c("AL", "KY", "MS", "TN"))
old.wsc.data <- subset(old.se.data, state %in% c("AR", "LA", "OK", "TX"))
#join with old data
old.counties <- counties %>% left_join(old.se.data, by = c("fips" = "fips"))
#old data equation
old.equation <- lnchildpov_under18 ~ rural +urban + lnmanufacturing + lnag + lnretail + lnhealth +
lnconstruction + lnless_hs +lnunemployment + lnincome_ratio + lnteenbirth + lnunmarried +
lnsingle_mom + lnuninsured + lnblack + lnhispanic
#Old OLS
old.ols <- lm(old.equation, data = old.se.data)
summary(old.ols)
#Old Morans Test
old.cont.morans <- lm.morantest(old.ols, se.neighbors.list)
old.cont.morans
old.dist.morans <- lm.morantest(old.ols, county.k1.neighbors)
old.dist.morans
#Old LaGrange Multiplier
old.cont.lm.tests <- lm.LMtests(old.ols, se.neighbors.list, test="all")
old.cont.lm.tests
old.dist.lm.tests <- lm.LMtests(old.ols, county.k1.neighbors, test="all")
old.dist.lm.tests
#Old analyses
old.dist.err.model <- spatialreg::errorsarlm(old.equation, data=old.se.data,
county.k1.neighbors)
old.dist.err.summary <- summary(old.dist.err.model, Nagelkerke = TRUE)
old.dist.err.summary
old.esc.err.model <- spatialreg::errorsarlm(old.equation, data=old.esc.data,
esc.k5.neighbors)
old.esc.err.summary <- summary(old.esc.err.model, Nagelkerke = TRUE)
old.esc.err.summary
old.satl.err.model <- spatialreg::errorsarlm(old.equation, data=old.satl.data,
satl.k2.neighbors)
old.satl.err.summary <- summary(old.satl.err.model, Nagelkerke = TRUE)
old.satl.err.summary
old.wsc.err.model <- spatialreg::errorsarlm(old.equation, data=old.wsc.data,
wsc.k1.neighbors)
old.wsc.err.summary <- summary(old.wsc.err.model, Nagelkerke = TRUE)
old.wsc.err.summary
#Nested Models
sdm <- spatialreg::lagsarlm(equation, se.data, county.k1.neighbors, type = "mixed")
sdm.summary <- summary(sdm, Nagelkerke = TRUE)
esc.sdm <- spatialreg::lagsarlm(equation, esc.data, esc.k5.neighbors, type = "mixed")
esc.sdm.summary <- summary(esc.sdm, Nagelkerke = TRUE) #not significant
satl.sdm <- spatialreg::lagsarlm(equation, satl.data, satl.k1.neighbors, type = "mixed")
satl.sdm.summary <- summary(satl.sdm, Nagelkerke = TRUE)
wsc.sdm <- spatialreg::lagsarlm(equation, wsc.data, wsc.k1.neighbors, type = "mixed")
wsc.sdm.summary <- summary(wsc.sdm, Nagelkerke = TRUE)
sdm.impacts <- summary(spatialreg::impacts(sdm, listw = county.k1.neighbors, R = 100), zstats = TRUE)#[["pzmat"]]
sdm.impacts
sdem <- spatialreg::errorsarlm(equation, se.data, county.k1.neighbors, etype = "emixed")
summary(sdem, Nagelkerke = TRUE)
#Simplifying Nested Models
spatialreg::LR.sarlm(sdm,se.ols)
spatialreg::LR.sarlm(sdm,se.SLX.model)
spatialreg::LR.sarlm(sdm,dist.lag.model)
spatialreg::LR.sarlm(sdm,dist.err.model)
spatialreg::LR.sarlm(sdem,se.ols)
spatialreg::LR.sarlm(sdem,se.SLX.model)
spatialreg::LR.sarlm(sdem,dist.err.model)
spatialreg::Hausman.test(sdem)