Mapping the proportion of Black, white, asian, hispanic and latino
people in NYC.
Methods
The borough boundaries are represented with a shapefile downloaded
from NYC Open Data. The data is from the 2020 decennial. This data
includes:
The total population The number of people who are hispanic
or latino The number of people who are Black The number of
people who are asian *The number of people who are white
The percent of each racial category in each tract was calculated, and
the areas with no residents are left blank.
#create raw table
race_NY_raw <- get_decennial(geography= "tract",
variables = c(total_pop = "P1_001N",
hisp_latin = "P2_002N",
black_alone = "P1_004N",
asian_alone = "P1_006N",
white_alone = "P1_003N"),
year = 2020,
state = "NY",
county = c("Kings","New York","Bronx","Queens","Richmond"),
geometry = T,
output = "wide")
#Making the data pretty
race_NY <- race_NY_raw|>
mutate(percent_hisp_latin = hisp_latin/total_pop,
percent_black = black_alone/total_pop,
percent_asian = asian_alone/total_pop,
percent_white = white_alone/total_pop)|>
select(GEOID, NAME, total_pop, percent_hisp_latin, percent_black, percent_asian,
percent_white, geometry)
## import borough shapefiles from NYC Open Data
boros <- st_read("~/Desktop/Stuff & Things/School/Grad School/DUE Methods 1/part2/data/raw/geo/Borough Boundaries.geojson")
###Results
ggplot(data = race_NY, mapping = aes(fill = percent_hisp_latin)) +
geom_sf(color = "#fff") +
theme_void() +
scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
palette = "YlOrRd",
direction = 1,
na.value = "transparent",
name="Percent Hispanic or Latino",
labels=percent_format(accuracy = 1L)) +
labs(
title = "Hispanic or Latino Population in NYC",
caption = "Source: Decennial, 2020")+
geom_sf(data = boros |> filter(boro_name == "Brooklyn"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Manhattan"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Bronx"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Staten Island"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Queens"),
color = "black", fill = NA, lwd = .25)

The map above shows that there are large concentrations of Hispanic
and Latino populations in the South Bronx, Bushwick, Brownsville and
Cyprus Hills.
ggplot(data = race_NY, mapping = aes(fill = percent_black)) +
geom_sf(color = "#fff") +
theme_void() +
scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
palette = "RdPu",
direction = 1,
na.value = "transparent",
name="Percent Black",
labels=percent_format(accuracy = 1L)) +
labs(
title = "Black Population in NYC",
caption = "Source: Decennial, 2020")+
geom_sf(data = boros |> filter(boro_name == "Brooklyn"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Manhattan"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Bronx"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Staten Island"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Queens"),
color = "black", fill = NA, lwd = .25)

This map shows higher concentrations of Black populations in North
Bronx, East Queens and North Brooklyn.
ggplot(data = race_NY, mapping = aes(fill = percent_asian)) +
geom_sf(color = "#fff") +
theme_void() +
scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
palette = "YlGn",
direction = 1,
na.value = "transparent",
name="Percent Asian",
labels=percent_format(accuracy = 1L)) +
labs(
title = "Asian Population in NYC",
caption = "Source: Decennial, 2020")+
geom_sf(data = boros |> filter(boro_name == "Brooklyn"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Manhattan"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Bronx"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Staten Island"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Queens"),
color = "black", fill = NA, lwd = .25)

This map shows that the highest concentration of the asian population
is in North Queens and South Brooklyn.
ggplot(data = race_NY, mapping = aes(fill = percent_white)) +
geom_sf(color = "#fff") +
theme_void() +
scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
palette = "Blues",
direction = 1,
na.value = "transparent",
name="Percent White",
labels=percent_format(accuracy = 1L)) +
labs(
title = "White Population in NYC",
caption = "Source: Decennial, 2020")+
geom_sf(data = boros |> filter(boro_name == "Brooklyn"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Manhattan"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Bronx"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Staten Island"),
color = "black", fill = NA, lwd = .25)+
geom_sf(data = boros |> filter(boro_name == "Queens"),
color = "black", fill = NA, lwd = .25)

This map shows that the white population is all across New York City,
but tend to avoid Northeast Brooklyn, East Queens and the Bronx.
---
title: "Race Distribution in NYC"
output: html_notebook
---

Mapping the proportion of Black, white, asian, hispanic and latino people in NYC. 

```{r, message=FALSE, results='hide'}
library(tidyverse)
library(tidycensus)
library(sf)
library(scales)
library(viridisLite)
library(RColorBrewer)
```

### Methods
The borough boundaries are represented with a shapefile downloaded from NYC Open Data. 
The data is from the 2020 decennial. This data includes:

*The total population
*The number of people who are hispanic or latino
*The number of people who are Black
*The number of people who are asian
*The number of people who are white

The percent of each racial category in each tract was calculated, and the areas 
with no residents are left blank.

```{r, message=FALSE, results='hide'}
#create raw table
race_NY_raw <- get_decennial(geography= "tract",
  variables = c(total_pop = "P1_001N",
                hisp_latin = "P2_002N",
                black_alone = "P1_004N",
                asian_alone = "P1_006N",
                white_alone = "P1_003N"),
  year = 2020,
  state = "NY",
  county = c("Kings","New York","Bronx","Queens","Richmond"),
  geometry = T,
  output = "wide")

#Making the data pretty
race_NY <- race_NY_raw|>
  mutate(percent_hisp_latin = hisp_latin/total_pop,
         percent_black = black_alone/total_pop,
         percent_asian = asian_alone/total_pop,
         percent_white = white_alone/total_pop)|>
  select(GEOID, NAME, total_pop, percent_hisp_latin, percent_black, percent_asian, 
         percent_white, geometry)

## import borough shapefiles from NYC Open Data
boros <- st_read("~/Desktop/Stuff & Things/School/Grad School/DUE Methods 1/part2/data/raw/geo/Borough Boundaries.geojson")

```


###Results

```{r}
ggplot(data = race_NY, mapping = aes(fill = percent_hisp_latin))  + 
  geom_sf(color = "#fff") +
  theme_void() +
  scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
                       palette = "YlOrRd",
                       direction = 1,
                       na.value = "transparent",
                       name="Percent Hispanic or Latino",
                       labels=percent_format(accuracy = 1L)) +
  labs(
    title = "Hispanic or Latino Population in NYC",
    caption = "Source: Decennial, 2020")+ 
  geom_sf(data = boros |> filter(boro_name == "Brooklyn"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Manhattan"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Bronx"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Staten Island"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Queens"), 
          color = "black", fill = NA, lwd = .25)

```

The map above shows that there are large concentrations of Hispanic and Latino populations 
in the South Bronx, Bushwick, Brownsville and Cyprus Hills.

```{r}
ggplot(data = race_NY, mapping = aes(fill = percent_black))  + 
  geom_sf(color = "#fff") +
  theme_void() +
  scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
                       palette = "RdPu",
                       direction = 1,
                       na.value = "transparent",
                       name="Percent Black",
                       labels=percent_format(accuracy = 1L)) +
  labs(
    title = "Black Population in NYC",
    caption = "Source: Decennial, 2020")+ 
  geom_sf(data = boros |> filter(boro_name == "Brooklyn"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Manhattan"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Bronx"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Staten Island"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Queens"), 
          color = "black", fill = NA, lwd = .25)
```
This map shows higher concentrations of Black populations in North Bronx, East Queens and North Brooklyn.


```{r}
ggplot(data = race_NY, mapping = aes(fill = percent_asian))  + 
  geom_sf(color = "#fff") +
  theme_void() +
  scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
                       palette = "YlGn",
                       direction = 1,
                       na.value = "transparent",
                       name="Percent Asian",
                       labels=percent_format(accuracy = 1L)) +
  labs(
    title = "Asian Population in NYC",
    caption = "Source: Decennial, 2020")+ 
  geom_sf(data = boros |> filter(boro_name == "Brooklyn"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Manhattan"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Bronx"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Staten Island"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Queens"), 
          color = "black", fill = NA, lwd = .25)
```

This map shows that the highest concentration of the asian population is in North Queens and South Brooklyn.


```{r}
ggplot(data = race_NY, mapping = aes(fill = percent_white))  + 
  geom_sf(color = "#fff") +
  theme_void() +
  scale_fill_distiller(breaks=c(0, .20, .40, .60, .80, 1.00),
                       palette = "Blues",
                       direction = 1,
                       na.value = "transparent",
                       name="Percent White",
                       labels=percent_format(accuracy = 1L)) +
  labs(
    title = "White Population in NYC",
    caption = "Source: Decennial, 2020")+ 
  geom_sf(data = boros |> filter(boro_name == "Brooklyn"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Manhattan"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Bronx"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Staten Island"), 
          color = "black", fill = NA, lwd = .25)+
  geom_sf(data = boros |> filter(boro_name == "Queens"), 
          color = "black", fill = NA, lwd = .25)
```

This map shows that the white population is all across New York City, but tend to avoid Northeast Brooklyn, East Queens and the Bronx. 
