Victims of gang-related homicides (total number of homicide victims; number of homicide victims - unknown gang-relation; number of homicide victims - known gang relation; number of gang-related homicide victims; percentage of gang-related homicide victims; rate (per 100,000 population) of gang-related homicide victims), Canada and regions, 1999 to 2024.

Over 2,300 homicides were gang-related in the United States in 2012. This was the highest amount since 2007 and a reversal of a negative trend from the previous two years.

Violent crime

Violent crime in the United States is not limited to gang violence, but the murders in this statistic compose a significant portion of the number of reported murders in those years. While there were many arrests for violent offenses in those years, few were gang members. Still, gang-related violence is a serious problem.

Ways to die

Gang-related killings are among the top circumstances for murders in the United States. Also enlightening is the number of murder victims by weapon. The vast majority of U.S. murder victims were killed by handguns or a type of firearm. The source does not give how many murders were averted by guns.

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.

In recent years, gun violence in the United States has become an alarmingly common occurrence. From 2016, there has been over ****** homicides by firearm in the U.S. each year and firearms have been found to make up the majority of murder weapons in the country by far, demonstrating increasing rates of gun violence occurring throughout the nation. As of 2025, Mississippi was the state with the highest gun violence rate per 100,000 residents in the United States, at **** percent, followed by Louisiana, at **** percent. In comparison, Massachusetts had a gun violence rate of *** percent, the lowest out of all the states. The importance of gun laws Gun laws in the United States vary from state to state, which has been found to affect the differing rates of gun violence throughout the country. Fewer people die by gun violence in states where gun safety laws have been passed, while gun violence rates remain high in states where gun usage is easily permitted and even encouraged. In addition, some states suffer from high rates of gun violence despite having strong gun safety laws due to gun trafficking, as traffickers can distribute firearms illegally past state lines. The right to bear arms Despite evidence from other countries demonstrating that strict gun control measures reduce rates of gun violence, the United States has remained reluctant to enact gun control laws. This can largely be attributed to the Second Amendment of the Constitution, which states that citizens have the right to bear arms. Consequently, gun control has become a highly partisan issue in the U.S., with ** percent of Democrats believing that it was more important to limit gun ownership while ** percent of Republicans felt that it was more important to protect the right of Americans to own guns.

The Office of Gun Violence Prevention (OGVP) shares real-time gun violence data to increase government transparency, improve the public's awareness, and support community-based gun violence prevention and reduction partners. All District crime data is available through Crime Cards. The dashboards below focus on gun violence only. The data in these dashboards is updated daily at 7:40AM with the incidents from the day before. View data covering 7-Day Look-back of Gun Violence and Year-to-date Gun Violence.All statistics presented here are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Read complete data notes at buildingblocks.dc.gov/data.

In 2019, there were six deaths by homicide per 100,000 of the population in the United States, compared to 5.9 deaths by homicide in the previous year. This is an increase from 1950, when there were 5.1 deaths by homicide per 100,000 resident population in the United States. However, within the provided time period, the death rate for homicide in the U.S. was highest in 1980, when there were 10.4 deaths by homicide per 100,000 of the population in the United States.

Homicides in the United States

The term homicide is used when a human being is killed by another human being. Criminal homicide takes several forms, for example murder; but homicide is not always a crime, it also includes affirmative defense, insanity, self-defense or the execution of convicted criminals. In the United States, youth homicide has especially been seen as a problem of urban areas, due to poverty, limited adult supervision, involvement in drug and gang activities, and school failure. Both homicide rates and suicide rates in the U.S. among people aged 20 to 24 and teenagers aged 15 to 19 have vastly increased since 2001.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

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 dataset consists of gun violence within Jefferson county that may fall within LMPDs radar, including non-fatal shootings, homicides, as well as shot-spotter data. The mapping data points where there are victims have been obfuscated to maintain privacy, while still being accurate enough to be placed in its correct boundaries, particularly around, neighborhoods, ZIP Codes, Council districts, and police divisions. The data also excludes any victim information that could be used to identify any individual. this data is used to make the public aware of what is going on in their communities. The data consists of only criminal incidents, excluding any cases that are deemed non-criminal.Field NameField DescriptionCase numberPolice report number. For ShotSpotter detections, it is the ShotSpotter ID.DateTimeDate and time in which the original incident occurred. Time is rounded down.AddressAddress rounded down to the one hundred block of where the initial incident occured. Unless it is an intersection.NeighborhoodNeighborhood in which the original incident occurred.Council DistrictCouncil district in which the original incident occurred.LatitudeLatitude coordinate used to map the incidentLongitudeLongitude coordinate used to map the incidentZIP CodeZIP Code in which the original incident occurred.Crime Typea distinction between incidents, whether it is a non-fatal shooting, homicide, or a ShotSpotter detection.CauseUsed to differentiate on the cause of death for homicide victims.SexGender of the victim of the initial incident.RaceRace/Ethnicity of the victim in a given incident.Age GroupCategorized age groups used to anonymize victim information.Division NamePolice division or department where the initial incident occurred.Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities, unless LMPD becomes involved in smaller agency incident.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Contact:Ivan Benitez, Ph.D.Gun Violence Data FellowOffice for Safe and Healthy Neighborhoodsivan.benitez@louisvilleky.gov

The Gun Violence Archive (GVA) is an online archive of gun violence incidents collected from over 7,500 law enforcement, media, government and commercial sources daily in an effort to provide near-real time data about the results of gun violence. GVA was established in 2013 an independent data collection and research group to provide comprehensive data for the national conversation regarding gun violence.

GVA catalogs both incidents of gun-related deaths and incidents where a victim was injured by shooting or by a victim who was the subject of an armed robber or home invader. Incidents of defensive gun use, homeowners who stop a home invasion, store clerks who stop a robbery, individuals who stop an assault or rape with a gun are also collected. The two exceptions to the near real-time collection are suicides by gun, which are collected quarterly and annually due to differing distribution methods by government agencies, and for armed robberies with no injuries or DGU characteristics, which are collected in aggregate with law enforcement quarterly and annual reports. GVA also records incidents of Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF) and local law enforcement involvement in recovering illegal or stolen weapons; incidents where guns were reported stolen from homes, vehicles, and businesses; incidents where Airsoft or BB guns are used as weapons (but not where they are used in general vandalism or delinquency); and TSA data of guns illegally taken through airport security points. Incident data are categorized by number of deaths, number of injuries, number of children, number of teens, mass shootings, officers shot, suspect shot by officer, home invasion, defensive use, and unintentional shooting.

Gun Violence Data Portals Market Outlook

According to our latest research, the Global Gun Violence Data Portals market size was valued at $420 million in 2024 and is projected to reach $1.2 billion by 2033, expanding at a robust CAGR of 12.5% during the forecast period of 2025–2033. The primary driver for this impressive growth trajectory is the increasing demand for real-time, transparent, and actionable data to inform policy decisions, law enforcement strategies, and public health interventions in response to escalating gun violence incidents worldwide. As governments, non-profit organizations, and academic institutions intensify their focus on data-driven solutions to address the complex challenge of gun violence, investments in advanced data portals have surged, underpinning the expansion of this market on a global scale.

Regional Outlook

North America currently dominates the Gun Violence Data Portals market, accounting for the largest share of global revenue, estimated at over 45% in 2024. This leadership is attributed to the region's mature technology infrastructure, high-profile gun violence incidents, and the presence of established data analytics and software providers. The United States, in particular, has witnessed significant policy-driven investments and public-private partnerships aimed at enhancing data transparency and accessibility for law enforcement, academic researchers, and advocacy groups. The region’s proactive stance on leveraging digital solutions for crime prevention and public health, coupled with strong regulatory mandates around data reporting and sharing, has cemented its position as the epicenter of innovation and adoption in this space.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR exceeding 15% during the forecast period. This accelerated growth is fueled by rising urbanization, increasing concerns over public safety, and the rapid digitization of government and law enforcement operations. Countries such as India, Japan, and Australia are investing heavily in cloud-based analytics platforms and collaborative data-sharing frameworks to address localized gun-related challenges and support evidence-based policymaking. The influx of foreign direct investment, coupled with government initiatives to modernize surveillance and crime reporting systems, is expected to further propel the adoption of gun violence data portals across Asia Pacific.

Meanwhile, emerging economies in Latin America and the Middle East & Africa present unique opportunities and challenges for the Gun Violence Data Portals market. While these regions are grappling with high rates of gun-related violence, the adoption of advanced data portals is often hindered by infrastructural limitations, fragmented data sources, and varying levels of digital literacy among end-users. However, targeted international funding, capacity-building programs, and regional collaborations are gradually overcoming these barriers, paving the way for localized solutions tailored to specific policy and enforcement needs. As these markets mature, the potential for scalable, cloud-based data portal solutions is expected to rise significantly, contributing to the overall global market growth.

Report Scope

Reported gang-related violent crimes in Los Angeles, California, from 1/1/14 to 12/31/17 are modeled using spatial–temporal marked Hawkes point processes with covariates. We propose an algorithm to estimate the spatial-temporally varying background rate nonparametrically as a function of demographic covariates. Kernel smoothing and generalized additive models are used in an attempt to model the background rate as closely as possible in an effort to differentiate inhomogeneity in the background rate from causal clustering or triggering of events. The models are fit to data from 2014 to 2016 and evaluated using data from 2017, based on log-likelihood and superthinned residuals. The impact of nonrandomized violence interruption performed by The City of Los Angeles Mayor’s Office of Gang Reduction Youth Development (GRYD) Incident Response (IR) Program is assessed by comparing the triggering associated with GRYD IR Program events to the triggering associated with sub-sampled non-GRYD events selected to have a similar spatial–temporal distribution. The results suggest that GRYD IR Program violence interruption yields a reduction of approximately 18.3% in the retaliation rate in locations more than 130 m from the original reported crimes, and a reduction of 14.2% in retaliations within 130 m.

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.

In 2023, a total of 71 people were killed due to arson in the United States. This was significantly lower than the 312 people who were killed in robberies in the country in that same year.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

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.

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.

This data collection examines gang and non-gang homicides as well as other types of offenses in small California jurisdictions. Data are provided on violent gang offenses and offenders as well as on a companion sample of non-gang offenses and offenders. Two separate data files are supplied, one for participants and one for incidents. The participant data include age, gender, race, and role of participants. The incident data include information from the "violent incident data collection form" (setting, auto involvement, and amount of property loss), and the "group indicators coding form" (argot, tattoos, clothing, and slang terminology).

Description:

This comprehensive dataset offers an in-depth look at gun-related deaths in the United States from 2012 to 2014, as reported by the Centers for Disease Control and Prevention (CDC). It provides a rich source of information for public health research, policy development, and sociological studies, offering a nuanced understanding of the dynamics and demographics of gun-related fatalities.

Features:

• Year: The year when the death occurred, providing a temporal context.
• Month: The month of the incident, adding more granularity to the timeline.
• Intent: Categorizes the death by intent, such as suicide or homicide, essential for understanding the circumstances.
• Police: Indicates whether a police officer was involved in the death.
• Sex: The gender of the deceased, crucial for demographic analysis.
• Age: The age of the deceased, providing insights into age-related trends.
• Race: The race of the deceased, essential for understanding racial disparities.
• Place: The location of the incident, such as home or street, which can influence the context of the death.
• Education: Educational background of the deceased, offering a socio-economic dimension.

Use Case:

This dataset is highly valuable for tasks such as public health research, policy formulation in gun control, and sociological studies. It can be employed to analyze trends and patterns in gun-related deaths, assist in crafting informed laws and public safety measures, and provide a foundation for educational and awareness initiatives about gun violence and its impact on different demographic groups.

Note: - Entries are organized chronologically, capturing each recorded incident in detail. - The dataset is especially significant for examining year-on-year trends and demographic variances in gun-related fatalities, serving as a critical resource for comprehensive analysis and research.

Handguns are by far the most common murder weapon used in the United States, accounting for 7,159 homicides in 2023. This is followed by firearms of an unstated type, with 5,295 cases in that year. Why do murders happen in the U.S.? While most of the time the circumstances of murders in the U.S. remain unknown, homicides due to narcotics come in as the second most common circumstance – making them more common than, for example, gang killings. Despite these gruesome facts, the violent crime rate has fallen significantly since 1990, and the United States is much safer than it was in the 1980s and 1990s. Knife crime vs disease: Leading causes of death The death rate in the U.S. had hovered around the same level since 1990 until there was a large increase due to the COVID-19 pandemic in recent years. Heart disease, cancer, and accidents were the three leading causes of death in the country in 2022. The rate of death from heart disease is significantly higher than the homicide rate in the United States, at 167.2 deaths per 100,000 population compared to a 5.7 homicides per 100,000. Given just 1,562 murders were caused by knife crime, it is fair to say that heart disease is a far bigger killer in the U.S.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).