How Many Guns Were Within 1,000 Feet of Schools in Baltimore in 2018?

Introduction

How close was gun violence to schools in Baltimore in 2018?

That was the question behind this analysis. The project grew out of work I did during my doctoral research. It did not make it into the final dissertation, mostly because dissertations, like closets and email inboxes, have limits. But the question stayed with me.

Under the Gun-Free School Zones Act of 1990, certain firearm possession within 1,000 feet of a school is prohibited under specific federal conditions, particularly when interstate commerce is involved and the person is unlicensed. Legal details aside, the broader public health question is easier to understand: what does it mean when firearm-related crimes happen within walking distance of children and schools?

Using open data from Baltimore City, I looked at firearm-related crimes in 2018 and mapped how many occurred within 1,000 feet of school locations across the city.

The Data

Source

The data came from the Baltimore City Open Data Portal, which provides public access to crime and geographic datasets.

Summary

A total of 5,343 firearm-related incidents with usable location information were reported in 2018.

These records included latitude and longitude, which made it possible to map the incidents and measure how close they were to schools.

Methods

To answer the question, I used spatial analysis in R. The general workflow was straightforward:

1. import firearm crime data
2. remove records without valid coordinates
3. load school and community area shapefiles
4. convert the crime data into spatial points
5. align all layers to the same coordinate reference system
6. create 1,000-foot buffers around schools
7. identify firearm crimes that fell inside those buffers
8. count how many incidents were associated with each school area

Libraries Used

library(sf)          # Simple Features for spatial data
library(dplyr)       # Data manipulation
library(tmap)        # Thematic maps
library(readr)       # Reading data files

Data Import and Cleaning

# Import the data
crimes <- read_csv("data/gun_crimes_2018.csv")

# Filter to retain rows with valid latitude and longitude
crimes <- crimes %>%
  filter(!is.na(Latitude) & !is.na(Longitude))

# Load Community Statistical Areas (CSAs) and school shapefiles
csas <- st_read("data/community_statistical_areas.shp")
schools <- st_read("data/schools.shp")

# Convert crimes data to spatial points
crimes_sf <- st_as_sf(crimes, coords = c("Longitude", "Latitude"), crs = 4326)

Coordinate Reference System Alignment

Before doing any spatial operations, all layers needed to use the same coordinate reference system.

# Ensure all spatial data layers use the same CRS
csas <- st_transform(csas, crs = st_crs(crimes_sf))
schools <- st_transform(schools, crs = st_crs(crimes_sf))

Mapping

I created several maps to understand the geographic pattern of the data before moving into the buffer analysis.

Map of Firearm Crimes

tmap_mode("plot")

map_crimes <- tm_shape(csas) +
  tm_borders() +
  tm_shape(crimes_sf) +
  tm_dots("CrimeType", palette = "Set1", size = 0.1, title = "Crime Type") +
  tm_layout(
    title = "Firearm Crimes in Baltimore, 2018",
    legend.position = c("left", "bottom")
  )

tmap_save(map_crimes, "output/firearm_crimes_map.png", dpi = 300)

Map of Schools

map_schools <- tm_shape(csas) +
  tm_borders() +
  tm_shape(schools) +
  tm_dots(col = "red", size = 0.2, title = "Schools") +
  tm_layout(
    title = "School Locations in Baltimore, 2018",
    legend.position = c("left", "bottom")
  )

tmap_save(map_schools, "output/schools_map.png", dpi = 300)

Combined Map of Crimes and Schools

map_combined <- tm_shape(csas) +
  tm_borders() +
  tm_shape(schools) +
  tm_dots(col = "red", size = 0.2, title = "Schools") +
  tm_shape(crimes_sf) +
  tm_dots("CrimeType", palette = "Set1", size = 0.1, title = "Crime Type") +
  tm_layout(
    title = "Firearm Crimes and Schools in Baltimore, 2018",
    legend.position = c("left", "bottom")
  )

tmap_save(map_combined, "output/combined_map.png", dpi = 300)

These maps made the core problem visually obvious. Firearm-related crimes were not isolated in some distant corner of the city. Many were occurring in close proximity to places where children learn, gather, and move through daily life.

Buffer Analysis

The next step was to create a 1,000-foot zone around each school and identify which firearm-related crimes occurred inside those zones.

Creating Buffers Around Schools

Because the legal and policy standard often discussed is 1,000 feet, I used that distance for the analysis. In metric terms, that is about 304.8 meters.

# Create 1,000-foot buffers around schools
buffers <- st_buffer(schools, dist = 304.8)

# Plot the buffers
map_buffers <- tm_shape(csas) +
  tm_borders() +
  tm_shape(buffers) +
  tm_borders(col = "blue") +
  tm_shape(schools) +
  tm_dots(col = "red", size = 0.2, title = "Schools") +
  tm_layout(
    title = "1,000-foot Buffers Around Schools in Baltimore, 2018",
    legend.position = c("left", "bottom")
  )

tmap_save(map_buffers, "output/buffers_map.png", dpi = 300)

Joining Crimes to School Buffers

# Identify crimes within the buffers
crimes_in_buffers <- st_join(crimes_sf, buffers, left = FALSE)

# Count crimes per school
crime_counts <- crimes_in_buffers %>%
  group_by(SchoolName) %>%
  summarise(CrimeCount = n()) %>%
  arrange(desc(CrimeCount))

# Save results
write_csv(crime_counts, "output/crime_counts_by_school.csv")

Findings

The most striking result was this: William Paca Elementary had the highest number of firearm-related crimes within 1,000 feet, with 81 incidents.

That number does not mean 81 incidents happened on school grounds. It means that the surrounding environment, within a distance that is easily walkable, experienced a large number of firearm-related crimes over the course of a single year.

From a public health perspective, that matters. Exposure to violence is not only about direct victimization. It also shapes stress, fear, neighborhood conditions, school experience, and the broader sense of safety that children and families carry with them every day.

Why This Matters

This project sits at the intersection of violence prevention, neighborhood context, and spatial epidemiology.

Schools do not exist apart from their surroundings. They are embedded in communities, and the conditions around them affect students long before anyone starts talking about academic performance or school climate. Mapping firearm-related crimes near schools helps make those surrounding conditions visible.

It also raises useful questions for prevention. Are these patterns associated with neighborhood disadvantage? Are certain schools more exposed because of broader community conditions? Are incidents clustered at particular times of day? Those are all questions that spatial analysis can help explore.

Further Analysis

This project could be extended in several useful directions.

1. Socioeconomic analysis
   Neighborhood-level indicators could be incorporated into regression models to explore which structural factors are most strongly associated with firearm crimes near schools.

2. Time-based analysis
   Crimes could be compared by time of day, school hours versus after hours, or weekday versus weekend patterns.

3. Alternative buffer distances
   Additional analyses could examine whether the patterns hold at smaller or larger distances, such as 500 feet or a quarter mile.

4. School-level context
   Future work could examine whether school type, enrollment, or neighborhood characteristics are associated with differences in nearby firearm crime counts.

References

• Baltimore City Open Data Portal
• CRAN Spatial Packages

← Back Home