Analysis of Fatal Shooting Outcomes in New York City

Introduction

Purpose and Data Sources for NYC Shooting Fatality Analysis

This project analyzes fatal shooting incidents in New York City using two NYPD datasets. The historic dataset covers incidents from 2006 through 2024, and the 2025 year to date dataset provides the most recent records. Together, these data allow an examination of long term patterns and current trends in fatal shootings across boroughs.

The analysis includes descriptive summaries of borough level proportions and counts, followed by a logistic regression model that evaluates whether victim sex and age group are associated with the likelihood of death. These approaches provide a clear view of how demographic factors and geographic context relate to fatal shooting outcomes in New York City.

knitr::include_graphics("Figures/img NYC.jpg")

library(tidyverse)     
library(tidycensus)
library(sf)
library(mapview)
library(knitr)
library(kableExtra)
library(broom)

library(tidyverse)
dat1 = read.csv("./Data Folder/Shooting_Historic.csv")
dat2 = read.csv("./Data Folder/Shooting_2025.csv")

dat1 = dat1 |>
  mutate(STATISTICAL_MURDER_FLAG = STATISTICAL_MURDER_FLAG %in% c(TRUE, "true", "True"))

dat2 = dat2 |>
  mutate(STATISTICAL_MURDER_FLAG = STATISTICAL_MURDER_FLAG %in% c(TRUE, "true", "True"))

dat_all = bind_rows(
dat1 |> mutate(source = "historic"),
dat2 |> mutate(source = "y2025")
)

Descriptive Analysis

This section presents descriptive summaries of fatal shooting incidents across the five boroughs. It focuses on understanding basic patterns in proportions and counts before moving into spatial visualization and regression modeling.

Borough-Level Analysis (2006–2024)

This subsection summarizes the number of fatal shootings, the total number of shooting incidents, and the resulting proportion of fatal outcomes for each borough using the historic dataset from 2006 to 2024. It provides a clear comparison of how often shootings have resulted in death across boroughs over the long term.

dat_all_sf = dat_all |>
  distinct(INCIDENT_KEY, .keep_all = TRUE) |>
  filter(!is.na(Latitude) & !is.na(Longitude)) |>
  filter(Latitude != 0 & Longitude != 0) |>
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326)

dat1_boro = dat1 |>
  group_by(BORO) |>
  summarise(
    n_murder = sum(STATISTICAL_MURDER_FLAG),  
    n_total = n(),                             
    prop_murder = n_murder / n_total,
    .groups = "drop"
  )

dat1_boro |> 
  mutate(
    n_murder = format(n_murder, big.mark = ","),
    n_total = format(n_total, big.mark = ","),
    prop_murder = sprintf("%.2f", prop_murder)
  ) |> 
  kable(
    col.names = c("Borough", "Murder cases", "Total shootings", "Murder proportion"),
    align = c("l", "r", "r", "r"),
    caption = "Fatal Shooting Proportions by Borough (2006 to 2024)"
  ) |> 
  kable_styling(full_width = FALSE, position = "center")

Fatal Shooting Proportions by Borough (2006 to 2024)

Borough	Murder cases	Total shootings	Murder proportion
BRONX	1,728	8,834	0.20
BROOKLYN	2,277	11,685	0.19
MANHATTAN	719	3,977	0.18
QUEENS	871	4,426	0.20
STATEN ISLAND	170	822	0.21

• Comments Fatality proportions are fairly similar across boroughs, ranging from 0.18 to 0.21. Staten Island shows the highest proportion, though it has the fewest total shootings. Brooklyn and the Bronx have the largest numbers of fatal cases due to their higher overall shooting volume. Manhattan shows the lowest proportion. Overall, differences in lethality are small, while differences in total incident counts are much larger.

Borough-Level Analysis (All Years Combined)

This subsection presents the number of fatal shootings, the total number of shooting incidents, and the resulting proportion of fatal outcomes for each borough using the full dataset that includes all available years through 2025. This updated summary shows how borough level fatality patterns change when recent incidents are incorporated.

dat_all_boro_prop = dat_all |>
  group_by(BORO) |>
  summarise(
    n_murder = sum(STATISTICAL_MURDER_FLAG),
    n_total  = n(),
    prop_murder = n_murder / n_total,
    .groups = "drop"
  )

dat_all_boro_count = dat_all |>
  group_by(BORO) |>
  summarise(
    n_murder = sum(STATISTICAL_MURDER_FLAG),
    n_total  = n(),
    .groups = "drop"
  )

nyc_boro = tigris::counties(state = "NY", cb = TRUE) |>
  filter(NAME %in% c("Bronx", "Kings", "New York", "Queens", "Richmond"))

boro_to_county = function(df) {
  df |>
    mutate(
      county_name = case_when(
        BORO == "MANHATTAN"     ~ "New York",
        BORO == "BROOKLYN"      ~ "Kings",
        BORO == "BRONX"         ~ "Bronx",
        BORO == "QUEENS"        ~ "Queens",
        BORO == "STATEN ISLAND" ~ "Richmond"
      )
    )
}

dat_all_boro_prop = boro_to_county(dat_all_boro_prop)
dat_all_boro_count = boro_to_county(dat_all_boro_count)

dat_all_boro_prop |>
  select(BORO, n_murder, n_total, prop_murder) |>
  mutate(
    BORO        = stringr::str_to_title(stringr::str_to_lower(BORO)),
    n_murder    = format(n_murder, big.mark = ","),
    n_total     = format(n_total, big.mark = ","),
    prop_murder = sprintf("%.2f", prop_murder)
  ) |>
  kable(
    col.names = c("Borough", "Murder cases", "Total shootings", "Murder proportion"),
    caption   = "Fatal Shooting Proportions by Borough (All Years Including 2025)",
    align     = c("l", "r", "r", "r")
  ) |>
  kable_styling(full_width = FALSE, position = "center")

Fatal Shooting Proportions by Borough (All Years Including 2025)

Borough	Murder cases	Total shootings	Murder proportion
Bronx	1,728	9,132	0.19
Brooklyn	2,277	11,951	0.19
Manhattan	719	4,088	0.18
Queens	871	4,512	0.19
Staten Island	170	830	0.20

• Comments Fatality proportions remain similar across boroughs when all years are included, mostly around 0.18 to 0.20. Brooklyn and the Bronx still account for the highest number of fatal shootings due to their larger total incident counts. Manhattan continues to show the lowest fatality proportion. Overall, adding recent years does not substantially change the borough-level patterns observed in the historic data.

Geographic Visualization

This section presents maps that visually display geographic differences in fatal shooting proportions and counts. The maps help reveal spatial patterns that are less obvious in tables or descriptive summaries.

Maps of Murder Proportion　(2006–2024)

This map shows the proportion of fatal shootings within each borough using historic data between 2006 and 2024, emphasizing long established spatial differences.

nyc_boro = tigris::counties(state = "NY", cb = TRUE) |>
  filter(NAME %in% c("Bronx", "Kings", "New York", "Queens", "Richmond"))

dat1_boro = dat1_boro |>
  mutate(
    county_name = case_when(
      BORO == "MANHATTAN" ~ "New York",
      BORO == "BROOKLYN" ~ "Kings",
      BORO == "BRONX" ~ "Bronx",
      BORO == "QUEENS" ~ "Queens",
      BORO == "STATEN ISLAND" ~ "Richmond"
    )
  )

nyc_boro_map = nyc_boro |>
  left_join(dat1_boro, by = c("NAME" = "county_name"))

mapview(nyc_boro_map,
        zcol = "prop_murder",
        layer.name = "Proportion of Murder by Borough",
        popup = paste0("Borough: ", nyc_boro_map$BORO,
                       "<br>Murder proportion: ",
                       round(nyc_boro_map$prop_murder, 3)))

• Comments Fatality proportions vary only slightly across boroughs, with most areas falling between 0.18 and 0.20. Staten Island appears at the higher end of the range, while Manhattan remains lower relative to other boroughs. Overall, the map highlights that spatial differences in fatality risk are modest, and do not show strong geographic clustering within the city.

Maps of Murder Proportion (All Years)

This map incorporates all years of data to illustrate the most recent geographic distribution of fatal shooting proportions.

nyc_boro_map_prop = nyc_boro |>
  left_join(dat_all_boro_prop, by = c("NAME" = "county_name"))

mapview(
  nyc_boro_map_prop,
  zcol = "prop_murder",
  layer.name = "Proportion of Murder by Borough (all years)",
  popup = paste0(
    "Borough: ", nyc_boro_map_prop$BORO, "<br>",
    "Murder proportion: ", round(nyc_boro_map_prop$prop_murder, 3), "<br>",
    "Total shootings: ", nyc_boro_map_prop$n_total
  )
)

• Comments Fatality proportions remain very similar across boroughs when all years are included, ranging only from about 0.18 to 0.20. Staten Island continues to have a slightly higher proportion, while Manhattan remains lower relative to the other boroughs. Overall, adding recent years does not introduce new spatial patterns, and the geographic variation in fatality risk remains modest.

Maps of Murder Counts　(2006–2024)

This map displays borough level case counts during the historic period and shows where fatal shootings were most concentrated.

dat1_boro = dat1 |>
  group_by(BORO) |>
  summarise(
    n_murder = sum(STATISTICAL_MURDER_FLAG),
    n_total  = n(),
    .groups = "drop"
  )


dat1_boro <- dat1_boro |>
  mutate(
    COUNTY = case_when(
      BORO == "MANHATTAN" ~ "New York",
      BORO == "BROOKLYN" ~ "Kings",
      BORO == "BRONX" ~ "Bronx",
      BORO == "QUEENS" ~ "Queens",
      BORO == "STATEN ISLAND" ~ "Richmond"
    )
  )

library(tigris)
options(tigris_progress = FALSE, tigris_use_cache = TRUE)

nyc_boro_map <- counties(state = "NY", cb = TRUE) |>
  filter(NAME %in% c("Bronx", "Kings", "New York", "Queens", "Richmond"))

nyc_boro_map <- nyc_boro_map |>
  left_join(dat1_boro, by = c("NAME" = "COUNTY"))

mapview(
  nyc_boro_map,
  zcol = "n_murder",
  layer.name = "Number of Murder Cases by Borough",
  popup = paste0(
    "Borough: ", nyc_boro_map$BORO, "<br>",
    "Number of murder cases: ", nyc_boro_map$n_murder, "<br>",
    "Total shootings: ", nyc_boro_map$n_total
  )
)

• Comments Brooklyn and the Bronx stand out as having the largest numbers of fatal shootings in the historic period. Queens and Manhattan show moderate levels, while Staten Island has very few cases. This distribution reflects substantial differences in overall shooting activity across boroughs.

Maps of Murder Counts (All Years)

This map includes all available data to present the current distribution of case counts across New York City.

nyc_boro_map_count = nyc_boro |>
  left_join(dat_all_boro_count, by = c("NAME" = "county_name"))

mapview(
  nyc_boro_map_count,
  zcol = "n_murder",
  layer.name = "Number of Murder Cases by Borough (all years)",
  popup = paste0(
    "Borough: ", nyc_boro_map_count$BORO, "<br>",
    "Number of murder cases: ", nyc_boro_map_count$n_murder, "<br>",
    "Total shootings: ", nyc_boro_map_count$n_total
  )
)

• Comments Brooklyn and the Bronx continue to have the largest numbers of fatal shootings when all years are included, reflecting consistently high incident volumes. Queen and Manhattan remain in the middle range, while Staten Island shows very few cases. These patterns suggest that the overall distribution of fatal shooting counts has remained stable over time.

Regression Analysis

This section examines individual-level predictors of fatal outcomes using a multivariable logistic regression model. Extending the descriptive results, the analysis evaluates how age, sex, and race are associated with the probability of death. These variables were selected because they represent key demographic determinants of trauma outcomes. Age groups (<25, 25–64, 65+) reflect meaningful differences in physiological reserve, while sex and race capture biological and structural factors.

Logistic Model

A logistic regression model was used with fatal outcome as the dependent variable and victim age group, sex, and race as predictors.

dat_all_stable = dat_all |>
  filter(VIC_SEX %in% c("M", "F")) |>
  filter(!VIC_AGE_GROUP %in% c("UNKNOWN", "", NA)) |>
  filter(!VIC_RACE %in% c("", NA)) |> 
  mutate(
    age_simple = case_when(
      VIC_AGE_GROUP %in% c("<18", "18-24")        ~ "young",
      VIC_AGE_GROUP %in% c("25-44", "45-64")      ~ "adult",
      VIC_AGE_GROUP == "65+"                      ~ "old"
    ),
    age_simple = factor(age_simple, levels = c("adult", "young", "old")),
    STATISTICAL_MURDER_FLAG = as.numeric(STATISTICAL_MURDER_FLAG),
    VIC_SEX = factor(VIC_SEX)
  )

dat_all_stable = dat_all_stable |>
  mutate(
    race_simple = case_when(
      VIC_RACE == "WHITE"    ~ "White",
      VIC_RACE == "BLACK"    ~ "Black",
      VIC_RACE == "HISPANIC" ~ "Hispanic",
      TRUE                   ~ "Other"
    ),
    race_simple = factor(race_simple, levels = c("White", "Black", "Hispanic", "Other"))
  )
  

fit_stable = glm(
  STATISTICAL_MURDER_FLAG ~ VIC_SEX + age_simple+ race_simple,
  data   = dat_all_stable,
  family = binomial()
)

or_table = tidy(fit_stable, exponentiate = TRUE, conf.int = TRUE) |>
  mutate(
    term = case_when(
      term == "(Intercept)"       ~ "Intercept",
      term == "VIC_SEXM"          ~ "Victim sex (male vs female)",
      term == "age_simpleyoung"   ~ "Age group young vs adult",
      term == "age_simpleold"     ~ "Age group old vs adult",
      term == "race_simpleBlack"      ~ "Race: Black vs White",
      term == "race_simpleHispanic"  ~ "Race: Hispanic vs White",
      term == "race_simpleOther"        ~ "Race: Other vs White",
      TRUE                        ~ term
    ),
    estimate = round(estimate, 2),
    conf.low = round(conf.low, 2),
    conf.high = round(conf.high, 2)
  ) |>
  select(
    Predictor = term,
    `Odds ratio` = estimate,
    `Lower 95 percent CI` = conf.low,
    `Upper 95 percent CI` = conf.high
  )

or_table |>
  kable(
    caption = "Adjusted odds ratios for fatal shooting outcomes",
    align   = c("l", "r", "r", "r")
  ) |>
  kable_styling(full_width = FALSE, position = "center")

Adjusted odds ratios for fatal shooting outcomes

Predictor	Odds ratio	Lower 95 percent CI	Upper 95 percent CI
Intercept	0.39	0.32	0.46
Victim sex (male vs female)	0.98	0.89	1.08
Age group young vs adult	0.67	0.63	0.71
Age group old vs adult	1.47	1.10	1.93
Race: Black vs White	0.72	0.61	0.85
Race: Other vs White	0.74	0.62	0.88

Interpretation & Epidemiological Insights

The model indicates that age is strongly associated with fatality: younger victims have lower odds of death (OR = 0.67), while older victims have higher odds (OR = 1.47) compared with adults. Sex is not a significant predictor, with males and females showing similar odds of fatal outcomes (OR = 0.98). Race also shows significant differences: Black victims (OR = 0.72) and those categorized as Other (OR = 0.74) have lower odds of fatality than White victims. This pattern may seem unexpected because many studies show higher firearm mortality rates among Black individuals. However, the difference in our results likely reflects the characteristics of this dataset. The analysis includes only incidents that resulted in death, and it may also contain cases such as firearm suicides. Suicides occur more frequently among White individuals, and their inclusion could raise the fatality odds observed for White victims in this model.

knitr::include_graphics("Figures/img_statue.jpg")