This study was undertaken to obtain information on the characteristics of gun ownership, gun-carrying practices, and weapons-related incidents in the United States -- specifically, gun use and other weapons used in self-defense against humans and animals. Data were gathered using a national random-digit-dial telephone survey. The respondents were comprised of 1,905 randomly-selected adults aged 18 and older living in the 50 United States. All interviews were completed between May 28 and July 2, 1996. The sample was designed to be a representative sample of households, not of individuals, so researchers did not interview more than one adult from each household. To start the interview, six qualifying questions were asked, dealing with (1) gun ownership, (2) gun-carrying practices, (3) gun display against the respondent, (4) gun use in self-defense against animals, (5) gun use in self-defense against people, and (6) other weapons used in self-defense. A "yes" response to a qualifying question led to a series of additional questions on the same topic as the qualifying question. Part 1, Survey Data, contains the coded data obtained during the interviews, and Part 2, Open-Ended-Verbatim Responses, consists of the answers to open-ended questions provided by the respondents. Information collected for Part 1 covers how many firearms were owned by household members, types of firearms owned (handguns, revolvers, pistols, fully automatic weapons, and assault weapons), whether the respondent personally owned a gun, reasons for owning a gun, type of gun carried, whether the gun was ever kept loaded, kept concealed, used for personal protection, or used for work, and whether the respondent had a permit to carry the gun. Additional questions focused on incidents in which a gun was displayed in a hostile manner against the respondent, including the number of times such an incident took place, the location of the event in which the gun was displayed against the respondent, whether the police were contacted, whether the individual displaying the gun was known to the respondent, whether the incident was a burglary, robbery, or other planned assault, and the number of shots fired during the incident. Variables concerning gun use by the respondent in self-defense against an animal include the number of times the respondent used a gun in this manner and whether the respondent was hunting at the time of the incident. Other variables in Part 1 deal with gun use in self-defense against people, such as the location of the event, if the other individual knew the respondent had a gun, the type of gun used, any injuries to the respondent or to the individual that required medical attention or hospitalization, whether the incident was reported to the police, whether there were any arrests, whether other weapons were used in self-defense, the type of other weapon used, location of the incident in which the other weapon was used, and whether the respondent was working as a police officer or security guard or was in the military at the time of the event. Demographic variables in Part 1 include the gender, race, age, household income, and type of community (city, suburb, or rural) in which the respondent lived. Open-ended questions asked during the interview comprise the variables in Part 2. Responses include descriptions of where the respondent was when he or she displayed a gun (in self-defense or otherwise), specific reasons why the respondent displayed a gun, how the other individual reacted when the respondent displayed the gun, how the individual knew the respondent had a gun, whether the police were contacted for specific self-defense events, and if not, why not.

The share of American households owning at least one firearm has remained relatively steady since 1972, hovering between ** percent and ** percent. In 2024, about ** percent of U.S. households had at least one gun in their possession. Additional information on firearms in the United States Firearms command a higher degree of cultural significance in the United States than any other country in the world. Since the inclusion of the right to bear arms in the Second Amendment to the Constitution of the United States, firearms have held symbolic power beyond their already obvious material power. Despite many Americans being proud gun-owners, a large movement exists within the country in opposition to the freedom afforded to those in possession of these potentially deadly weapons. Those opposed to current gun regulation have sourced their anger from the large number of deaths due to firearms in the country, as well as the high frequency of gun violence apparent in comparison to other developed countries. Furthermore, the United States has fallen victim to a number of mass shootings in the last two decades, most of which have raised questions over the ease at which a person can obtain a firearm. Although this movement holds a significant position in the public political discourse of the United States, meaningful change regarding the legislation dictating the ownership of firearms has not occurred. Critics have pointed to the influence possessed by the National Rifle Association through their lobbying of public officials. The National Rifle Association also lobbies for the interests of firearm manufacturing in the United States, which has continued to rise since a fall in the early 2000s.

Texas was the state with the highest number of registered weapons in the United States in 2024, with 1,136,732 firearms. Rhode Island, on the other hand, had the least, with 4,895 registered firearms. Gun laws in the United States Gun ownership in the U.S. is protected by the 2nd Amendment of the Constitution, which allows citizens to own firearms and form a militia if necessary. Outside of the 2nd Amendment, gun laws in the U.S. vary from state to state, and gun owners are subject to the laws of the state they are currently in, not necessarily the state they live in. For example, if concealed carry is allowed in a gun owner’s state of residence but not in the state they are traveling in, the owner is subject to the law of the state they are traveling in. Civilian-owned firearms The United States is estimated to have the highest rate of civilian-owned firearms in the world, more than double that of Yemen, which has the second-highest gun ownership rate. Unfortunately, along with high gun ownership rates comes a higher number of homicides by firearm, which was about 13,529 homicides in 2023.

Analysis of ‘Do You Know Where America Stands On Guns?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/poll-quiz-gunse on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This folder contains the data behind the quiz Do You Know Where America Stands On Guns?

guns-polls.csv contains the list of polls about guns that we used in our quiz. All polls have been taken after February 14, 2018, the date of the school shooting in Parkland, Florida.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 100 samples along with End, Republican Support, technical information and other features such as: - Start - Support - and more.

How to use this dataset

• Analyze Question in relation to Url
• Study the influence of Population on Pollster
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Gun Violence Dataset in US is a tabularized data set for gun violence analysis that includes the date, location, victim and suspect information, and geographic coordinates of major 2024 shootings across the U.S.

2) Data Utilization (1) Gun Violence Dataset in US has characteristics that: • Each row contains key information about the shooting, including incident-specific ID, date of occurrence, state and city/county, number of deaths and injuries, suspects (death, injury, arrest), latitude, and longitude. • Data is designed to analyze the distribution of gun incidents and the extent of damage by month and region, and spatial analysis through geographic coordinates is also possible. (2) Gun Violence Dataset in US can be used to: • Analysis of shooting trends by region: Use data by location, magnitude of damage, and time to visualize and analyze the regional and temporal distribution and risk areas of gun violence. • Establishing public safety policies and prevention strategies: Based on victim and suspect information and incident characteristics, it can be used to establish effective gun control, prevention policies, resource allocation strategies, and more.

The dataset consists of photos depicting individuals holding guns. It specifically focuses on the segmentation of guns within these images and the detection of people holding guns. Each image in the dataset presents a different scenario, capturing individuals from various backgrounds, genders, and age groups in different poses while holding guns. The dataset is an essential resource for the development and evaluation of computer vision models and algorithms in fields related to firearms recognition, security systems, law enforcement, and safety analysis.

This dataset is about book subjects. It has 1 row and is filtered where the books is Armed America : the remarkable story of how and why guns became as American as apple pie. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Information from Bloomington Police Department regarding guns reported stolen.

Key code for Race:

A- Asian/Pacific Island, Non-Hispanic B- African American, Non-Hispanic C- Hawaiian/Other Pacific Island, Hispanic H- Hawaiian/Other Pacific Island, Non-Hispanic I- Indian/Alaskan Native, Non-Hispanic K- African American, Hispanic L- Caucasian, Hispanic N- Indian/Alaskan Native, Hispanic P- Asian/Pacific Island, Hispanic S- Asian, Non-Hispanic T- Asian, Hispanic U- Unknown W- Caucasian, Non-Hispanic

Key Code for Reading Districts:

Example: LB519

L for Law call or incident B stands for Bloomington 5 is the district or beat where incident occurred All numbers following represents a grid sector.

Disclaimer: The Bloomington Police Department takes great effort in making open data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided by many people and that cannot always be verified. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data, or for the use or interpretation of the results of any research conducted.

About this dataset

Context

The dataset tabulates the Gun Barrel City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Gun Barrel City. The dataset can be utilized to understand the population distribution of Gun Barrel City by age. For example, using this dataset, we can identify the largest age group in Gun Barrel City.

Key observations

The largest age group in Gun Barrel City, TX was for the group of age 60 to 64 years years with a population of 612 (9.55%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Gun Barrel City, TX was the 85 years and over years with a population of 126 (1.97%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Gun Barrel City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Gun Barrel City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Barrel City Population by Age. You can refer the same here

THIS DATASET WAS LAST UPDATED AT 8:10 AM EASTERN ON AUG. 23

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Firearms background checks for the USA for 2012 (Jan-Nov) and since 1999.These statistics represent the number of firearm background checks initiated through the NICS. They do not represent the number of firearms sold. NICS is used by Federal Firearms Licensees (FFLs) to instantly determine whether a prospective buyer is eligible to buy firearms or explosives. Before ringing up the sale, cashiers call in a check to the FBI or to other designated agencies to ensure that each customer does not have a criminal record or isn't otherwise ineligible to make a purchase. More than 100 million such checks have been made in the last decade, leading to more than 700,000 denials. More information on NICS - http://www.fbi.gov/about-us/cjis/nics Some really useful informations such as the rate of checks per 1000 people. All data is provided by state. Downloaded from the Guardian Datablog - http://www.guardian.co.uk/news/datablog/2012/dec/17/how-many-guns-us and then joined to USA States data http://geocommons.com/overlays/21424. Gun data originally from FBI http://www.fbi.gov/about-us/cjis/nics. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-17 and migrated to Edinburgh DataShare on 2017-02-21.

Number and percentage of homicide victims, by type of firearm used to commit the homicide (total firearms; handgun; rifle or shotgun; fully automatic firearm; sawed-off rifle or shotgun; firearm-like weapons; other firearms, type unknown), Canada, 1974 to 2018.

Context

The Gun Violence Archive is an online archive of gun violence incidents collected from over 2,000 media, law enforcement, government and commercial sources daily in an effort to provide near-real time data about the results of gun violence. GVA in an independent data collection and research group with no affiliation with any advocacy organization.

Content

This dataset includes files that separate gun violence incidents by category, including deaths and injuries of children and teens, and a collection of mass shootings.

Inspiration

• What has been the trend of gun violence in the past few years?
• What states have the highest incidents per capita per year? How has this metric changed over time?
• Are officer involved shootings on the rise? Where are they most concentrated? Do they correlate with the rates of accidental deaths and mass shootings?

Acknowledgements

This dataset is owned by the Gun Violence Archive, and can be accessed in its original form here.

Dataset Card for Dataset Name

This dataset provides a structural unlearning task for LLMs.

  Dataset Abstract

Recently, machine unlearning, which seeks to erase specific data stored in the pre-trained or fine-tuned models, has emerged as a crucial protective measure for LLMs. However, unlearning approaches for LLMs that have been considered thus far have focused on the removal of independent data points and have not taken into account that the stored facts are logically… See the full description on the dataset page: https://huggingface.co/datasets/xinchiqiu/PISTOL.

Introduction: Firearm legality and ownership have been contentious topics in American culture, due to the well-documented, yet preventable, health and safety risks. States vary in the amount of firearm ownership, as well as firearm mortality and injury rates. Objectives: The primary aim of this project is to compare two states, New Jersey and Texas, on the likelihood of firearm violence occurring to each state's citizens. The variables of gun ownership, firearm mortalities, and firearm injuries are compared and visualized to understand if living in one state is safer than living in the other. Methods: Data analysis focused on connecting and comparing the two states with variables pointing to firearm safety/danger. Line graphs compare the two states and firearm injuries and mortalities over a sixteen-year period as well as number of firearms per state. Scatterplots show a correlation, if any, between number of firearms and injuries/mortalities in the two states. Results: Texas had a consistently higher mortality rate by firearms (excluding suicides) for each year of the seventeen years. Texas also led in firearm injuries from the years 2000-2010, 2012, and 2014-2016, but not in 2011 and 2013. New Jersey consistently has a lower mortality rate (3.5 and under per 100,000) and lower gun ownership (.11 and under per household). Texas’ data has both a higher mortality rate (between 3.8 and 4.8 per 100,000) and a higher gun ownership rate (.34 to .40 per household). With a few exceptions from the years 2011 and 2013, the state data points are clustered to show the relationship between gun ownership and firearm injuries to be high/high for Texas and low/low for New Jersey. Conclusions: From the years 2000-2016 it is, on average, 20% less likely that one will be injured by a firearm and 30% less likely that one will be killed by a firearm if one were to live in New Jersey instead of Texas, causing the conclusion that it is safer to live in New Jersey than in Texas.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gun Plain township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gun Plain township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gun Plain township, the median household income stands at $126,667 for householders within the 25 to 44 years age group, followed by $99,718 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $41,492.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Plain township median household income by age. You can refer the same here

Weapons Sales in the United States increased to 13512 SIPRI TIV Million in 2024 from 11102 SIPRI TIV Million in 2023. United States Weapons Sales - values, historical data, forecasts and news - updated on August of 2025.

The global automatic weapons market, valued at $9.47 billion in 2025, is projected to experience robust growth, driven by escalating geopolitical instability, increasing defense budgets across major global powers, and the continuous modernization of armed forces worldwide. The market's Compound Annual Growth Rate (CAGR) of 7.50% from 2025 to 2033 signifies a significant expansion, fueled by demand for advanced automatic rifles, machine guns, and automatic launchers. Technological advancements, such as the integration of smart technologies and improved accuracy, are key trends shaping the industry, while factors like stringent export regulations and ethical concerns regarding the proliferation of lethal weaponry act as restraints. The market is segmented by weapon type (automatic rifles, machine guns, automatic launchers, automatic cannons, and Gatling guns) and platform (land, airborne, and naval), reflecting diverse operational needs across various military branches and applications. North America and Europe currently hold significant market shares due to high defense spending and established manufacturing bases, but the Asia-Pacific region is anticipated to demonstrate strong growth in the coming years, driven by increasing military expenditure and modernization efforts within several nations. Key players like Heckler & Koch, General Dynamics, and Rheinmetall are at the forefront of innovation, constantly striving to meet evolving military requirements and emerging market demands. The competitive landscape is characterized by intense rivalry among established players and emerging manufacturers. Companies are focusing on research and development to enhance the capabilities of automatic weapons, incorporating features such as improved ergonomics, reduced recoil, and increased accuracy. The integration of advanced materials and technologies is further driving innovation within the sector. Growth strategies, including mergers and acquisitions, strategic partnerships, and technological advancements, are being adopted by market leaders to expand their market share and maintain a competitive edge. The demand for lighter, more portable, and technologically advanced weapons is also driving innovation. Regional variations in market dynamics will continue to exist, based on specific geopolitical factors and governmental defense priorities. The projected growth trajectory indicates a significant market opportunity for manufacturers in the coming decade, albeit one subject to the complex interplay of global political and economic factors. This report provides a detailed analysis of the global automatic weapons industry, covering the period 2019-2033, with a focus on market size, growth drivers, challenges, and key players. It leverages a robust dataset, incorporating historical data (2019-2024), a base year of 2025, and forecasts extending to 2033. The report is crucial for stakeholders seeking to understand the dynamics of this complex and regulated sector. High-search-volume keywords like "automatic weapons market," "machine gun market," "military weapons market," "automatic rifle market," and "defense industry analysis" are strategically integrated to maximize online visibility. Recent developments include: March 2023: the Estonian Defense Investment Centre awarded an order to Israel's IWI for the supply of 1,000 NG7 "Negev" light machine weapon systems to the Estonian Army. The "Negevv" machine guns, which are expected to be delivered towards the end of 2023, will take over from MG3 and KSP-58 machines that are already in use. NEGEV 7.62 LMG is a NATO 7.62x51mm Light machine gun, which is used by many countries worldwide., February 2022: The US Army awarded FN America with a USD 49 million contract to supply M240L medium machine guns and titanium receivers. The M240L, which is roughly 18% lighter than the M240B, was adopted in 2010 after a joint effort by FN and the US Army. It is to reduce overall weight while maintaining the performance and durability of the machine gun.. Notable trends are: Land Segment to Witness Highest Growth During the Forecast Period.

This set of raw data contains information from Bloomington Police Department cases, specifically it identified cases where officers have fired a gun at a suspect. **Please note that this particular dataset contains no data. As of current date, the Bloomington Police Department has had no officer involved shootings to report. ** # Key code for Race: - A- Asian/Pacific Island, Non-Hispanic - B- African American, Non-Hispanic - I- Indian/Alaskan Native, Non-Hispanic - K- African American, Hispanic - L- Caucasian, Hispanic - N- Indian/Alaskan Native, Hispanic - P- Asian/Pacific Island, Hispanic - U- Unknown - W- Caucasian, Non-Hispanic # Key Code for Reading Districts: Example: LB519 - L for Law call or incident - B stands for Bloomington - 5 is the district or beat where incident occurred - All numbers following represents a grid sector. A map of the five districts can be located on Raidsonline.com, under the tab labeled Agency Layers. Disclaimer: The Bloomington Police Department takes great effort in making all sets of data as accurate as possible, but there is no avoiding the introduction of errors in this process. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data or for the use, or interpretation of the results of any research conducted.

Firearms are the leading cause of death for minors in the United States and US gun culture is often discussed as a reason behind the prevalence of school shootings. Yet, few studies systematically analyze if there is a connection between the two: Do school shooters show a distinct gun culture? This article studies gun culture in action in school shootings. It studies if school shooters show distinct meanings and practices around firearms prior to the shooting, as well as patterns in access to firearms. To do so, I analyze a full sample of US school shootings. Relying on publicly available court, police, and media data, I combine qualitative in-depth analyses with cross-case comparisons and descriptive statistics. Findings suggest most school shooters come from a social setting in which firearms are a crucial leisure activity and hold meanings of affection, friendship, and bonding. These meanings translate into practices: all school shooters had easy access to the firearms they used for the shooting. Findings contribute to research on firearms and youth violence, public health, as well as the sociology of culture.