Sadly, the trend of fatal police shootings in the United States seems to only be increasing, with a total 1,173 civilians having been shot, 248 of whom were Black, as of December 2024. In 2023, there were 1,164 fatal police shootings. Additionally, the rate of fatal police shootings among Black Americans was much higher than that for any other ethnicity, standing at 6.1 fatal shootings per million of the population per year between 2015 and 2024. Police brutality in the U.S. In recent years, particularly since the fatal shooting of Michael Brown in Ferguson, Missouri in 2014, police brutality has become a hot button issue in the United States. The number of homicides committed by police in the United States is often compared to those in countries such as England, where the number is significantly lower. Black Lives Matter The Black Lives Matter Movement, formed in 2013, has been a vocal part of the movement against police brutality in the U.S. by organizing “die-ins”, marches, and demonstrations in response to the killings of black men and women by police. While Black Lives Matter has become a controversial movement within the U.S., it has brought more attention to the number and frequency of police shootings of civilians.

The rate of fatal police shootings in the United States shows large differences based on ethnicity. Among Black Americans, the rate of fatal police shootings between 2015 and December 2024 stood at 6.1 per million of the population per year, while for white Americans, the rate stood at 2.4 fatal police shootings per million of the population per year. Police brutality in the United States Police brutality is a major issue in the United States, but recently saw a spike in online awareness and protests following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Just a few months before, Breonna Taylor was fatally shot in her apartment when Louisville police officers forced entry into her apartment. Despite the repeated fatal police shootings across the country, police accountability has not been adequate according to many Americans. A majority of Black Americans thought that police officers were not held accountable for their misconduct, while less than half of White Americans thought the same. Political opinions Not only are there differences in opinion between ethnicities on police brutality, but there are also major differences between political parties. A majority of Democrats in the United States thought that police officers were not held accountable for their misconduct, while a majority of Republicans that they were held accountable. Despite opposing views on police accountability, both Democrats and Republicans agree that police should be required to be trained in nonviolent alternatives to deadly force.

As of November 17, 277 Black people were killed by the police in the United States in 2024. This compares to 201 Hispanic people and 445 white people. The rate of police shootings of Black Americans is much higher than any other ethnicity, at 6.2 per million people. This rate stands at 2.8 per million for Hispanic people and 2.4 per million for white people.

The 2014 killing of Michael Brown in Ferguson, Missouri, began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide.

Since Jan. 1, 2015, The Washington Post has been compiling a database of every fatal shooting in the US by a police officer in the line of duty. It's difficult to find reliable data from before this period, as police killings haven't been comprehensively documented, and the statistics on police brutality are much less available. As a result, a vast number of cases go unreported.

The Washington Post is tracking more than a dozen details about each killing - including the race, age and gender of the deceased, whether the person was armed, and whether the victim was experiencing a mental-health crisis. They have gathered this information from law enforcement websites, local new reports, social media, and by monitoring independent databases such as "Killed by police" and "Fatal Encounters". The Post has also conducted additional reporting in many cases.

There are four additional datasets. These are US census data on poverty rate, high school graduation rate, median household income, and racial demographics.

Source of census data: https://factfinder.census.gov/faces/nav/jsf/pages/community_facts.xhtml

Between 2015 and 2022, 38 percent of police killings of Black people in the United States were committed while the victim was fleeing, the highest percentage of any ethnicity. Over the same time period, 22 percent of police killings of Asian Americans occurred while the victim was fleeing - the lowest percentage of any ethnicity.

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.

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 ---

Values are: posterior mean (posterior standard deviation) of the regression coefficients. The symbol log referes to the natural logarithm. Pop refers to absolute population size. Pct. B. refers to the percentage of the county population that is black. Md. In. refers to median income. Gini refers to the Gini index of inequality. GRP refers to the Google search racism proxy. W. Ast and B. Ast refer to the white- and black-specific arrest rates for assualt, respectively. W. Wps and B. Wps refer to the white- and black-specific arrest rates for weapons violations, respectively. Posterior probabilty that a postive regression coeffcient is less than zero (or a negative one greater than zero) is coded as: * indicates a probability between 0.10 and 0.05, ** indicates a probability between 0.05 and 0.01, and *** indicates a probability of 0.01 or less.

Following racially charged events, individuals often diverge in perceptions of what happened and how justice should be served. Examining data gathered shortly after the 2014 shooting of Michael Brown in Ferguson, Missouri alongside reactions to a novel officer-involved shooting, we unpack the processes by which racial divisions emerge. Even in a controlled information environment, White Americans preferred information that supported claims of a justified shooting. Conversely, Black Americans preferred information that implied the officer behaved inappropriately. These differences stemmed from two distinct processes: we find some evidence for a form of race-based motivated reasoning and strong evidence for belief updating based on racially distinct priors. Differences in summary judgments were larger when individuals identified strongly with their racial group or when expectations about the typical behaviors of Black Americans and police diverged. The findings elucidate processes whereby individuals in different social groups come to accept differing narratives about contentious events. (2020-09-04)

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

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

Analysis of ‘US Police Shootings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ahsen1330/us-police-shootings on 28 January 2022.

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

Context

In the recent killings, a hot topic came into being, "Racism". So, I chose to gather some data to take out some insights and analyze the story around racism in America. I downloaded the raw data from kaggle and prepared it for visualization while correcting values, handling missing content, normalization and categorization

Content

It contains basic data about people like their name, age, gender and race. Along with it, is the shooting/killing information, like date of event, where it happened? how they were shot? did they attack? Were they holding weapons? Did they show any mental illness? Was the policeman wearing a camera/was the incident recorded? Did the suspect flee? Apart from that, a category column holds type of weapon used by the suspect

Acknowledgements

Kagglers

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

Data and R code to replicate the findings reported in the publication: Lethal Force in Black and White: Assessing Racial Disparities in the Circumstances of Police Killings. See README file for details.

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

This set of raw data contains information from Bloomington Police Department cases, specifically it identified cases where officers have fired a gun at a suspect.

**Please note that this particular dataset contains no data. As of current date, the Bloomington Police Department has had no officer involved shootings to report. **

Key code for Race:

• A- Asian/Pacific Island, Non-Hispanic
• B- African American, Non-Hispanic
• I- Indian/Alaskan Native, Non-Hispanic
• K- African American, Hispanic
• L- Caucasian, Hispanic
• N- Indian/Alaskan Native, Hispanic
• P- Asian/Pacific Island, Hispanic
• U- Unknown
• W- Caucasian, Non-Hispanic

Key Code for Reading Districts:

Example: LB519

• ‘L’ for Law call or incident
• ‘B’ stands for Bloomington
• 5 is the district or beat where incident occurred
• All numbers following represents a grid sector.

A map of the five districts can be located on Raidsonline.com, under the tab labeled ‘Agency Layers’.

Disclaimer: The Bloomington Police Department takes great effort in making all sets of data as accurate as possible, but there is no avoiding the introduction of errors in this process. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data or for the use, or interpretation of the results of any research conducted.

This page includes replication code and data for Bor et al "Police killings and their spillover effects on the mental health of black Americans: a population-based, quasi-experimental study" published in the Lancet. The data and code will be provided to coincide with the publication of the print version of the paper.

We present a theoretical model predicting that racially biased policing produces 1) more use of potentially lethal force by firearms against Black civilians than against White civilians and 2) lower fatality rates for Black civilians than White civilians. We empirically evaluate this second prediction with original officer-involved shooting data from 2010 to 2017 for eight local police jurisdictions, finding that Black fatality rates are significantly lower than White fatality rates and that this significance would survive an omitted covariate three times as strong as any of our observed covariates. Furthermore, using outcome test methodology and a comparability assumption, we estimate that at least 30% of Black civilians shot by the police would not have been shot had they been White. An omitted covariate would need to be at least three times as strong as any of our observed covariates to eliminate this finding. Finally, any omitted covariate would have to affect Black fatality rates substantially more than Hispanic fatality rates in order to be consistent with the data.

Analysis of ‘🚓 Fatal Police Shootings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/fatal-police-shootingse on 13 February 2022.

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

About this dataset

The Washington Post has tracked fatal police shootings in the US since 2015, using news and police reports as well as social media and databases like Killed by Police and Fatal Encounters.

The collected data include the race, gender, and age of the deceased, the circumstances of the shooting, and whether the person was armed or experiencing a mental-health crisis.

The Washington Post updates visualizations of the data and provides more information about methodology on the Fatal Force page.

Source: https://github.com/washingtonpost/data-police-shootings
Updated: synced daily
License: CC BY-NC-SA

This dataset was created by Data Society and contains around 7000 samples along with Is Geocoding Exact, Armed, technical information and other features such as: - Body Camera - State - and more.

How to use this dataset

• Analyze Latitude in relation to Manner Of Death
• Study the influence of Name on Age
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Data Society

Start A New Notebook!

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

The Shootings dashboard contains information on shooting incidents where a victim was struck by a bullet, either fatally or non-fatally; that occurred in the City of Boston and fall under Boston Police Department jurisdiction. The dashboard does not contain records for self-inflicted gunshot wounds or shootings determined to be justifiable. Information on the incident, and the demographics of victims are included. This information is updated based on analysis conducted by the Boston Regional Intelligence Center under the Boston Police Department Bureau of Intelligence and Analysis. The data is for 2015 forward, with a 7 day rolling delay to allow for analysis and data entry to occur.

Bloomington Police Department cases where officers have fired a gun at an individual.

Key code for Race:

A- Asian/Pacific Island, Non-Hispanic B- African American, Non-Hispanic C- Hawaiian/Other Pacific Island, Hispanic H- Hawaiian/Other Pacific Island, Non-Hispanic I- Indian/Alaskan Native, Non-Hispanic K- African American, Hispanic L- Caucasian, Hispanic N- Indian/Alaskan Native, Hispanic P- Asian/Pacific Island, Hispanic S- Asian, Non-Hispanic T- Asian, Hispanic U- Unknown W- Caucasian, Non-Hispanic

Key Code for Reading Districts:

Example: LB519

L for Law call or incident B stands for Bloomington 5 is the district or beat where incident occurred All numbers following represents a grid sector.

Disclaimer: The Bloomington Police Department takes great effort in making open data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided by many people and that cannot always be verified. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data, or for the use or interpretation of the results of any research conducted.