Mini-Assignment #5
Will Georgia
2024-10-18
# Load packages:
library(tidycensus)
library(tidyr)
library(sf)## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUElibrary(tmap)## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')library(jsonlite)
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(httr)
library(jsonlite)
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library(skimr)
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## layout# Import data file:
fe_data <- as.data.frame(read_csv("FATAL ENCOUNTERS - Form Responses.csv"))## New names:
## • `` -> `...32`
## • `` -> `...33`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 31498 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (27): Name, Gender, Race, Race with imputations, Imputation probability,...
## dbl (6): Unique ID, Age, Longitude, UID Temporary, Unique ID formula, Uniqu...
## lgl (2): ...32, ...33
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.fe_data <- fe_data %>% rename(Highest_force=`Highest level of force`,Intended_force=`Intended use of force (Developing)`)
fe_data_filtered <-suppressMessages(
fe_data %>%
filter(`State` == "GA", `Location of death (county)` %in% c("Fulton","Cobb","Clayton","Dekalb","Gwinnett")) %>%
drop_na("Longitude", "Latitude"))
fe_data_sf <- st_as_sf(x=fe_data_filtered, coords = c("Longitude", "Latitude"), crs=4326)# activate census API
tidycensus::census_api_key(Sys.getenv("census_api"))## To install your API key for use in future sessions, run this function with `install = TRUE`.atl_tracts <- suppressMessages(
get_acs(geography = "tract",
state = "GA",
county = c("Fulton","Cobb","Clayton","Dekalb","Gwinnett"),
variables = c(hhincome = 'B19019_001'),
year = 2022,
survey = "acs5",
geometry = TRUE,
output = "wide")
)%>%
rename(hhincome = hhincomeE) %>%
st_transform(4326)## | | | 0% | |= | 1% | |=== | 5% | |================ | 23% | |=========================== | 38% | |======================================================================| 100%atl_counties <- suppressMessages(
get_acs(geography = "county",
state = "GA",
county = c("Fulton","Cobb","Clayton","Dekalb","Gwinnett"),
variables = c(hhincome = 'B19019_001'),
year = 2022,
survey = "acs5",
geometry = TRUE,
output = "wide")
)%>%
rename(hhincome = hhincomeE) %>%
st_transform(4326)## | | | 0% | | | 1% | |= | 1% | |=== | 4% | |============ | 17% | |===================== | 30% | |============================== | 42% | |====================================== | 55% | |=============================================== | 67% | |======================================================== | 80% | |================================================================= | 93% | |======================================================================| 100%head(atl_tracts$geometry)## Geometry set for 6 features
## Geometry type: MULTIPOLYGON
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## Bounding box: xmin: -84.44623 ymin: 33.57743 xmax: -84.16538 ymax: 33.96102
## Geodetic CRS: WGS 84
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## Geodetic CRS: WGS 84## MULTIPOLYGON (((-84.27709 33.95761, -84.27519 3...## MULTIPOLYGON (((-84.34981 33.66384, -84.3498 33...## MULTIPOLYGON (((-84.84931 33.51318, -84.84429 3...## MULTIPOLYGON (((-84.7394 33.95907, -84.73961 33...## MULTIPOLYGON (((-84.45893 33.56541, -84.45863 3...head(fe_data_sf$geometry)## Geometry set for 6 features
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## Geodetic CRS: WGS 84
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## POINT (-84.41753 33.74908)## POINT (-84.38759 33.78627)fe_atl_tract <- st_join(atl_tracts, fe_data_sf, join = st_intersects) %>% drop_na("Highest_force")
fe_atl_counties <- st_join(atl_counties, fe_data_sf, join = st_intersects) %>% drop_na("Highest_force")tmap_mode("view")## tmap mode set to interactive viewingtm_shape(fe_atl_tract) + tm_dots(col="Race with imputations") +
tm_style("albatross", frame.lwd=10)ggplot(data = fe_atl_tract) +
geom_bar(mapping = aes(x=Gender, fill=Highest_force)) +
labs(x = "Gender",
title = "Analyzing Police-caused Fatalities in Metro Atlanta",
subtitle = "Comparing Gender vs. Highest Use of Force",
y = "Count of Deaths")
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title = "Analyzing Police-caused Fatalities in Metro Atlanta",
subtitle = "Comparing Race vs. Highest Use of Force",
y = "Count of Deaths") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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Conclusions
Reviewing police killings in
America is frankly sobering. We, the people, endow our police forces
with an incredibly important task - ensuring our safety and peace of
mind as we freely live out our lives. We also expect the police to
respect our rights as citizens to live without fear of improper or
excessive use of force. As a result, police killings require the highest
level of scrutiny to understand whether the force was justified or an
overreach.
Brian Burghart has done an admirable job compiling data on recent killings. On closer look, the data do appear to point to some concerning trends. Since the United States is a very large, very diverse nation, the data in this analysis was first pared down to the five counties making up the core of metro Atlanta. With a population that has a wide range of demographic features, Atlanta presents a good snapshot of the overall dataset.
To start, a simple mapping of killings across the Atlanta region incorporating the race of the deceased reveals a few apparent trends. First, the largest cluster is near downtown and just to the south. More notably, across the region, killings of African-American individuals appear to heavily outnumber those of any other group. That presents a significant problem when understanding that African-Americans make up just over 33% of the area population compared to Whites making up about 46% (per the Metro Atlanta Chamber of Commerce).
Next, a series of bar charts were created to review a couple different variables Burghart compiled. Killings by gender show that male deaths are far more numerous than female deaths. And when incorporating the highest use of force police used in the confrontations, another significant takeaway emerges. Deaths among males seem to result from police using firearms much more frequently than deaths among females. Do men actually pose a more significant threat, or do police resort to guns too quickly with males?
Comparing race against highest use of force is also unsettling as the bar chart shows just how often police fatalities are a result of firearms. Since serious injury and death occur all too easily with guns, should police be using that brute force in these cases?
Finally, reviewing race against what Burghart has called “intended use of force” is most disturbing. Here, it’s clear that Atlanta area cops more often than not (and across all races) kill people as a result of their intention to use deadly force. Were the intentions of the police aligned with the threats they faced in these situations?
Overall, the data shows that there are serious questions that deserve further investigation and analysis. And Metro Atlanta police departments owe it to citizens to help provide answers to those questions. After all, full faith and trust in law enforcement is key to a free and fair society.