THIS DATASET WAS LAST UPDATED AT 2:11 PM EASTERN ON JUNE 25

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.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

This study investigates to what extent the gender of the enterprise owner impacts the productive use of energy. Specifically, it explores how women and men differ in their productive use within micro, small, and medium-sized enterprises (MSME).

Data collection methodology: - Period: January 2023 to January 2024. - Sampling Approach: Deliberate and convenience sampling. - Data Collection Tool: Structured questionnaire distributed via a web-based platform.

Description of data: The dataset offers a gender-focused analysis of productive energy use, assessing 65 women-owned food and textile businesses across seven African countries: Egypt, Ghana, Kenya, Malawi, Nigeria, Tanzania, and Tunisia. These data complement the research article by providing in-depth insights into energy use patterns and business profiles.

Structure of the dataset: - Sheet 1: This sheet contains the cleaned initial responses from participants, with basic assumptions added for calculation clarity and transparency. - Sheet 2: This sheet contains analysed data and calculations as presented in the accompanying research paper. It covers business profiles, legal statuses of women-owned enterprises, energy use patterns (demand levels, types of energy carriers, access type), and energy expenditure.

Notable findings: 1. Energy carriers: Female-owned businesses typically rely on one or two energy carriers, whereas female-male co-owned enterprises use multiple energy carriers. 2. Fuel use: A variance in fuel use among different ownership structures is observed, with diesel, biomass, and liquefied petroleum gas being notable choices. 3. Awareness and demand levels: Increasing ownership diversity correlates with greater awareness of energy metrics and higher monthly demand for electric and mechanical power. A similar trend - with some variance - is observed for thermal energy. 4. Energy expenditure: Enterprises with diverse ownership structures tend to have lower energy expenditure per kilogram of production output (USD/kg/month). Some sole female-owned enterprises have energy expenditures exceeding 100 USD/kg/month, female-female partnerships may reach 100 USD/kg/month while female-male co-owned enterprises stay below 10 USD/kg/month.

Conclusions and recommendations: This research highlights the importance of understanding gendered productive energy practices and the need for gender mainstreaming in energy access and use interventions. It underscores the necessity for renewable energy solutions and capacity-building programs to address the efficiency and accessibility challenges women entrepreneurs face. Additionally, it recommends further research to enhance the efficiency and sustainability of energy use in women-owned enterprises.

Use of data: Researchers, policymakers, and practitioners can use this data to develop targeted energy access interventions for women-owned MSME, and design renewable energy solutions tailored to the specific needs of female entrepreneurs.