Comparing economic conditions in Jersey City (for graphics)
Andrew Ba Tran
This is a more-focused geospatial analysis of unemployment rates in the census tracts chosen specifically by developers compared with the tracts immediately adjacent to the project site. This is another version with tracts within a certain distance of the site.
The numbers are bucketed and colors are chosen randomly for so it would be easier for graphics to edit.
nj_h <- tracts("NJ", county="Hudson", cb=F)
nj_h_centroids <- SpatialPointsDataFrame(gCentroid(nj_h, byid=TRUE),
nj_h@data, match.ID=FALSE)
nj_h_centroids <- as.data.frame(nj_h_centroids)
nj_h_centroids <- select(nj_h_centroids, GEOID, x, y)
nj_h_centroids$distance_proj1 <- 0
nj_h_centroids$distance_proj2 <- 0
for (i in 1:nrow(nj_h_centroids)) {
nj_h_centroids$distance_proj1[i] <- distm (c(-74.063644, 40.734330), c(nj_h_centroids$x[i], nj_h_centroids$y[i]), fun = distHaversine)[,1]/ 1609
nj_h_centroids$distance_proj2[i] <- distm (c(-74.03577, 40.72), c(nj_h_centroids$x[i], nj_h_centroids$y[i]), fun = distHaversine)[,1]/ 1609
}1 Journal Square (2017)
# GEOIDs: 34017001900, 34017004600, 34017005300, 34017006600, 34017006700, 34017007100
# GEIODs: 19, 20 18, 17, 9.02, 12.02, 71
hudson_unemployment_2013 <- get_acs(geography="tract", endyear=2013, variables= c("B23025_005E", "B23025_002E"), county = "Hudson", state="NJ")
hudson_unemployment_2013$moe <- NULL
hudson_unemployment_2013 <- spread(hudson_unemployment_2013,variable, estimate )
hudson_unemployment_2013$per_un <- round(hudson_unemployment_2013[,4]/hudson_unemployment_2013[,3]*100,2)
hudson_unemployment_2013 <- filter(hudson_unemployment_2013, GEOID!="34017006900" & GEOID!="34017980100")
proj1 <- c("34017001900", "34017004600", "34017005300", "34017006600", "34017006700", "34017007100")
hudson_unemployment_2013_sm <- filter(hudson_unemployment_2013, GEOID %in% proj1)
colnames(hudson_unemployment_2013_sm) <- c("GEOID", "name", "total", "unemployed", "unemp_rate")
hudson_unemployment_2013_sm$un_rate <- hudson_unemployment_2013_sm$unemp_rate$B23025_005
hudson_unemployment_2013_sm$unemp_rate <- NULL
hudson_unemployment_2013_sm$radius <- "gerrymandered"
proj1_half <- c("34017001900", "34017002000", "34017001800", "34017001700", "34017000902", "34017001202", "34017007100")
hudson_unemployment_2013_sm_half <- filter(hudson_unemployment_2013, GEOID %in% proj1_half)
colnames(hudson_unemployment_2013_sm_half) <- c("GEOID", "name", "total", "unemployed", "unemp_rate")
hudson_unemployment_2013_sm_half$un_rate <- hudson_unemployment_2013_sm_half$unemp_rate$B23025_005
hudson_unemployment_2013_sm_half$unemp_rate <- NULL
hudson_unemployment_2013_sm_half$radius <- "adjacent tracts"
hudson_unemployment_sm <- rbind(hudson_unemployment_2013_sm, hudson_unemployment_2013_sm_half)
hudson_unemployment_sm$name <- gsub(",.*", "", hudson_unemployment_sm$name)
nj_hf <- fortify(nj_h, region="GEOID")
nj_hud <- left_join(nj_hf, hudson_unemployment_sm, by=c("id"="GEOID"))
nj_hud <- filter(nj_hud, !is.na(radius))
nj_hud$nj_mid <- cut(nj_hud$un_rate, 6)
colors <- colorRampPalette(c("white", "red"))(length(levels(nj_hud$nj_mid)))
nj_map <- ggplot()
#nj_map <- nj_map + geom_polygon(data=nj_hf, aes(x=long, y=lat, group=group), fill=NA, color="black", size=.1)
nj_map <- nj_map + geom_polygon(data=nj_hud, aes(x=long, y=lat, group=group, fill=factor(nj_mid)), color="black", size=.5)
nj_map <- nj_map + facet_wrap(~radius)
nj_map <- nj_map + coord_map()
#nj_map <- nj_map + scale_x_discrete()
nj_map <- nj_map + scale_fill_manual(drop=FALSE, values=c("gray", "yellow", "orange", "red", "green", "purple"), na.value="#EEEEEE", name="Unemployment rate")
nj_map <- nj_map + theme_nothing(legend=TRUE)
nj_map <- nj_map + labs(x=NULL, y=NULL, title="1 Journal Square (2017)")
nj_map <- nj_map + theme(panel.grid.major = element_line(colour = NA))
nj_map <- nj_map + theme(text = element_text(size=15))
nj_map <- nj_map + theme(plot.title=element_text(face="bold", hjust=.4))
nj_map <- nj_map + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))
nj_map <- nj_map + theme(plot.caption=element_text(size=12, margin=margin(t=12), color="#7a7d7e", hjust=0))
nj_map <- nj_map + theme(legend.key.size = unit(1, "cm"))
nj_map <- nj_map + annotate("segment", x = -74.063644, xend = -74.035, y = 40.734330, yend = 40.734330, colour = "tomato", size=.5)
nj_map <- nj_map + annotate("point", x = -74.063644, y = 40.734330, colour = "lightblue", size = 1)
nj_map <- nj_map + annotate("text", x = -74.01, y = 40.734330, label = "1 Journal Square", size=3, colour="gray30")
print(nj_map)
#ggsave(nj_map, device="pdf","map1a.pdf")
nj_map <- ggplot()
nj_map <- nj_map + geom_polygon(data=nj_hf, aes(x=long, y=lat, group=group), fill=NA, color="black", size=.1)
nj_map <- nj_map + geom_polygon(data=nj_hud, aes(x=long, y=lat, group=group, fill=factor(nj_mid)), color="black", size=.5)
nj_map <- nj_map + facet_wrap(~radius)
nj_map <- nj_map + coord_map()
#nj_map <- nj_map + scale_x_discrete()
nj_map <- nj_map + scale_fill_manual(drop=FALSE, values=c("gray", "yellow", "orange", "red", "green", "purple"), na.value="#EEEEEE", name="Unemployment rate")
nj_map <- nj_map + theme_nothing(legend=TRUE)
nj_map <- nj_map + labs(x=NULL, y=NULL, title="1 Journal Square (2017)")
nj_map <- nj_map + theme(panel.grid.major = element_line(colour = NA))
nj_map <- nj_map + theme(text = element_text(size=15))
nj_map <- nj_map + theme(plot.title=element_text(face="bold", hjust=.4))
nj_map <- nj_map + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))
nj_map <- nj_map + theme(plot.caption=element_text(size=12, margin=margin(t=12), color="#7a7d7e", hjust=0))
nj_map <- nj_map + theme(legend.key.size = unit(1, "cm"))
nj_map <- nj_map + annotate("segment", x = -74.063644, xend = -74.035, y = 40.734330, yend = 40.734330, colour = "tomato", size=.5)
nj_map <- nj_map + annotate("point", x = -74.063644, y = 40.734330, colour = "lightblue", size = 1)
nj_map <- nj_map + annotate("text", x = -74.01, y = 40.734330, label = "1 Journal Square", size=3, colour="gray30")
print(nj_map)
#ggsave(nj_map, device="pdf","map1b.pdf")For this project, officials said the qualifying rate using 2015 annual average data is 8.0 percent (1.5 times the United States not seasonally adjusted unemployment rate for the same time period).
So this project would’ve qualified based on any of the measures listed below.
sm_table <- hudson_unemployment_sm %>%
group_by(radius) %>%
summarize(average_unemployment=round(mean(un_rate, na.rm=T),2), median_unemployment=round(median(un_rate, na.rm=T),2)) %>%
arrange(average_unemployment)
kable(sm_table)| radius | average_unemployment | median_unemployment |
|---|---|---|
| adjacent tracts | 10.59 | 9.75 |
| gerrymandered | 15.74 | 15.89 |
65 Bay Street (2015)
# GEOIDs: 34017004400, 34017004500, 34017004600, 34017005200, 34017005300, 34017005500, 34017005600, 34017005801, 34017006000, 34017006100, 34017006200, 34017006400, 34017006500, 34017006700, 34017006800, 34017007600
#GEOIDS: 77, 78, 70, 64, 75, 74, 76
hudson_unemployment_2012 <- get_acs(geography="tract", endyear=2012, variables= c("B23025_005E", "B23025_002E"), county = "Hudson", state="NJ")
hudson_unemployment_2012$moe <- NULL
hudson_unemployment_2012 <- spread(hudson_unemployment_2012,variable, estimate )
hudson_unemployment_2012$per_un <- round(hudson_unemployment_2012[,4]/hudson_unemployment_2012[,3]*100,2)
hudson_unemployment_2012 <- filter(hudson_unemployment_2012, GEOID!="34017006900" & GEOID!="34017980100")
proj1 <- c("34017004400", "34017004500", "34017004600", "34017005200", "34017005300", "34017005500", "34017005600", "34017005801", "34017006000", "34017006100", "34017006200", "34017006400", "34017006500", "34017006700", "34017006800", "34017007600")
hudson_unemployment_2012_sm <- filter(hudson_unemployment_2012, GEOID %in% proj1)
colnames(hudson_unemployment_2012_sm) <- c("GEOID", "name", "total", "unemployed", "unemp_rate")
hudson_unemployment_2012_sm$un_rate <- hudson_unemployment_2012_sm$unemp_rate$B23025_005
hudson_unemployment_2012_sm$unemp_rate <- NULL
hudson_unemployment_2012_sm$radius <- "gerrymandered"
proj1_half <- c("34017007700", "34017007800", "34017007000", "34017006400", "34017007500", "34017007400", "34017007600")
hudson_unemployment_2012_sm_half <- filter(hudson_unemployment_2012, GEOID %in% proj1_half)
colnames(hudson_unemployment_2012_sm_half) <- c("GEOID", "name", "total", "unemployed", "unemp_rate")
hudson_unemployment_2012_sm_half$un_rate <- hudson_unemployment_2012_sm_half$unemp_rate$B23025_005
hudson_unemployment_2012_sm_half$unemp_rate <- NULL
hudson_unemployment_2012_sm_half$radius <- "adjacent tracts"
hudson_unemployment_sm <- rbind(hudson_unemployment_2012_sm, hudson_unemployment_2012_sm_half)
hudson_unemployment_sm$name <- gsub(",.*", "", hudson_unemployment_sm$name)
nj_hf <- fortify(nj_h, region="GEOID")
nj_hud <- left_join(nj_hf, hudson_unemployment_sm, by=c("id"="GEOID"))
nj_hud <- filter(nj_hud, !is.na(radius))
nj_hud$nj_mid <- cut(nj_hud$un_rate, 6)
colors <- colorRampPalette(c("white", "red"))(length(levels(nj_hud$nj_mid)))
nj_map <- ggplot()
#nj_map <- nj_map + geom_polygon(data=nj_hf, aes(x=long, y=lat, group=group), fill=NA, color="black", size=.1)
nj_map <- nj_map + geom_polygon(data=nj_hud, aes(x=long, y=lat, group=group, fill=factor(nj_mid)), color="black", size=.5)
nj_map <- nj_map + facet_wrap(~radius)
nj_map <- nj_map + coord_map()
#nj_map <- nj_map + scale_x_discrete()
nj_map <- nj_map + scale_fill_manual(drop=FALSE, values=c("gray", "yellow", "orange", "red", "green", "purple"), na.value="#EEEEEE", name="Unemployment rate")
#nj_map <- nj_map + scale_fill_viridis(option = "heat", direction=-1, name = "Unemployment rate")
#nj_map <- nj_map + scale_color_viridis(option = "heat", direction=-1)
nj_map <- nj_map + theme_nothing(legend=TRUE)
nj_map <- nj_map + labs(x=NULL, y=NULL, title="65 Bay Street (2015)")
nj_map <- nj_map + theme(panel.grid.major = element_line(colour = NA))
nj_map <- nj_map + theme(text = element_text(size=15))
nj_map <- nj_map + theme(plot.title=element_text(face="bold", hjust=.4))
nj_map <- nj_map + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))
nj_map <- nj_map + theme(plot.caption=element_text(size=12, margin=margin(t=12), color="#7a7d7e", hjust=0))
nj_map <- nj_map + theme(legend.key.size = unit(1, "cm"))
nj_map <- nj_map + annotate("segment", x = -74.03577, xend = -74.005, y = 40.72, yend = 40.72, colour = "tomato", size=.5)
nj_map <- nj_map + annotate("point", x = -74.03577, y = 40.72, colour = "lightblue", size = 1)
nj_map <- nj_map + annotate("text", x = -73.98217, y = 40.72, label = "65 Bay Street", size=3, colour="gray30")
print(nj_map)
#ggsave(nj_map, device="pdf", "map2a.pdf")
nj_map <- ggplot()
nj_map <- nj_map + geom_polygon(data=nj_hf, aes(x=long, y=lat, group=group), fill=NA, color="black", size=.1)
nj_map <- nj_map + geom_polygon(data=nj_hud, aes(x=long, y=lat, group=group, fill=factor(nj_mid)), color="black", size=.5)
nj_map <- nj_map + facet_wrap(~radius)
nj_map <- nj_map + coord_map()
#nj_map <- nj_map + scale_x_discrete()
nj_map <- nj_map + scale_fill_manual(drop=FALSE, values=c("gray", "yellow", "orange", "red", "green", "purple"), na.value="#EEEEEE", name="Unemployment rate")
#nj_map <- nj_map + scale_fill_viridis(option = "heat", direction=-1, name = "Unemployment rate")
#nj_map <- nj_map + scale_color_viridis(option = "heat", direction=-1)
nj_map <- nj_map + theme_nothing(legend=TRUE)
nj_map <- nj_map + labs(x=NULL, y=NULL, title="65 Bay Street (2015)")
nj_map <- nj_map + theme(panel.grid.major = element_line(colour = NA))
nj_map <- nj_map + theme(text = element_text(size=15))
nj_map <- nj_map + theme(plot.title=element_text(face="bold", hjust=.4))
nj_map <- nj_map + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))
nj_map <- nj_map + theme(plot.caption=element_text(size=12, margin=margin(t=12), color="#7a7d7e", hjust=0))
nj_map <- nj_map + theme(legend.key.size = unit(1, "cm"))
nj_map <- nj_map + annotate("segment", x = -74.03577, xend = -74.005, y = 40.72, yend = 40.72, colour = "tomato", size=.5)
nj_map <- nj_map + annotate("point", x = -74.03577, y = 40.72, colour = "lightblue", size = 1)
nj_map <- nj_map + annotate("text", x = -73.98217, y = 40.72, label = "65 Bay Street", size=3, colour="gray30")
print(nj_map)
#ggsave(nj_map, device="pdf", "map2b.pdf")For this project, officials said the qualifying rate using 2015 annual average data is 9.3 percent (1.5 times the United States not seasonally adjusted unemployment rate for the same time period).
So this project would’ve qualified based on only the gerrymandered version since unemployment was too low in the other ways of grouping the census tracts.
sm_table <- hudson_unemployment_sm %>%
group_by(radius) %>%
summarize(average_unemployment=round(mean(un_rate, na.rm=T),2), median_unemployment=round(median(un_rate, na.rm=T),2)) %>%
arrange(average_unemployment)
kable(sm_table)| radius | average_unemployment | median_unemployment |
|---|---|---|
| adjacent tracts | 5.05 | 4.99 |
| gerrymandered | 16.59 | 18.12 |