Since the 1780s, over 25,000 United States law enforcement officers have died while on active duty, or due to injuries and illnesses obtained while on duty. Gunfire is responsible for over half of all total law enforcement deaths recorded, particularly before the 1930s. From this point on, the total share of gunfire deaths has decreased significantly, mostly due to the increase in vehicle or health related deaths, although gunfire has remained the most common individual cause of death in almost every year. Gunfire deaths These deaths rose steadily after the Civil War, and peaked at over 200 annual deaths during the 1920s, due to the increase in criminal activity during prohibition. Because of this, the National Firearms Act of 1934 was introduced in an attempt to reduce gun-related crime, by requiring the registration of any transfer of ownership and imposing a tax on gun manufacturers and distributors. After Prohibition's end and the introduction of these measures, annual law enforcement deaths from gunfire have been well below 100 in almost every year, except for a brief spike in the early 1970s, during the crime wave that began in the 1960s and the beginning of the "War on Drugs". Overall, gunfire deaths of law enforcement have fallen since the 1970s, reaching a low of just 34 deaths in 2013. In contrast, the total number of people killed by police shootings has consistently been above one thousand for most of the past decade.

Increase in health-related deaths

The majority of non-gunfire deaths are a result of vehicle-related accidents, and the number of crashes and accidents rose throughout the 20th century in line with the increase in car ownership. However, the number of deaths from heart attacks and job-related illness has also risen over time, due to the aging of the population and increasingly unhealthy lifestyle trends across the country. In recent decades, additional health issues have emerged that have had a disproportionate impact on law enforcement. In 2001, 72 officers died as a result of the September 11th terrorist attacks, and over 350 additional deaths have been attributed to the adverse health effects (primarily respiratory illnesses) sustained by first responders from law enforcement. From 2020-2022, COVID-19 became the largest single cause of law enforcement deaths. Figures relate to cases where the individual contracted COVID-19 while on duty, and highlights the increased exposure to the virus faced by those considered to be frontline or essential workers. This number is likely to fall in the future as infection rates fall and newer strains of the virus are less lethal.

As of September 30, there were ** law enforcement officers killed in the United States in 2024. Within the provided time period, the highest number of law enforcement officers killed in the United States was reported in 2021, at ** officers.

In 2022, a total of 226 law enforcement officers died in the line of duty in the United States. This is a significant decrease from the year before, when 458 law enforcement officers died in the line of duty in the United States.

In 2020, Brazil recorded over *** deaths of police officers. The following year the number fell to a total of ***. In 2024, the number increased to *** deaths. Moreover, during 2023, the number of people killed by police officers in Brazil registered a year-on-year decrease of nearly one percent points.

Context

This dataset contains data behind the story, The Dallas Shooting Was Among The Deadliest For Police In U.S. History. The data are scraped from ODMP and capture information on all tracked on-duty police officer deaths in the U.S. broken down by cause from 1971 until 2016.

Content

This dataset tags every entry as human or canine. There are 10 variables:

• person

• dept: Department

• eow: End of watch

• cause: Cause of death

• cause_short: Shortened cause of death

• date: Cleaned EOW

• year: Year from EOW

• canine

• dept_name

• state

Inspiration

Using the data, can you determine the temporal trend of police officer deaths by cause? By state? By department?

Acknowledgements

The primary source of data is the Officer Down Memorial Page (ODMP), started in 1996 by a college student who is now a police officer and who continues to maintain the database. The original data and code can be found on the FiveThirtyEight GitHub.

This dataset contains information on fatal police deaths in the United States. The data includes the victim's rank, name, department, date of death, and cause of death. The data spans from 1791 to the present day. This dataset will be updated on monthly basis. Data Scrapped from this website :- https://www.odmp.org/

New Version Features -> With the new web scrapper I have upgraded dataset with more information. 1) The new dataset version is "police_deaths_USA_v6.csv" and "k9_deaths_USA_v6.csv". 2) Splitted the dataset into 2 different datasets 1 for Human Unit and other for K9 Unit. 3) Check out the new web scrapper code in this file "final_scrapper_program_with_comments.ipynb". 4) Also added the correction file which is needed to adjust some data points from K9 dataset. 5) Extended data of Human Unit dataset to 13 Features. 6) Extended data of K9 Unit dataset to 14 Features.

The police_deaths dataset contains 13 variables:

1) Rank -> Rank assigned or achieved by the police throughout their tenure.

2) Name -> The name of the person.

3) Age -> Age of the person.

4) End_Of_Watch -> The death date on which the the person declared as dead.

5) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

6) Cause -> The cause of the death.

7) Department -> The department's name where the person works.

8) State -> The state where the department is situated.

9) Tour -> The Duration of there Tenure.

10) Badge -> Badge of the person.

11) Weapon -> The Weapon by which the officer has been killed.

12) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

13) Summary -> Summary of the police officer and also the summary of the incident of what happened ? How he/she died ?, etc.

The k9_deaths dataset contains 14 variables:

1) Rank -> Rank assigned or achieved by the K9 throughout their tenure.

2) Name -> The name of the K9.

3) Breed -> Breed of the K9.

4) Gender -> Gender of the K9.

5) Age -> Age of the K9.

6) End_Of_Watch -> The death date on which the the person declared as dead.

7) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

8) Cause -> The cause of the death.

9) Department -> The department's name where the K9 was assigned.

10) State -> The state where the department is situated.

11) Tour -> The Duration of there Tenure.

12) Weapon -> The Weapon by which the officer has been killed.

13) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

14) Summary -> Summary of the K9 dog and also the summary of the incident of what happened ? How he/she died ?, etc.

Acknowledgements:

The original dataset was collected by FiveThirtyEight and it contains police death data from 1791 to 2016. Here is the link -> https://data.world/fivethirtyeight/police-deaths.

The reason I made this dataset is because it had not been updated since 2016 and the scrapping script was outdated, so I decided to make a new scrapper and update the dataset till present. I got this idea from the FiveThirtyEight group and a fellow kaggler, Satoshi Datamoto, who uploaded the dataset on kaggle. Thank you for inspiration.

Tableau Visualization link :- https://public.tableau.com/app/profile/mayuresh.koli/viz/USALawEnforcementLineofDutyDeaths/main_dashboard

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.

For most of the last 35 years, the number of police officers who die on the job in the U.S. declined, but one grim statistic held steady: The most common cause of death was gun homicide. Those numbers grew significantly on Thursday night when five police officers were shot and killed at a demonstration in Dallas that was protesting recent killings by police officers in other states. President Obama called it “a vicious, calculated and despicable attack on law enforcement.” Per officer, policing had become even safer in recent years than the overall death counts suggest, which makes the Dallas shooting that much more of a singular, horrific massacre. That’s because the decline in the number of deaths by police officers in the line of duty has occurred as the number of officers has risen. The number of full-time, sworn local police officers increased by 35 percent from 1987 to 2013, according to the Bureau of Justice Statistics. During that same period, the number of officers killed declined by 34 percent. 1 And a growing share of officer deaths are happening in accidental or deliberate car collisions.

In 2022, significantly more male law enforcement officers died in the line of duty in the United States than did female law enforcement officers. In that year, 204 male law enforcement officers died in the line of duty in the United States.

"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 w...

In 2023, the majority of law enforcement officers that were killed in the United States were white. That year, ** white law enforcement officers as well as **** Black officers were killed. Overall, a total of ** law enforcement officers were killed in the United States in that year.

Version 3 release notes:

• Fix bug where Philadelphia Police Department had incorrect FIPS county code.

The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. Each data set contains over 2,200 columns and has a wealth of information about the circumstances of assaults on officers.

All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. It was then cleaned in R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors).

About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself.

I did not make any changes to the numeric columns except for the following. A few years of data had the values "blank" or "missing" as indicators of missing values. Rows in otherwise numeric columns (e.g. jan_asslt_no_injury_knife) with these values were replaced with NA. There were three obvious data entry errors in officers killed by felony/accident that I changed to NA.

In 1978 the agency "pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.
In 1979 the agency "metuchen" (ORI = NJ01210) reported 991 officers killed by felony during August.
In 1990 the agency "penobscot state police" (ORI = ME010SP) reported 860 officers killed by accident during July.

No other changes to numeric columns were made.

Each zip file contains all years as individual monthly files of the specified data type It also includes a file with all years aggregated yearly and stacked into a single data set. Please note that each monthly file is quite large (2,200+ columns) so it may take time to download the zip file and open each data file.

For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data.

The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:

"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collection is to identify situations in which officers are killed or assaulted, describe the incidents statistically, and publish the data to aid agencies in developing policies to improve officer safety.

"... agencies must record assaults on sworn officers. Reporting agencies must count all assaults that resulted in serious injury or assaults in which a weapon was used that could have caused serious injury or death. They must include other assaults not causing injury if the assault involved more than mere verbal abuse or minor resistance to an arrest. In other words, agencies must include in this section all assaults on officers, whether or not the officers sustained injuries."

If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 12 release notes:Adds 2021 data.Version 11 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will (probably, I haven't seen confirmation either way) be the last LEOKA data they release. Changes .rda file to .rds.Version 10 release notes:Changes release notes description, does not change data.Version 9 release notes:Adds data for 2019.Version 8 release notes:Fix bug for years 1960-1971 where the number of months reported variable was incorrectly down by 1 month. I recommend caution when using these years as they only report either 0 or 12 months of the year, which differs from every other year in the data. Added the variable officers_killed_total which is the sum of officers_killed_by_felony and officers_killed_by_accident.Version 7 release notes:Adds data from 2018Version 6 release notes:Adds data in the following formats: SPSS and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 5 release notes: Adds data for 1960-1974 and 2017. Note: many columns (including number of female officers) will always have a value of 0 for years prior to 1971. This is because those variables weren't collected prior to 1971. These should be NA, not 0 but I'm keeping it as 0 to be consistent with the raw data. Removes support for .csv and .sav files.Adds a number_of_months_reported variable for each agency-year. A month is considered reported if the month_indicator column for that month has a value of "normal update" or "reported, not data."The formatting of the monthly data has changed from wide to long. This means that each agency-month has a single row. The old data had each agency being a single row with each month-category (e.g. jan_officers_killed_by_felony) being a column. Now there will just be a single column for each category (e.g. officers_killed_by_felony) and the month can be identified in the month column. This also results in most column names changing. As such, be careful when aggregating the monthly data since some variables are the same every month (e.g. number of officers employed is measured annually) so aggregating will be 12 times as high as the real value for those variables. Adds a date column. This date column is always set to the first of the month. It is NOT the date that a crime occurred or was reported. It is only there to make it easier to create time-series graphs that require a date input.All the data in this version was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. Data is the same as from NACJD but using all FBI files makes cleaning easier as all column names are already identical. Version 4 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 3 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. All the data was acquired from the FBI as text/DAT files and read into R using the package asciiSetupReader. The FBI also provided a PDF file explaining how to create the setup file to read the data. Both the FBI's PDF and the setup file I made are included in the zip files. About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself. Several agency had impossible large (>15) officer deaths in a single month. For those months I changed the value to NA. The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collection is to identify situations in which

This dataset is one of three that pull together data from several different sources regarding police-related violence and racial equity in the United States. The datasets currently include these types of data:

Part 1: Citizen deaths, police deaths, and other outcomes - Police shootings - Citizen fatalities involving police - Police officer deaths suffered in the line of duty

Part 2: Demographics, crime stats, protests, and other data - Social and economic data - Political leanings of citizens - Sales of DoD equipment to law enforcement agencies - City budgets - Police department headcounts - Police department policies and contract provisions - Juvenile arrests by type of crime and race - Crimes and arrests for the prime city in the four largest metro areas. - Protest activity - Police response - Press activity - Video clips of incidents

I don't believe that any amount of data can fully describe the social dysfunction we have. At the same time, I hope these datasets can be an objective source for providing thoughtful, fact-based analysis of this important issue.

In 2023, the states of Rio de Janeiro and São Paulo combined concentrated nearly half of the number of police officers killed in Brazil, with 36 and 25 deaths, respectively. Further, about 173 police officers were killed in Brazil in 2022.

This data serves two primary purposes: (1) to report the number of employees in each law enforcement agency, categorized by sworn officers and civilian employees, as well as by sex; and (2) to track the number of officers assaulted or killed each month. Employee counts are reported annually, providing agency-level totals without intra-year fluctuations. Assault data includes details on shift type (e.g., alone, with a partner, on foot, in a vehicle), the offender’s weapon, and the type of call (e.g., robbery, disturbance, traffic stop). Fatality data distinguishes between felonious deaths (i.e., murders) and accidental deaths.

The Uniform Crime Reporting Program Data, Police Employee Data, 2020 file contains monthly data on felonious or accidental killings and assaults upon United States law enforcement officers acting in the line of duty. The Federal Bureau of Investigation (FBI) assembled the data and processed them from UCR Master Police Employee (LEOKA) data tapes. Each agency record in the file includes the following summary variables: state code, population group code, geographic division, Metropolitan Statistical Area code, and agency name. These variables afford considerable flexibility in creating subsets or aggregations of the data. Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as a periodic nationwide assessment of reported crimes not available elsewhere in the criminal justice system. Each year, this information is reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Police Employee (LEOKA) Data provide information about law enforcement officers killed or assaulted (hence the acronym, LEOKA) in the line of duty. The variables created from the LEOKA forms provide in-depth information on the circumstances surrounding killings or assaults, including type of call answered, type of weapon used, and type of patrol the officers were on.

Content

Police Deaths

This directory contains the data and code behind the story The Dallas Shooting Was Among The Deadliest For Police In U.S. History. The primary source of data is the Officer Down Memorial Page (ODMP), started in 1996 by a college student who is now a police officer and who continues to maintain the database.

File descriptions:

File	Description
scrape.R	Scrapes data on the death of every officer tracked on ODMP
all_data.csv	Output of scrape.R
clean.R	Takes the data in all_data.csv, cleans it and formats the dates correctly, and tags every entry as human or canine.
clean_data.csv	Output of clean.R
plot.R	Summarizes police deaths by category and generates a plot similar to the one below

https://i1.wp.com/espnfivethirtyeight.files.wordpress.com/2016/07/bialik-flowers-king-police-deaths-1.png" alt="">

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

This data was obtained from https://mappingpoliceviolence.us/.

Mapping Police Violence is a 501(c)(3) organization that publishes the most comprehensive and up-to-date data on police violence in America to support transformative change.

This is a database set on openly sharing information on police violence in America.

Some information on this data according to their website: Our data has been meticulously sourced from official police use of force data collection programs in states like California, Texas and Virginia, combined with nationwide data from The Gun Violence Archive and the Fatal Encounters database, two impartial crowdsourced databases. We've also done extensive original research to further improve the quality and completeness of the data; searching social media, obituaries, criminal records databases, police reports and other sources to identify the race of 90 percent of all victims in the database.

We believe the data represented on this site is the most comprehensive accounting of people killed by police since 2013. Note that the Mapping Police Violence database is more comprehensive than the Washington Post police shootings database: while WaPo only tracks cases where people are fatally shot by on-duty police officers, our database includes additional incidents such as cases where police kill someone through use of a chokehold, baton, taser or other means as well as cases such as killings by off-duty police. A recent report from the Bureau of Justice Statistics estimated approximately 1,200 people were killed by police between June, 2015 and May, 2016. Our database identified 1,100 people killed by police over this time period. While there are undoubtedly police killings that are not included in our database (namely, those that go unreported by the media), these estimates suggest that our database captures 92% of the total number of police killings that have occurred since 2013. We hope these data will be used to provide greater transparency and accountability for police departments as part of the ongoing work to end police violence in America.

The circumstances surrounding "justifiable homicides" by police are the focus of this data collection, which examines occurrences in 57 United States cities during the period 1970-1979. Homicides by on- and off-duty police officers serving communities of 250,000 or more were studied. Data were collected through a survey questionnaire sent to police executives of the 57 cities. The Federal Bureau of Investigation supplied data on justifiable homicides by police, including age, sex, and race data. The variables include number of sworn officers, number of supervisory officers, average years of education, department regulations about issues such as off-duty employment, uniforms, carrying firearms, and disciplinary actions, in-service training, pre-service training, firearms practice, assignments without firearms, on-duty deaths, and off-duty deaths. The study was funded by a grant from the National Institute of Justice to the International Association of Chiefs of Police.