THIS DATASET WAS LAST UPDATED AT 2:11 PM EASTERN ON SEPT. 3

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.

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.

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.

BackgroundPrior research suggests that United States governmental sources documenting the number of law-enforcement-related deaths (i.e., fatalities due to injuries inflicted by law enforcement officers) undercount these incidents. The National Vital Statistics System (NVSS), administered by the federal government and based on state death certificate data, identifies such deaths by assigning them diagnostic codes corresponding to “legal intervention” in accordance with the International Classification of Diseases–10th Revision (ICD-10). Newer, nongovernmental databases track law-enforcement-related deaths by compiling news media reports and provide an opportunity to assess the magnitude and determinants of suspected NVSS underreporting. Our a priori hypotheses were that underreporting by the NVSS would exceed that by the news media sources, and that underreporting rates would be higher for decedents of color versus white, decedents in lower versus higher income counties, decedents killed by non-firearm (e.g., Taser) versus firearm mechanisms, and deaths recorded by a medical examiner versus coroner.Methods and findingsWe created a new US-wide dataset by matching cases reported in a nongovernmental, news-media-based dataset produced by the newspaper The Guardian, The Counted, to identifiable NVSS mortality records for 2015. We conducted 2 main analyses for this cross-sectional study: (1) an estimate of the total number of deaths and the proportion unreported by each source using capture–recapture analysis and (2) an assessment of correlates of underreporting of law-enforcement-related deaths (demographic characteristics of the decedent, mechanism of death, death investigator type [medical examiner versus coroner], county median income, and county urbanicity) in the NVSS using multilevel logistic regression. We estimated that the total number of law-enforcement-related deaths in 2015 was 1,166 (95% CI: 1,153, 1,184). There were 599 deaths reported in The Counted only, 36 reported in the NVSS only, 487 reported in both lists, and an estimated 44 (95% CI: 31, 62) not reported in either source. The NVSS documented 44.9% (95% CI: 44.2%, 45.4%) of the total number of deaths, and The Counted documented 93.1% (95% CI: 91.7%, 94.2%). In a multivariable mixed-effects logistic model that controlled for all individual- and county-level covariates, decedents injured by non-firearm mechanisms had higher odds of underreporting in the NVSS than those injured by firearms (odds ratio [OR]: 68.2; 95% CI: 15.7, 297.5; p < 0.01), and underreporting was also more likely outside of the highest-income-quintile counties (OR for the lowest versus highest income quintile: 10.1; 95% CI: 2.4, 42.8; p < 0.01). There was no statistically significant difference in the odds of underreporting in the NVSS for deaths certified by coroners compared to medical examiners, and the odds of underreporting did not vary by race/ethnicity. One limitation of our analyses is that we were unable to examine the characteristics of cases that were unreported in The Counted.ConclusionsThe media-based source, The Counted, reported a considerably higher proportion of law-enforcement-related deaths than the NVSS, which failed to report a majority of these incidents. For the NVSS, rates of underreporting were higher in lower income counties and for decedents killed by non-firearm mechanisms. There was no evidence suggesting that underreporting varied by death investigator type (medical examiner versus coroner) or race/ethnicity.

Mortality rate from firearms includes homicides, suicides, accidental deaths, deaths by law enforcement, and deaths for which intent was undetermined. Mortality rate is based on the location of residence and has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify firearm deaths are W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, and U01.4. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Violence is a public health crisis in the US, with gun violence being a major driver. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults (ages 15-34 years), males, and Black individuals most disproportionately impacted. Firearm-related suicides disproportionately impact older, White men. Comprehensive prevention strategies should work to address underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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.

List of every shooting incident that occurred in NYC during the current calendar year.

This is a breakdown of every shooting incident that occurred in NYC during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a shooting incident in NYC and includes information about the event, the location and time of occurrence. In addition, information related to suspect and victim demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.

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.

BackgroundGun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author’s knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings.Methods and findingsThis study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health—social mobility, social capital, income inequality, racial and economic segregation, and social spending—with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1–standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73–0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73–0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66–0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67–0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%–27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity.ConclusionsThis study finds that the rich–poor gap, level of citizens’ trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans.

Number of homicide victims, by method used to commit the homicide (total methods used; shooting; stabbing; beating; strangulation; fire (burns or suffocation); other methods used; methods used unknown), Canada, 1974 to 2024.

The State Firearm Database catalogs the presence or absence of 134 firearm safety laws in 14 categories covering the 26-year period from 1991 to 2019. The classification system categorizes state firearm provisions using a methodology that both captures differences and maintains a level of comparability between states. Because of this, the database is not the most detailed nor the most comprehensive record of all state firearm policies. Other resources may provide users with a deeper understanding of individual provisions, while this database serves as an efficient way to compare the broad scope of state firearm laws across the country. These provisions covered 14 aspects of state policies, including regulation of the process by which firearm transfers take place, ammunition, firearm possession, firearm storage, firearm trafficking, and liability of firearm manufacturers. In addition, descriptions of the criteria used to code each provision have been provided so that there is transparency in how various law exemptions, exceptions, and other nuances were addressed.

The study was a comprehensive analysis of felonious killings of officers. The purposes of the study were (1) to analyze the nature and circumstances of incidents of felonious police killings and (2) to analyze trends in the numbers and rates of killings across different types of agencies and to explain these differences. For Part 1, Incident-Level Data, an incident-level database was created to capture all incidents involving the death of a police officer from 1983 through 1992. Data on officers and incidents were collected from the Law Enforcement Officers Killed and Assaulted (LEOKA) data collection as coded by the Uniform Crime Reporting (UCR) program. In addition to the UCR data, the Police Foundation also coded information from the LEOKA narratives that are not part of the computerized LEOKA database from the FBI. For Part 2, Agency-Level Data, the researchers created an agency-level database to research systematic differences among rates at which law enforcement officers had been feloniously killed from 1977 through 1992. The investigators focused on the 56 largest law enforcement agencies because of the availability of data for explanatory variables. Variables in Part 1 include year of killing, involvement of other officers, if the officer was killed with his/her own weapon, circumstances of the killing, location of fatal wounds, distance between officer and offender, if the victim was wearing body armor, if different officers were killed in the same incident, if the officer was in uniform, actions of the killer and of the officer at entry and final stage, if the killer was visible at first, if the officer thought the killer was a felon suspect, if the officer was shot at entry, and circumstances at anticipation, entry, and final stages. Demographic variables for Part 1 include victim's sex, age, race, type of assignment, rank, years of experience, agency, population group, and if the officer was working a security job. Part 2 contains variables describing the general municipal environment, such as whether the agency is located in the South, level of poverty according to a poverty index, population density, percent of population that was Hispanic or Black, and population aged 15-34 years old. Variables capturing the crime environment include the violent crime rate, property crime rate, and a gun-related crime index. Lastly, variables on the environment of the police agencies include violent and property crime arrests per 1,000 sworn officers, percentage of officers injured in assaults, and number of sworn officers.

This indicator is based on location of residence. Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify homicides are X85-Y09, Y87.1, and U01-U02. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Violence is a public health crisis in the US, with gun violence being a major driver. Almost three quarters of homicides involve firearms. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults ages 15-34 years, males, and Black individuals most disproportionately impacted. Comprehensive prevention strategies should work to address the underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This data collection consists of a spreadsheet with details extracted from 6000 coroners' inquests into accidental deaths in sixteenth-century England held at the National Archives. Tudor England was a dangerous place. There were plagues and wars, perilous childbirths and shocking infant mortality. But what risks did people face as they went about their everyday lives? Thousands of coroner's inquest reports on accidental deaths preserved at The National Archives allow us to investigate. These reports cover almost the whole of England, town and country, young and old, men and women, rich and poor. They tell us about working practices in farming, industry and housework and about leisure activities such as football, swimming, bell-ringing and riverside flower-picking, even the risks of getting too close to performing bears. They show contrasts between men's and women's lives, between different agricultural regions, between different times of day and seasons of the year. They show changes across the century, such as the replacement of archery by guns. Reports submitted by sixteenth-century coroners were photographed and the details they contained about the incidents in which individuals died were abstracted into a spreadsheet.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

These data were collected using the National Electronic Injury Surveillance System (NEISS), the primary data system of the United States Consumer Product Safety Commission (CPSC). CPSC began operating NEISS in 1972 to monitor product-related injuries treated in United States hospital emergency departments (EDs). In June 1992, the National Center for Injury Prevention and Control (NCIPC), within the Centers for Disease Control and Prevention, established an interagency agreement with CPSC to begin collecting data on nonfatal firearm-related injuries in order to monitor the incidents and the characteristics of persons with nonfatal firearm-related injuries treated in United States hospital EDs over time. This dataset represents all nonfatal firearm-related injuries (i.e., injuries associated with powder-charged guns) and all nonfatal BB and pellet gun-related injuries reported through NEISS from 1993 through 2018. The cases consist of initial ED visits for treatment of the injuries. Cases were reported even if the patients subsequently died. Secondary visits and transfers from other hospitals were excluded. Information is available on injury diagnosis, firearm type, use of drugs or alcohol, criminal incident, and locale of the incident. Demographic information includes age, sex, and race of the injured person.