U.S. Citizenship in Corona, Queens

The analysis is an exploration of U.S. citizenship in Corona, Queens, as part of a bigger research of the socio-demographic conditions in the neighbourhood. The analysis will be divided to 3 different categories: U.S. citizenship, U.S. citizenship by naturalisation, and non-U.S. citizenship. The result will also be compared to neighbouring districts in Community Board 3 and 4.

Data Processing Preparation

To prepare for the analysis, we will load packages before processing with our data exploration.

library(tidyverse)
library(tidycensus)
library(scales)
library(sf)
library(RColorBrewer)
library(knitr)
options(scipen = 999)

We will then import variables from American Community Survey (ACS).

acs201620 <- load_variables(2020, "acs5", cache = T)

For the next step, we will import spatial data, which are the borough shapefiles from NYC Open Data and Neighborhood Tabulation Areas for NYC.

boros <- st_read("raw/geo/Borough Boundaries.geojson")

nabes <- st_read("raw/geo/nynta2020.shp")

Data Processing

For our exploration, we will look at the imported ACS variables and choose the Nativity and Citizenship Status in the United States (B05001) and create a new dataframe to process the variables.

• B05001_001 = Estimate!!Total:
• B05001_002 = Estimate!!Total:!!U.S. citizen, born in the United States
• B05001_005 = Estimate!!Total:!!U.S. citizen by naturalization
• B05001_006 = Estimate!!Total:!!Not a U.S. citizen

raw_citizenship <- get_acs(geography = "tract",
                        variables = c(`Total Population` = "B05001_001",
                                      `U.S. Citizen` = "B05001_002",
                                      `U.S. Citizen (Naturalisation)` = "B05001_005",
                                      `Non-U.S. Citizen` = "B05001_006"),
                        state='NY',
                        county = 'Queens',
                        geometry = T,
                        year = 2020,
                        output = "wide")

We will then process the raw dataframe to process the percentage of citizenship.

citizenship <- raw_citizenship %>% 
  mutate(`U.S. Citizenship Percentage` = `U.S. CitizenE`/`Total PopulationE`,
         `U.S. Citizenship (Naturalisation) Percentage` = `U.S. Citizen (Naturalisation)E`/`Total PopulationE`,
         `Non-U.S. Citizenship Percentage` = `Non-U.S. CitizenE`/`Total PopulationE`)

Notice that the table below shows NaN, which means that the denominator is zero (the same as N/A). We want to process these NaNs to actual numbers.

GEOID	NAME	Total PopulationE	Total PopulationM	U.S. CitizenE	U.S. CitizenM	U.S. Citizen (Naturalisation)E	U.S. Citizen (Naturalisation)M	Non-U.S. CitizenE	Non-U.S. CitizenM	geometry	U.S. Citizenship Percentage	U.S. Citizenship (Naturalisation) Percentage	Non-U.S. Citizenship Percentage
36081005000	Census Tract 50, Queens County, New York	0	12	0	12	0	12	0	12	MULTIPOLYGON (((-73.85767 4…	NaN	NaN	NaN
36081006900	Census Tract 69, Queens County, New York	3785	598	2492	513	740	228	465	194	MULTIPOLYGON (((-73.92511 4…	0.6583884	0.1955086	0.1228534
36081007500	Census Tract 75, Queens County, New York	3982	681	2430	509	1026	283	464	307	MULTIPOLYGON (((-73.92915 4…	0.6102461	0.2576595	0.1165244

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Processing N/As

We need to redefine the N/As to numbers. First, we will use is.na() to look at the rows with N/A.

na_tracts <- citizenship %>% 
  filter(is.na(`U.S. Citizenship Percentage`)) %>%
  filter(is.na(`U.S. Citizenship (Naturalisation) Percentage`)) %>% 
  filter(is.na(`Non-U.S. Citizenship Percentage`))

We will now convert the NaNs or N/As to numbers with the code chunk below.

citizenship <- raw_citizenship %>% 
  mutate(`U.S. Citizenship Percentage` = `U.S. CitizenE`/`Total PopulationE`,
         `U.S. Citizenship Percentage` = ifelse(is.nan(`U.S. Citizenship Percentage`),
                                                NA, `U.S. Citizenship Percentage`)) %>%
  mutate(`Non-U.S. Citizenship Percentage` = `Non-U.S. CitizenE`/`Total PopulationE`,
         `Non-U.S. Citizenship Percentage` = ifelse(is.nan(`Non-U.S. Citizenship Percentage`),
                                                NA, `Non-U.S. Citizenship Percentage`)) %>% 
  mutate(`U.S. Citizenship (Naturalisation) Percentage` = `U.S. Citizen (Naturalisation)E`/`Total PopulationE`,
         `U.S. Citizenship (Naturalisation) Percentage` = ifelse(is.nan(`U.S. Citizenship (Naturalisation) Percentage`),
                                                    NA, `U.S. Citizenship (Naturalisation) Percentage`))

GEOID	NAME	Total PopulationE	Total PopulationM	U.S. CitizenE	U.S. CitizenM	U.S. Citizen (Naturalisation)E	U.S. Citizen (Naturalisation)M	Non-U.S. CitizenE	Non-U.S. CitizenM	geometry	U.S. Citizenship Percentage	Non-U.S. Citizenship Percentage	U.S. Citizenship (Naturalisation) Percentage
36081005000	Census Tract 50, Queens County, New York	0	12	0	12	0	12	0	12	MULTIPOLYGON (((-73.85767 4…	NA	NA	NA
36081006900	Census Tract 69, Queens County, New York	3785	598	2492	513	740	228	465	194	MULTIPOLYGON (((-73.92511 4…	0.6583884	0.1228534	0.1955086
36081007500	Census Tract 75, Queens County, New York	3982	681	2430	509	1026	283	464	307	MULTIPOLYGON (((-73.92915 4…	0.6102461	0.1165244	0.2576595

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Selecting Census Tract and Spatial Join

For the next step, we will choose the census tract for Corona.
Before that, we have to transform the spatial projection to prepare for the map.

We will check the projection for the census tract with st_crs() to print spatial data frames projections in the console, which will show that the EPSG (code for projection) is 4269.

st_crs(citizenship)

Check projection for the NTA data, which shows that the EPSG is 2263.

st_crs(nabes)

Whenever we are working with a New York City data, we want the projection to be 2263, therefore we will change the projection using st_transform.

citizenship_2263 <-  st_transform(citizenship, 2263)

Check projection again to make sure it works and it will show that the EPSG is now 2263.

st_crs(citizenship_2263)

We will select the fields from NTA to add to citizenship and remove unnecessary fields in the neighbourhood shapefile.

nabes_selected <- nabes %>%
  select(BoroName, BoroName, NTA2020, NTAName)

We can now perform spatial join!

citizenship_nabes <- citizenship_2263 %>%
  st_join(nabes_selected, 
          left = TRUE, # left -> defines it as left_join -- meaning all census tract are kept
          join = st_intersects, # join -> defines the join definition as "if they intersect"
          largest = TRUE) # largest -> if a census tract overlaps with more than one neighbourhood, name/join it is as the largest neighbourhood

---

Selecting Corona

corona <- citizenship_nabes %>% 
  filter(NTAName == "Corona" | NTAName == "North Corona")

Output

Maps

For this step, we can process the maps for each categories.

The map above shows the percentage of U.S. citizenship in Corona (by census tract). In general, the rate of U.S. citizenship in Corona is below 50%, with a concentration of higher percentage in the lower border and top-left corner of the neighbourhood.

The percentage of naturalised U.S. citizens in Corona has a low average of 20%, with the highest rate of 32% in census tract area 401.

Contrasting to the maps above, there is a higher average of non-U.S. citizenship in Corona with the highest percentage of 70%, especially in the center part the neighbourhood. The lowest percentage is illustrated on the bottom-left of the map, which is the LeFrak City Apartments.

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Summary Statistics for Corona and Neighbouring Districts (CB 3 and 4)

As we have insights on the citizenships in Corona, we can look at the rate in other neighbourhoods in Community Board 3 and 4 to further examine the data.

qcb3and4_citizenship_nabes_stats <- st_drop_geometry(citizenship_nabes) %>% 
  group_by(NTAName) %>% 
  filter(NTAName == "Corona" | NTAName == "North Corona" | NTAName == "East Elmhurst" | NTAName == "Jackson Heights" | NTAName == "Elmhurst") %>% 
  summarise(Borough = first(BoroName),
            `Est. Total Population` = sum(`Total PopulationE`),
            `Est. Total U.S. Citizenship` = sum(`U.S. CitizenE`),
            `Est. Total U.S. Citizenship (Naturalisation)` = sum(`U.S. Citizen (Naturalisation)E`),
            `Est. Total Non-U.S. Citizenship` = sum(`Non-U.S. CitizenE`)) %>% 
  mutate(`Est. U.S. Citizenship Percentage` = percent(`Est. Total U.S. Citizenship`/`Est. Total Population`, accuracy = 1L),
          `Est. U.S. Citizenship (Naturalisation) Percentage` = percent(`Est. Total U.S. Citizenship (Naturalisation)`/`Est. Total Population`, accuracy = 1L),
          `Est. Non-U.S. Citizenship Percentage` = percent(`Est. Total Non-U.S. Citizenship`/`Est. Total Population`, accuracy = 1L))

NTAName	Borough	Est. Total Population	Est. Total U.S. Citizenship	Est. Total U.S. Citizenship (Naturalisation)	Est. Total Non-U.S. Citizenship	Est. U.S. Citizenship Percentage	Est. U.S. Citizenship (Naturalisation) Percentage	Est. Non-U.S. Citizenship Percentage
Corona	Queens	69333	27728	16108	24183	40%	23%	35%
East Elmhurst	Queens	24321	10537	7492	5806	43%	31%	24%
Elmhurst	Queens	98560	31225	31629	33757	32%	32%	34%
Jackson Heights	Queens	89628	36016	25695	25539	40%	29%	28%
North Corona	Queens	39263	13955	7552	17113	36%	19%	44%

From the table above, we can see that Corona (North Corona and Corona) has the highest percentage of non-U.S. citizenship compared to other CB 3 and 4 neighbourhoods. However, we understand that there is a discrete number of undocumented residents in Corona, hence it can be assumed that the rate of non-U.S. citizens in Corona is higher than the indicated number.