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.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

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.

Context

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

Content

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

Inspiration

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

Acknowledgements

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

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.

Gun ownership in the United States is the highest in the world, and constitutionally protected by the Second Amendment to the United States Constitution. Firearms are widely used in the United States for self-defence, hunting, and recreational uses, such as target shooting.

Source: https://www.kaggle.com/datasets/nidzsharma/us-mass-shootings-19822023

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13190980%2F56c639fee11c268267f9cf4ece33cf6a%2Fnewplot%20(6).png?generation=1680562776580204&alt=media" alt="">

Data columns:

• 0 case
• 1 location
• 2 date
• 3 summary
• 4 fatalities
• 5 injured
• 6 total_victims
• 7 location.1
• 8 age_of_shooter
• 9 prior_signs_mental_health_issues - Cleaned
• 10 mental_health_details - Cleaned
• 11 weapons_obtained_legally
• 12 where_obtained
• 13 weapon_type
• 14 weapon_details
• 15 race - cleaned
• 16 gender - cleaned
• 17 latitude - filled from location with Google Maps API
• 18 longitude - filled from location with Google Maps API
• 19 type
• 20 year - retrieved from date column
• 21 quarter - retrieved from date column
• 22 half - retrieved from date column
• 23 month_name - retrieved from date column
• 24 day_of_week - retrieved from date column
• 25 age_group - "Teenage", "Early Adulthood", "Middle Adulthood", "Old Age"
• 26 decade - retrieved from date
• 27 name - retrieved from splitting summary
• 28 current_age - retrieved from splitting summary
• 29 description - retrieved from splitting summary

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

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 study constructed a comprehensive, longitudinal dataset of all counties nested within U.S. States from 1970 to 2012. The study's main purpose was to facilitate research that would further understanding on firearm legislation and its impacts on violence. This comprehensive data collection effort included information on firearm legislation implemented across U.S. States over time in combination with multiple measures of firearm-related violence and injury. Moreover, to better understand the conditions under which firearm legislation is more or less effective, incorporation of county characteristics allowed for examination of whether the effectiveness of state-level firearm legislation depends upon particular characteristics of counties. The researchers conducted a secondary analysis utilizing a variety of archived external government and census sources. The Study's Dataset Include two Stata Files: CJRC_firearms_research.dta (95 Variables, 129,027 Cases) state_law_data.dta (19 Variables, 2,168 Cases)

In the United States, gun laws vary from one state to the next; whether residents need a permit or a background check to purchase a firearm, whether residents must undergo firearm training before making this purchase, and whether residents can openly carry their guns in public is dependent upon state legislation. As of January 15, 2025, ** U.S. states required background checks and/or permits for the purchase of a handgun. A further ** states had regulations on openly carrying firearms in public; however, only California, Connecticut, Florida, and Illinois had completely prohibited open carry for all firearms. In comparison, Maryland, New Jersey, and New York prohibited open carry for handguns but either did not have regulations in place or required a permit for other types of guns. A constitutional right The Second Amendment of the Constitution, which states that citizens have the right to bear arms, has made it difficult for any gun control legislation to be passed on a national level in the United States. As a result, gun control laws in the U.S. are state-based, and often differ based on political perspectives. States with strong gun laws in place, such as Massachusetts, generally experience less gun violence, however, some states with strong gun laws, such as Maryland, continue to face high rates of gun violence, which has largely been attributed to gun trafficking activity found throughout the nation. A culture of gun owners In comparison to other high-income countries with stricter gun control laws, the United States has the highest gun homicide rate at **** gun homicides per 100,000 residents. However, despite increasing evidence that easy access to firearms, whether legal or illegal, encourages higher rates of gun violence, the United States continues to foster an environment in which owning a firearm is seen as personal freedom. Almost **** of U.S. households have reported owning at least one firearm and ** percent of registered voters in the U.S. were found to believe that it was more important to protect the right of Americans to own guns, compared to ** percent who said it was more important to limit gun ownership.

This dataset contains individual-level homicide and non-fatal shooting victimizations, including homicide data from 1991 to the present, and non-fatal shooting data from 2010 to the present (2010 is the earliest available year for shooting data). 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-access dataset, but with "UNKNOWN" in the shooting column.

Each row represents a single victimization, i.e., a unique event when an individual became the victim of a homicide or non-fatal shooting. Each row does not represent a unique victim—if someone is victimized multiple times there will be multiple rows for each of those distinct events.

The dataset is refreshed daily, but excludes the most recent complete day to allow the Chicago Police Department (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.

A version of this dataset with additional crime types is available by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Violence Reduction Victims Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup.

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: 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.” Officer-involved shootings are not included.

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.

Note: In some instances, CPD'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 reliable crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: Homicide victims names are delayed by two weeks to allow time for the victim’s family to be notified of their passing.

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.

Note: This dataset includes variables referencing administrative or political boundaries that are subject to change. These include Street Outreach Organization boundary, Ward, Chicago Police Department District, Chicago Police Department Area, Chicago Police Department Beat, Illinois State Senate District, and Illinois State House of Representatives District. These variables reflect current geographic boundaries as of November 1st, 2021. In some instances, current boundaries may conflict with those that were in place at the time that a given incident occurred in prior years. For example, the Chicago Police Department districts 021 and 013 no longer exist. Any historical violent crime victimization that occurred in those districts when they were in existence are marked in this dataset as having occurred in the current districts that expanded to replace 013 and 021."

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.

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 United States is ranked first in gun possession globally and is among the countries suffering the most from firearm violence. Several aspects of the US firearm ecosystem have been detailed over the years, mostly focusing on nation- or state-level phenomena. Systematic, high-resolution studies that compare US cities are largely lacking, leaving several questions open. For example, how does firearm violence vary with the population size of a US city? Are guns more prevalent and accessible in larger cities? In search of answers to these questions, we apply urban scaling theory, which has been instrumental in understanding the present and future of urbanization for the past 15 years. We collate a dataset about firearm violence, accessibility and ownership in 929 cities, ranging from 10,000 to 20,000,000 people. We discover superlinear scaling of firearm violence (measured through the incidence of firearm homicides and armed robberies) and sublinear scaling of both firearm ownership (inferred from the percentage of suicides that are committed with firearm) and firearm accessibility (measured as the prevalence of federal firearm-selling licenses). To investigate the mechanism underlying the US firearm ecosystem, we establish a novel information-theoretic methodology that infers associations from the variance of urban features about scaling laws. We unveil influence of violence and firearm accessibility on firearm ownership, which we model through a Cobb–Douglas function. Such an influence suggests that self-protection could be a critical driver of firearm ownership in US cities, whose extent is moderated by access to firearms.

description: 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.; abstract: 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.

Analysis of ‘Police Killings US’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/azizozmen/police-killings-us on 13 February 2022.

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

"In 2015, The Washington Post began to log every fatal shooting by an on-duty police officer in the United States. In that time there have been more than 5,000 such shootings recorded by The Post. After Michael Brown, an unarmed Black man, was killed in 2014 by police in Ferguson, Mo., a Post investigation found that the FBI undercounted fatal police shootings by more than half. This is because reporting by police departments is voluntary and many departments fail to do so. The Washington Post’s data relies primarily on news accounts, social media postings, and police reports. Analysis of more than five years of data reveals that the number and circumstances of fatal shootings and the overall demographics of the victims have remained relatively constant..." SOURCE ==> Washington Post Article

For more information about this story

This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

2016 PoliceKillingUS DATASET
2017 PoliceKillingUS DATASET
2018 PoliceKillingUS DATASET
2019 PoliceKillingUS DATASET
2020 PoliceKillingUS DATASET

Features at the Dataset:

The file fatal-police-shootings-data.csv contains data about each fatal shooting in CSV format. The file can be downloaded at this URL. Each row has the following variables:

• id: a unique identifier for each victim
• name: the name of the victim
• date: the date of the fatal shooting in YYYY-MM-DD format
• manner_of_death: shot, shot and Tasered
• armed: indicates that the victim was armed with some sort of implement that a police officer believed could inflict harm
  • undetermined: it is not known whether or not the victim had a weapon
  • unknown: the victim was armed, but it is not known what the object was
  • unarmed: the victim was not armed
• age: the age of the victim
• gender: the gender of the victim. The Post identifies victims by the gender they identify with if reports indicate that it differs from their biological sex.
  • M: Male
  • F: Female
  • None: unknown
• race:
  • W: White, non-Hispanic
  • B: Black, non-Hispanic
  • A: Asian
  • N: Native American
  • H: Hispanic
  • O: Other
  • None: unknown
• city: the municipality where the fatal shooting took place. Note that in some cases this field may contain a county name if a more specific municipality is unavailable or unknown.
• state: two-letter postal code abbreviation
• signs of mental illness: News reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.
• threat_level: The threat_level column was used to flag incidents for the story by Amy Brittain in October 2015. http://www.washingtonpost.com/sf/investigative/2015/10/24/on-duty-under-fire/ As described in the story, the general criteria for the attack label was that there was the most direct and immediate threat to life. That would include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The other and undetermined categories represent all remaining cases. Other includes many incidents where officers or others faced significant threats.
• flee: News reports have indicated the victim was moving away from officers
  • Foot
  • Car
  • Not fleeing

The threat column and the fleeing column are not necessarily related. For example, there is an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer. Also, attacks represent a status immediately before fatal shots by police while fleeing could begin slightly earlier and involve a chase. - body_camera: News reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.

SOURCE

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

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024

Drug-Induced Death Rate - This indicator shows the drug-induced death rate per 100,000 population. Drug-induced deaths include all deaths for which illicit or prescription drugs are the underlying cause. In 2007, drug-induced deaths were more common than alcohol-induced or firearm-related deaths in the United States. Between 2012-2014, there were 2793 drug-induced deaths in Maryland. Link to Data Details

The Uniform Crime Reports National Time-Series Data, 1967-1980, include detailed criminal offense and clearance information submitted monthly by over 3,000 consistently reporting law enforcement agencies in the United States. These data, provided in annually pooled cross-sections, were processed at the Center for Applied Social Research, Northeastern University, Boston, Massachusetts to produce easily accessible and highly reliable time-series data on officially reported crime. Originally provided by the Federal Bureau of Investigation (FBI), these data exclude Uniform Crime Report (UCR) data from infrequently reporting law enforcement agencies. In general, only those agencies that submitted ten or more monthly reports in every year during 1967 through 1980 are included in this collection. The data include detailed breakdowns of offenses and clearances taken from disaggregated UCR Return A tapes. Of particular interest are weapon-specific robbery and assault variables, types of rape, burglary, larceny, and motor vehicle theft, and clearances by arrest (or other exceptional means) of adults and juveniles for each offense sub-type. Both monthly and annual counts of these are available. Finally, as an aid to the user, each agency is identified by its FBI "ORI Code" as well as a sequential case number produced and documented by ICPSR in the codebook's appendix. Cases also may be identified by geographic region, state, SMSA, county, population size and group, and frequency of reporting.

Analysis of ‘Index, Violent, Property, and Firearm Rates By County: Beginning 1990’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9c9619a6-a7cc-48f7-aefb-cb5c77327b9c on 27 January 2022.

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

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.

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

This study examined the relationships among trends in deadly gun violence, overall gun availability, and the availability of more lethal types of guns. Using firearms confiscated by the Dallas, Texas, police department from 1980 to 1992 as indicators of the types of guns circulating among criminal/high-risk groups, the project examined changes over time in Dallas' street gun arsenal and assessed the impact these changes had upon gun violence mortality in Dallas. The focus of the project was on the characteristics of the guns rather than their numbers. All confiscated firearms were analyzed and characterized according to basic weapon type and caliber groupings. Dates of confiscation were missing from the majority of the pre-1988 records, but by aggregating the gun data into bimonthly (Part 1) and quarterly (Part 2) time series databases, it was possible to estimate the bimonthly and quarterly periods of confiscation for most of the 1980-1992 records. Records that could not be assigned to bimonthly or quarterly periods were dropped. Confiscated firearms were grouped into basic categories based on stopping power (i.e., wounding potential), rate of fire, and ammunition capacity. The following measures were created for each bimonthly and quarterly period: (1) weapons with high stopping power (large guns), (2) semiautomatic weaponry (semis), (3) weapons combining high stopping power and a semiautomatic firing mechanism (large semis), (4) handguns with high stopping power (large handguns), (5) semiautomatic handguns (semi handguns), and (6) handguns combining high stopping power and semiautomatic firing (large semi handguns). Several violence measures were obtained from the Federal Bureau of Investigation's (FBI) Uniform Crime Reports Supplemental Homicide Reports and Return A (or Offenses Known and Clearances by Arrest) data files (see UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: 1975-1997 [ICPSR 9028]). These measures were also aggregated at bimonthly and quarterly levels. Data from the Dallas Police Department master gun property file include total handguns, total semiautomatic handguns, total large-caliber handguns, total large-caliber semiautomatic handguns, total shotguns, total semiautomatic shotguns, total rifles, total semiautomatic rifles, and total counts and total semiautomatic counts for various calibers of handguns, shotguns, and rifles. Data that were aggregated using the FBI data include total homicides, gun homicides, total robberies, gun robberies, and gun aggravated assaults. The data file also includes the year and the bimonthly or quarterly period counter.

Update Frequency: Datasets are refreshed every night to ensure the most current information is available. Even if there are no changes, the data will be updated nightly.

The data included in this dataset has been reviewed and approved by a Milwaukee Police Department supervisor and the Milwaukee Police Department’s Records Management Division. This approval process can take a few weeks from the reported date of the crime. For preliminary crime data, please visit the Milwaukee Police Department’s Crime Maps and Statistics dashboard at https://city.milwaukee.gov/police/Information-Services/Crime-Maps-and-Statistics.

Crimes that occurred within the last month in the City of Milwaukee, data from Milwaukee Police Department. Crimes are not shown at the exact location to protect the victim's identity.

Wisconsin Incident Based Report (WIBR) Group A Offenses.

The Crime Data represents incident level data defined by Wisconsin Incident Based Reporting System (WIBRS) codes. WIBRS reporting is a crime reporting standard and can not be compared to any previous UCR report. Therefore, the Crime Data may reflect:

Information not yet verified by further investigation Preliminary crime classifications that may be changed at a later date based upon further investigation Information that may include mechanical or human error Neither the City of Milwaukee nor the Milwaukee Police Department guarantee (either express or implied) the accuracy, completeness, timeliness, or correct sequencing of the Crime Data. The City of Milwaukee and the Milwaukee Police Department shall have no liability for any error or omission, or for the use of, or the results obtained from the use of the Crime Data. In addition, the City of Milwaukee and the Milwaukee Police Department caution against using the Crime Data to make decisions/comparisons regarding the safety of or the amount of crime occurring in a particular area. When reviewing the Crime Data, the site user should consider that:

The information represents only police services where a report was made and does not include other calls for police service The information does not reflect or certify "safe" or "unsafe" areas The information will sometimes reflect where the crime was reported versus where the crime occurred

The use of the Crime Data indicates the site user's unconditional acceptance of all risks associated with the use of the Crime Data.

XY fields in data is in projection Wisconsin State Plane South NAD27 (WKID 32054).