Final Exam: Racial Gerrymandering in Tennessee
Brock
2025-05-02
## # A tibble: 36,607 × 3
## name label concept
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## 1 B01001A_001 Estimate!!Total: Sex by Age (White Alone)
## 2 B01001A_002 Estimate!!Total:!!Male: Sex by Age (White Alone)
## 3 B01001A_003 Estimate!!Total:!!Male:!!Under 5 years Sex by Age (White Alone)
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## 10 B01001A_010 Estimate!!Total:!!Male:!!30 to 34 years Sex by Age (White Alone)
## # ℹ 36,597 more rows## [1] "e9062f013a68317e4a158ee94eeb55a8f957224f"Introduction
This analysis examines racial composition changes across Tennessee’s U.S. House districts before and after the 2022 redistricting. Specifically, it explores whether racial gerrymandering may have occurred, with a focus on Davidson County.
Map: Percent White by Congressional District (2021)
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## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |=== | 5% | |===== | 8% | |======= | 10% | |========= | 13% | |=========== | 15% | |============ | 18% | |============== | 20% | |================ | 23% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |=========================== | 39% | |============================ | 40% | |============================== | 42% | |=============================== | 44% | |================================= | 47% | |=================================== | 50% | |===================================== | 52% | |======================================= | 55% | |======================================== | 58% | |========================================= | 58% | |========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 65% | |=============================================== | 67% | |=============================================== | 68% | |================================================= | 70% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |===================================================== | 75% | |====================================================== | 77% | |======================================================= | 79% | |======================================================== | 80% | |======================================================== | 81% | |========================================================= | 81% | |========================================================== | 83% | |=========================================================== | 84% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 90% | |================================================================ | 92% | |================================================================= | 92% | |================================================================== | 94% | |=================================================================== | 96% | |===================================================================== | 99% | |======================================================================| 100%Chart: Percent White by District (2021)
Horizontal Chart: Percent White by District (2022)
Davidson County: Race and Vote Share
Conclusion
The shift in racial composition after the 2022 redistricting, particularly in Davidson County, supports claims that minority voters may have been intentionally fragmented to reduce their political influence. The data visualization and voting trends show patterns consistent with racial gerrymandering.
# Required packages
library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(leaflet)
library(leaflet.extras2)
library(leafpop)
library(RColorBrewer)
# Load ACS variable names
tidycensus::load_variables(2022, "acs1", cache = TRUE)
# Set Census API Key
census_api_key("e9062f013a68317e4a158ee94eeb55a8f957224f", install = TRUE, overwrite = TRUE)
# Define variable for % White
VariableList <- c(pctwhite = "DP05_0077P")
Data2021 <- get_acs(
geography = "congressional district",
state = "TN",
variables = VariableList,
year = 2021,
survey = "acs1",
output = "wide",
geometry = TRUE
)
Data2021 <- rename(Data2021, Area = NAME, PctWhite = pctwhiteE, Range = pctwhiteM)
Map2021 <- mapview(
Data2021, zcol = "PctWhite", layer.name = "Percent White (2021)",
col.regions = brewer.pal(9, "Purples"),
popup = popupTable(Data2021, feature.id = FALSE, row.numbers = FALSE,
zcol = c("Area", "PctWhite", "Range"))
)
Map2021
Data2022 <- get_acs(
geography = "congressional district",
state = "TN",
variables = VariableList,
year = 2022,
survey = "acs1",
output = "wide",
geometry = TRUE
)
Data2022 <- rename(Data2022, Area = NAME, PctWhite = pctwhiteE, Range = pctwhiteM)
Map2022 <- mapview(
Data2022, zcol = "PctWhite", layer.name = "Percent White (2022)",
col.regions = brewer.pal(9, "Greens"),
popup = popupTable(Data2022, feature.id = FALSE, row.numbers = FALSE,
zcol = c("Area", "PctWhite", "Range"))
)
Map2022 # Updated to ensure identical variables as 2021
mygraph2021 <- ggplot(Data2021, aes(x = PctWhite, y = reorder(Area, PctWhite))) +
geom_errorbarh(aes(xmin = PctWhite - Range, xmax = PctWhite + Range)) +
geom_point(size = 3, color = "#228B22") +
theme_minimal(base_size = 13) +
labs(title = "Percent White by Congressional District (2021)",
subtitle = "Margins represent sampling error", x = "% White", y = "")
print(mygraph2021)
mygraph2022b <- ggplot(Data2022, aes(x = PctWhite, y = reorder(Area, PctWhite))) +
geom_errorbarh(aes(xmin = PctWhite - Range, xmax = PctWhite + Range)) +
geom_point(size = 3, color = "#099d91") +
theme_minimal(base_size = 12.5) +
labs(title = "Percent White by Congressional District (2022)",
subtitle = "Tennessee - ACS 2022 Data",
x = "Percentage",
y = "District")
mygraph2022b
Davidson2024 <- read.csv("https://github.com/drkblake/Data/raw/refs/heads/main/Davidson2024.csv")
precinctPlot <- ggplot(Davidson2024, aes(x = Pct_Nonwhite, y = Pct_Harris, color = Winner)) +
geom_point(size = 3, alpha = 0.8) +
scale_color_manual(values = c("Harris" = "#2E8B57", "Trump" = "#DC143C")) +
theme_minimal(base_size = 13) +
labs(title = "Nonwhite Population vs. Harris Vote Share by Precinct",
subtitle = "Davidson County, 2024",
x = "% Nonwhite Residents", y = "% Voted for Harris")
print(precinctPlot)