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 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.

Since 2013, protests opposing police violence against Black people have occurred across a number of American cities under the banner of “Black Lives Matter.” We develop a new dataset of Black Lives Matter protests that took place in 2014–2015 and explore the contexts in which they emerged. We find that Black Lives Matter protests are more likely to occur in localities where more Black people have previously been killed by police. We discuss the implications of our findings in light of the literature on the development of social movements and recent scholarship on the carceral state’s impact on political engagement.

About this dataset

The Washington Post compiled a dataset of every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

In 2015, The Post began tracking more than a dozen details about each killing by culling local news reports, law enforcement websites and social media and by monitoring independent databases such as Killed by Police and Fatal Encounters. The available features are: - Race of the deceased; - Circumstances of the shooting; - Whether the person was armed; - Whether the victim was experiencing a mental-health crisis; - Among others.

In 2016, The Post is gathering additional information about each fatal shooting that occurs this year and is filing open-records requests with departments. More than a dozen additional details are being collected about officers in each shooting.

The Post is documenting only those shootings in which a police officer, in the line of duty, shot and killed a civilian — the circumstances that most closely parallel the 2014 killing of Michael Brown in Ferguson, Mo., which began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths.

The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. In 2015, The Post documented more than two times more fatal shootings by police than had been recorded by the FBI. Last year, the FBI announced plans to overhaul how it tracks fatal police encounters.

How to use this dataset

• Understanding US Police Shootings by Curtis Thompson
• Dataset Questions and Suggestions
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors.

BibTeX

@misc{wapo-police-shootings-bot , author = {The Washington Post}, title = {data-police-shootings}, month = jan, year = 2015, publisher = {Github}, url = {https://github.com/washingtonpost/data-police-shootings} }

License

CC BY NC SA 4.0

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A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI’s Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.

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.

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

The Washington Post is compiling a database of every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

In 2015, The Post began tracking more than a dozen details about each killing — including the race of the deceased, the circumstances of the shooting, whether the person was armed and whether the person was experiencing a mental-health crisis — by culling local news reports, law enforcement websites and social media, and by monitoring independent databases such as Killed by Police and Fatal Encounters. The Post conducted additional reporting in many cases.

In 2016, The Post is gathering additional information about each fatal shooting by police that occurs this year and is filing open-records requests with departments. More than a dozen additional details are being collected about officers in each shooting. Officers’ names are being included in the database after The Post contacts the departments to request comment.

The Post is documenting only those shootings in which a police officer, in the line of duty, shoots and kills a civilian — the circumstances that most closely parallel the 2014 killing of Michael Brown in Ferguson, Mo., which began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths. The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. In 2015, The Post documented more than twice as many fatal shootings by police as had been recorded by the FBI. Last year, the FBI announced plans to overhaul how it tracks fatal police encounters.

The Post's database is updated regularly as fatal shootings are reported and as facts emerge about individual cases. The Post is seeking assistance in making the database as comprehensive as possible. To provide information about fatal police shootings since Jan. 1, 2015, send us an email at policeshootingsfeedback@washpost.com. The Post is also interested in obtaining photos of the deceased and original videos of fatal encounters with police.

Data Dictionary

...

Key	List of...	Comment	Example Value
Person.Name	String	Full name of the individual or "Unknown" if not reported	"Tim Elliot"
Person.Age	Integer	Age in years of the individual or 0 (zero) if not reported	53
Person.Gender	String	One of Male, Female, or Unknown	"Male"
Person.Race	String	One of Asian, African American, White, Hispanic, Native American, Other, or Unknown.	"Asian"
Incident.Date.Month	Integer	Month (1-12) in which the shooting occurred	1
Incident.Date.Day	Integer	Day (1-31) in which the shooting occurred	2
Incident.Date.Year	Integer	Year (2015-2019) in which the shooting occurred	2015
Incident.Date.Full	String	Date in which shooting occurred (Year/Month/Day)	"2015/01/02"
Incident.Location.City	String	Name of city in which the shooting occurred	"Shelton"
Incident.Location.State	String	Name of U.S. State in which the shooting occurred	"WA"
Factors.Armed	String	Description of any weapon carried by the person (.e., "gun", "knife", "unarmed"); value is "unknown" if not reported.	"gun"
Factors.Mental-Illness	Boolean	True if factors of mental illness were perceived in the person; False otherwise	True
Factors.Threat-Level	String	Threat of person as perceived by police. One of "attack", "undetermined", or "other"; value is "unknown" if not reported.	"attack"
Factors.Fleeing	String	Means by which person was fleeing (e.g., "Car", "Foot") or "Not fleeing"; value is "unknown" if not reported.	"Not fleeing"
Shooting.Manner

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

Field descriptions:

Header	Description	Source
name	Name of deceased	Guardian
age	Age of deceased	Guardian
gender	Gender of deceased	Guardian
raceethnicity	Race/ethnicity of deceased	Guardian
month	Month of killing	Guardian
day	Day of incident	Guardian
year	Year of incident	Guardian
streetaddress	Address/intersection where incident occurred	Guardian
city	City where incident occurred	Guardian
state	State where incident occurred	Guardian
latitude	Latitude, geocoded from address
longitude	Longitude, geocoded from address
state_fp	State FIPS code	Census
county_fp	County FIPS code	Census
tract_ce	Tract ID code	Census
geo_id	Combined tract ID code
county_id	Combined county ID code
namelsad	Tract description	Census
lawenforcementagency	Agency involved in incident	Guardian
cause	Cause of death	Guardian
armed	How/whether deceased was armed	Guardian
pop	Tract population	Census
share_white	Share of pop that is non-Hispanic white	Census
share_bloack	Share of pop that is black (alone, not in combination)	Census
share_hispanic	Share of pop that is Hispanic/Latino (any race)	Census
p_income	Tract-level median personal income	Census
h_income	Tract-level median household income	Census
county_income	County-level median household income	Census
comp_income	h_income / county_income	Calculated from Census
county_bucket	Household income, quintile within county	Calculated from Census
nat_bucket	Household income, quintile nationally	Calculated from Census
pov	Tract-level poverty rate (official)	Census
urate	Tract-level unemployment rate	Calculated from Census
college	Share of 25+ pop with BA or higher	Calculated from Census

Note regarding income calculations:

All income fields are in inflation-adjusted 2013 dollars.

comp_income is simply tract-level median household income as a share of county-level median household income.

county_bucket provides where the tract's median household income falls in the distribution (by quintile) of all tracts in the county. (1 indicates a tract falls in the poorest 20% of tracts within the county.) Distribution is not weighted by population.

nat_bucket is the same but for all U.S. counties.