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 the United States, Black people have higher rates of gun homicide than White people across all age groups. As of 2022, gun homicide rates were highest among Black people aged between 15 and 24 years, at ***** gun homicides per 100,000 of the population. In comparison, there were only **** gun homicides per 100,000 of the White population within this age range. However, the risk for gun homicide was greatest among all adolescents and adults between the ages of 15 to 44 in that year. The impact of guns on young Americans In the last few years, firearms have become the leading cause of death for American children and teenagers aged one to 19 years old, accounting for more deaths than car crashes and diseases. School shootings also remain on the rise recently, with the U.S. recording ** times as many school shootings than other high-income nations from 2009 to 2018. Black students in particular experience a disproportionately high number of school shootings relative to their population, and K-12 teachers at schools made up mostly of students of color are more likely to report feeling afraid that they or their students would be a victim of attack or harm. The right to bear arms Despite increasingly high rates of gun-related violence, gun ownership remains a significant part of American culture, largely due to the fact that the right to bear arms is written into the U.S. Constitution. Although firearms are the most common murder weapon used in the U.S., accounting for approximately ****** homicides in 2022, almost **** of American households have at least one firearm in their possession. Consequently, it is evident that firearms remain easily accessible nationwide, even though gun laws may vary from state to state. However, the topic of gun control still causes political controversy, as the majority of Republicans agree that it is more important to protect the right of Americans to own guns, while Democrats are more inclined to believe that it is more important to limit gun ownership.

Data tables relating to offences involving weapons as recorded by police and hospital episode statistics.

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.

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 Balkan countries Albania and Bosnia have the highest murder rates with cases involving firearms in Europe. Perhaps somewhat surprisingly, Sweden came third. The Scandinavian country has seen increasing levels of gang-related violence in recent years.

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.

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 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 Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs’ departments. DCJS compiles these reports as New York’s official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Firearm counts are derived from taking the number of violent crimes which involve a firearm. Population data are provided every year by the FBI, based on US Census information. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

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

In 2023, 13,529 recorded murders in the United States were committed by firearm. This is a decrease from the previous year, when 12,244 homicides were committed with a firearm in the country. However, figures may not accurately reflect the total number of homicides, as not all law enforcement agencies in the U.S. submitted homicide data.

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.

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 Vi

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.

These dashboards provide the public with structured, aggregated data on firearm licenses, applications, incidents and the demographics of victims and offenders of crimes involving firearms.

This study examined spatial and temporal features of crime guns in Pittsburgh, Pennsylvania, in order to ascertain how gun availability affected criminal behavior among youth, whether the effects differed between young adults and juveniles, and whether that relationship changed over time. Rather than investigating the general prevalence of guns, this study focused only on those firearms used in the commission of crimes. Crime guns were defined specifically as those used in murders, assaults, robberies, weapons offenses, and drug offenses. The emphasis of the project was on the attributes of crime guns and those who possess them, the geographic sources of those guns, the distribution of crime guns over neighborhoods in a city, and the relationship between the prevalence of crime guns and the incidence of homicide. Data for Part 1, Traced Guns Data, came from the City of Pittsburgh Bureau of Police. Gun trace data provided a detailed view of crime guns recovered by police during a two-year period, from 1995 to 1997. These data identified the original source of each crime gun (first sale to a non-FFL, i.e., a person not holding a Federal Firearms License) as well as attributes of the gun and the person possessing the gun at the time of the precipitating crime, and the ZIP-code location where the gun was recovered. For Part 2, Crime Laboratory Data, data were gathered from the local county crime laboratory on guns submitted by Pittsburgh police for forensic testing. These data were from 1993 to 1998 and provided a longer time series for examining changes in crime guns over time than the data in Part 1. In Parts 3 and 4, Stolen Guns by ZIP-Code Data and Stolen Guns by Census Tract Data, data on stolen guns came from the local police. These data included the attributes of the guns and residential neighborhoods of owners. Part 3 contains data from 1987 to 1996 organized by ZIP code, whereas Part 4 contains data from 1993 to 1996 organized by census tract. Part 5, Shots Fired Data, contains the final indicator of crime gun prevalence for this study, which was 911 calls of incidents involving shots fired. These data provided vital information on both the geographic location and timing of these incidents. Shots-fired incidents not only captured varying levels of access to crime guns, but also variations in the willingness to actually use crime guns in a criminal manner. Part 6, Homicide Data, contains homicide data for the city of Pittsburgh from 1990 to 1995. These data were used to examine the relationship between varying levels of crime gun prevalence and levels of homicide, especially youth homicide, in the same city. Part 7, Pilot Mapping Application, is a pilot application illustrating the potential uses of mapping tools in police investigations of crime guns traced back to original point of sale. NTC. It consists of two ArcView 3.1 project files and 90 supporting data and mapping files. Variables in Part 1 include date of manufacture and sale of the crime gun, weapon type, gun model, caliber, firing mechanism, dealer location (ZIP code and state), recovery date and location (ZIP code and state), age and state of residence of purchaser and possessor, and possessor role. Part 2 also contains gun type and model, as well as gun make, precipitating offense, police zone submitting the gun, and year the gun was submitted to the crime lab. Variables in Parts 3 and 4 include month and year the gun was stolen, gun type, make, and caliber, and owner residence. Residence locations are limited to owner ZIP code in Part 3, and 1990 Census tract number and neighborhood name in Part 4. Part 5 contains the date, time, census tract and police zone of 911 calls relating to shots fired. Part 6 contains the date and census tract of the homicide incident, drug involvement, gang involvement, weapon, and victim and offender ages. Data in Part 7 include state, county, and ZIP code of traced guns, population figures, and counts of crime guns recovered at various geographic locations (states, counties, and ZIP codes) where the traced guns first originated in sales by an FFL to a non-FFL individual. Data for individual guns are not provided in Part 7.