Distribution of race by ZCTA

Jerry Tuttle

7 hours ago

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What’s a ZCTA?

For years I assumed that a city consists of a number of zipcodes, and the city’s population was simply the sum of the populations those zipcodes.

It turns out this isn’t quite right.

Zipcodes are postal delivery routes. They are a collection of lines (streets) and points (delivery boxes) designed purely for mail trucks to drop off letters as efficiently as possible. They do not have official, enclosed borders. Also, zipcode definitions frequently change.

To make geographical analysis possible, the Census Bureau invented ZCTAs (ZIP Code Tabulation Areas) using Census blocks into artificial, enclosed geographic polygons. Each resulting shape is given a 5-digit name that matches the dominant zipcode of the greatest number of addresses inside it.

The U.S. Census Bureau publishes decennial population by city, and also by these smaller ZCTAs, but never by actual USPS zipcodes. (They do publish other statistics by zipcode.)

A ZCTA polygon is unlikely to perfectly match the geographical area of the dominant zipcode. Moreover, a ZCTA may actually cross a municipal boundary. For example, ZCTA 11001 (Floral Park) and 11040 (New Hyde Park) span both Queens (NYC) and Nassau Counties, but most of their area and population are in Nassau County. So counting population in such ZCTAs in full for NYC would overstate NYC’s population. Adding the populations of all the ZCTAs is not going to equal the population of NYC.

Distribution of race in a smaller city

I am using the R package tidycensus to pull data from the U.S. Census Bureau API into R. Census data includes many possible variables. Some broad categories are population, median household income, housing units, median rent, education, occupation, etc. For convenience I chose population by race for this project, although I could have chosen something else. You need to request a one-time Census API key from https://api.census.gov/data/key_signup.html .

With the above as background, I decided to examine the distribution of race in a smaller city, Fort Myers, Florida. There are 13 inhabited ZCTAs in the greater Fort Myers area, plus some miscellaneous ZCTAs such as for post office boxes. A map of the greater Fort Myers area with its ZCTAs, which extend beyond Fort Myers City, is as follows, where the dashed red line indicates Fort Myers City limits:

I used the classification of 2020 Census Table P2 data, which separates Hispanic people of any race from non-Hispanic people who are then classified by race. These are mutually exclusive categories. (I recognize some awkwardness to this, and I am not attempting to describe every racial and ethnic category. For convenience I will use the word “race”, and I apologize if this offends anyone; Hispanic is not a race.) I combined five smaller non-Hispanic races including Multiracial into an Other category. The following is the distribution of race of Fort Myers City, which had a 2020 population of 86,395:

Race (NH = Non-Hispanic)	Percentage
White (NH)	47.51%
Hispanic (any race)	23.61%
Black (NH)	21.87%
Other (NH)	7.01%

Superimposing the above table as a pie chart onto the map of Fort Myers City gives the following:

Next, I collected the data and drew the pie chart for a single ZCTA, 33905. This ZCTA has a greater percentage of Hispanics than Fort Myers City, and a smaller percentage of blacks.

I initially tried to create a single map with a separate pie chart for every ZCTA, but that map was too busy because many ZCTAs were too close to each other. I decided what I really cared about was the ZCTAs with the largest percentage of Hispanics and the ZCTAs with the largest percentage of blacks:

What’s the value in all this?

What’s the value in all this? Your pharmacy, department store, and video streaming service all use information about you to provide personalized recommendations based on what they think you’ll buy or watch next. Do our political parties produce customized political messages for different neighborhoods based on what they think are their residents’ major political issues? Is race/ethnicity an important (although of course not the only) variable in people’s political issues? I think maybe our political parties should think about making greater use of demographic and geographic information when deciding how to communicate with voters.

R code

There is a lot of R code. I was considerably helped by Microsoft Copilot and Google Gemini.


# Race by ZCTA
# MY_KEY <- "xxxxx"
# Sys.setenv(CENSUS_API_KEY = "MY_KEY")

library(tidycensus)
library(tigris)
library(dplyr)
library(tidyr)
library(stringr)
library(sf)
library(ggplot2)
library(scatterpie)
library(gridExtra)

options(tigris_use_cache = TRUE)

# ------------------------------------------------------------
# Variable Definitions
# ------------------------------------------------------------

# 2020 Census P2 (Place-Level)
vars_p2 <- c(
  total     = "P2_001N",
  hispanic  = "P2_002N",
  white_nh  = "P2_005N",
  black_nh  = "P2_006N",
  aian_nh   = "P2_007N",
  asian_nh  = "P2_008N",
  nhpi_nh   = "P2_009N",
  other_nh  = "P2_010N",
  multi_nh  = "P2_011N"
)

# 2020 Census DHC ZCTA Variables
vars_p5 <- c(
  total_pop = "P5_001N",
  white_nh  = "P5_003N",
  black_nh  = "P5_004N",
  aian_nh   = "P5_005N",
  asian_nh  = "P5_006N",
  nhpi_nh   = "P5_007N",
  other_nh  = "P5_008N",
  multi_nh  = "P5_009N",
  hispanic  = "P5_010N"
)

# ACS Variables (for individual ZCTA analysis)
vars_acs <- c(
  total     = "B03002_001",
  white_nh  = "B03002_003",
  black_nh  = "B03002_004",
  aian_nh   = "B03002_005",
  asian_nh  = "B03002_006",
  nhpi_nh   = "B03002_007",
  other_nh  = "B03002_008",
  multi_nh  = "B03002_009",
  hispanic  = "B03002_012"
)

# ------------------------------------------------------------
# Geography
# ------------------------------------------------------------

places_fl <- places(state = "FL", year = 2020)

fort_myers_shape < - places_fl %>%
  filter(NAME == "Fort Myers")

zctas_fl <- zctas(
  year = 2020,
  cb = TRUE,
  starts_with = "339"
)

# ------------------------------------------------------------
# CITY OF FORT MYERS RACE TABLE
# ------------------------------------------------------------

race_eth_city <- get_decennial(
  geography = "place",
  state = "FL",
  variables = vars_p2,
  year = 2020,
  sumfile = "pl",
  output = "wide"
) %>%
  filter(NAME == "Fort Myers city, Florida")

race_city < - race_eth_city %>%
  transmute(
    white_nh,
    hispanic,
    black_nh,
    other =
      aian_nh +
      asian_nh +
      nhpi_nh +
      other_nh +
      multi_nh
  )

race_table_city < - race_city %>%
  pivot_longer(
    everything(),
    names_to = "race",
    values_to = "population"
  ) %>%
  mutate(
    pct = population / sum(population)
  )

print(race_table_city)

# ------------------------------------------------------------
# CITY OF FORT MYERS MAP
# ------------------------------------------------------------

ggplot(fort_myers_shape) +
  geom_sf(
    fill = "lightblue",
    color = "black"
  ) +
  labs(
    title = "City of Fort Myers (2020 Census Boundary)"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text  = element_blank(),
    axis.ticks = element_blank()
  )

# ------------------------------------------------------------
# CITY PIE CHART MAP
# ------------------------------------------------------------

ctr <- st_coordinates(
  st_centroid(
    st_union(fort_myers_shape)
  )
)

pie_df_city <- cbind(
  data.frame(
    x = ctr[1],
    y = ctr[2]
  ),
  race_city
)

ggplot() +
  geom_sf(
    data = fort_myers_shape,
    fill = "lightblue",
    color = "black"
  ) +
  geom_scatterpie(
    data = pie_df_city,
    aes(x = x, y = y, r = 0.03),
    cols = names(race_city)
  ) +
  scale_fill_manual(
    values = c(
      white_nh = "lightgray",
      hispanic = "orange",
      black_nh = "steelblue",
      other = "purple"
    )
  ) +
  labs(
    title = "Fort Myers City Racial Composition",
    fill = "Race/Ethnicity"
  ) +
  coord_sf() +
  theme(
    plot.title = element_text(face = "bold"),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.title = element_text(face = "bold"),
    legend.text = element_text(face = "bold")
  )

# ------------------------------------------------------------
# GREATER FORT MYERS ZCTA MAP
# ------------------------------------------------------------

fort_myers_zctas < - zctas_fl %>%
  st_filter(fort_myers_shape,
            .predicate = st_intersects)

ggplot() +
  geom_sf(
    data = fort_myers_zctas,
    fill = "lightblue",
    color = "black",
    alpha = 0.6
  ) +
  geom_sf(
    data = fort_myers_shape,
    fill = NA,
    color = "red",
    linewidth = 1.8,
    linetype = "dashed"
  ) +
  geom_sf_label(
    data = fort_myers_zctas,
    aes(label = ZCTA5CE20),
    size = 3,
    face = "bold"
  ) +
  labs(
    title = "Greater Fort Myers Area by ZCTA",
    subtitle = "Dashed red line indicates city limits", 
    x = "",
    y = "") + 
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "bold"),
    axis.text = element_blank(),
    axis.ticks = element_blank()
  )

# ------------------------------------------------------------
# SINGLE ZCTA ANALYSIS (33905)
# ------------------------------------------------------------

race_eth_33905 <- get_acs(
  geography = "zcta",
  variables = vars_acs,
  year = 2020,
  output = "wide"
) %>%
  filter(GEOID == "33905")

race_33905 < - race_eth_33905 %>%
  transmute(
    white_nh = white_nhE,
    hispanic = hispanicE,
    black_nh = black_nhE,
    other =
      aian_nhE +
      asian_nhE +
      nhpi_nhE +
      other_nhE +
      multi_nhE
  )

race_table_33905 < - race_33905 %>%
  pivot_longer(
    everything(),
    names_to = "race",
    values_to = "population"
  ) %>%
  mutate(
    pct = population / sum(population)
  )

print(race_table_33905)

# ------------------------------------------------------------
# ZCTA 33905 PIE MAP
# ------------------------------------------------------------

zcta_33905 < - zctas_fl %>%
  filter(ZCTA5CE20 == "33905")

coords_33905 <- st_coordinates(
  st_centroid(zcta_33905)
)

pie_df_33905 < - race_33905 %>%
  mutate(
    x = coords_33905[1,1],
    y = coords_33905[1,2]
  )


ggplot() +
  geom_sf(
    data = zcta_33905,
    fill = "lightblue",
    color = "black"
  ) +
  geom_scatterpie(
    data = pie_df_33905,
    aes(x = x, y = y, r = 0.025),
    cols = c(
      "white_nh",
      "hispanic",
      "black_nh",
      "other"
    )
  ) +
  scale_fill_manual(
    values = c(
      white_nh = "lightgray",
      hispanic = "orange",
      black_nh = "steelblue",
      other = "purple"
    )
  ) +
  labs(
    title = "ZCTA 33905 Racial Composition",
    fill = "Race/Ethnicity"
  ) +
  coord_sf() +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold"),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank()
  )

# ------------------------------------------------------------
# ALL FORT MYERS AREA ZCTAS
# ------------------------------------------------------------

fort_myers_list <- c(
  "33901","33903","33905","33907",
  "33908","33912","33913","33916",
  "33917","33919","33931","33965",
  "33966","33967"
)

fort_myers_master <- get_decennial(
  geography = "zcta",
  variables = vars_p5,
  year = 2020,
  sumfile = "dhc",
  output = "wide",
  geometry = TRUE
)

fort_myers_projected < - fort_myers_master %>%
  filter(GEOID %in% fort_myers_list) %>%
  mutate(
    zcta = str_extract(NAME, "\\d{5}"),
    pct_hispanic =
      100 * hispanic / total_pop,
    pct_black_nh =
      100 * black_nh / total_pop,
    minority_other_nh =
      aian_nh +
      asian_nh +
      nhpi_nh +
      other_nh +
      multi_nh
  ) %>%
  st_transform(2237)

# ------------------------------------------------------------
# POPULATION MATRIX
# ------------------------------------------------------------

fort_myers_population_table < - fort_myers_projected %>%
  st_drop_geometry() %>%
  select(
    zcta,
    total_pop,
    white_nh,
    hispanic,
    black_nh,
    minority_other_nh
  ) %>%
  arrange(zcta)

print(fort_myers_population_table)

# ------------------------------------------------------------
# CENTROIDS
# ------------------------------------------------------------

centroids_master < - fort_myers_projected %>%
  st_centroid() %>%
  mutate(
    X = st_coordinates(.)[,1],
    Y = st_coordinates(.)[,2]
  ) %>%
  st_drop_geometry()

top_5_hispanic < - centroids_master %>%
  slice_max(
    pct_hispanic,
    n = 5
  ) %>%
  mutate(
    label_text =
      paste0(
        zcta,
        "\n",
        round(pct_hispanic, 1),
        "%"
      )
  )

top_5_black < - centroids_master %>%
  slice_max(
    pct_black_nh,
    n = 5
  ) %>%
  mutate(
    label_text =
      paste0(
        zcta,
        "\n",
        round(pct_black_nh, 1),
        "%"
      )
  )

# ------------------------------------------------------------
# BASE MAP
# ------------------------------------------------------------

base_map <- ggplot() +
  geom_sf(
    data = fort_myers_projected,
    fill = "lightblue",
    color = "gray60"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(
      face = "bold",
      hjust = .5
    ),
    axis.title = element_blank(),
    axis.text = element_blank(),
    panel.grid = element_blank()
  ) +
  coord_sf(datum = NA)

# ------------------------------------------------------------
# TOP 5 HISPANIC
# ------------------------------------------------------------

map_hispanic <- base_map +
  geom_text(
    data = top_5_hispanic,
    aes(
      X,
      Y,
      label = label_text
    ),
    face = "bold"
  ) +
  labs(
    title = "Top 5 ZCTAs by Hispanic %"
  )

# ------------------------------------------------------------
# TOP 5 BLACK NH
# ------------------------------------------------------------

map_black <- base_map +
  geom_text(
    data = top_5_black,
    aes(
      X,
      Y,
      label = label_text
    ),
    face = "bold"
  ) +
  labs(
    title = "Top 5 ZCTAs by Black (NH) %"
  )

# ------------------------------------------------------------
# DISPLAY FINAL MAPS
# ------------------------------------------------------------

grid.arrange(
  map_hispanic,
  map_black,
  ncol = 2
)

End < >

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What’s a ZCTA?

Distribution of race in a smaller city

What’s the value in all this?

R code

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