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

The killing of Tyre Nichols in January 2023 by Memphis Police Officers has reignited debates about police brutality in the United States. Between 2013 and 2024, over 1,000 people have been killed by police in every year. Some of the most infamous examples include the murder of George Floyd in May 2020, and the shooting of Breonna Taylor earlier that year. Within this time period, the most people killed by police in the United States was in 2023, at 1,353 people. Police Violence in the U.S. Police violence is defined as any instance where a police officer’s use of force results in a civilian’s death, regardless of whether it is considered justified by the law. While many people killed by police in the U.S. were shot, other causes of death have included tasers, vehicles, and physical restraints or beatings. In the United States, the rate of police shootings is much higher for Black Americans than it is for any other ethnicity and recent incidents of police killing unarmed Black men and women in the United States have led to widespread protests against police brutality, particularly towards communities of color. America’s Persistent Police Problem Despite increasing visibility surrounding police violence in recent years, police killings have continued to occur in the United States at a consistently high rate. In comparison to other countries, police in the U.S. have killed people at a rate three times higher than police in Canada, and 60 times the rate of police in England. While U.S. police have killed people in almost all 50 states, as well as the District of Columbia, New Mexico was reported to have the highest rate of people killed by the police in the United States, with 8.03 people per million inhabitants killed by police.

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 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.

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.

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)

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.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program each year by police agencies in the United States. These arrest reports provide data on 43 offenses including violent crime, drug use, gambling, and larceny. The data received by ICPSR were structured as a hierarchical file containing, per reporting police agency: an agency header record, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to logical record length format with the agency header record variables copied onto the detail records. Consequently, each record contains arrest counts for a particular agency-offense.

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 ---

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

In the United States, more men than women are shot to death by the police. As of October 22, the U.S. police shot 904 men and 44 women to death in 2024. In 2023, the police shot 1,107 men and 48 women to death.

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 ---

Officer Involved Shooting (OIS) Database and Statistical Analysis. Data is updated after there is an officer involved shooting.PIU#Incident # - the number associated with either the incident or used as reference to store the items in our evidence rooms Date of Occurrence Month - month the incident occurred (Note the year is labeled on the tab of the spreadsheet)Date of Occurrence Day - day of the month the incident occurred (Note the year is labeled on the tab of the spreadsheet)Time of Occurrence - time the incident occurredAddress of incident - the location the incident occurredDivision - the LMPD division in which the incident actually occurredBeat - the LMPD beat in which the incident actually occurredInvestigation Type - the type of investigation (shooting or death)Case Status - status of the case (open or closed)Suspect Name - the name of the suspect involved in the incidentSuspect Race - the race of the suspect involved in the incident (W-White, B-Black)Suspect Sex - the gender of the suspect involved in the incidentSuspect Age - the age of the suspect involved in the incidentSuspect Ethnicity - the ethnicity of the suspect involved in the incident (H-Hispanic, N-Not Hispanic)Suspect Weapon - the type of weapon the suspect used in the incidentOfficer Name - the name of the officer involved in the incidentOfficer Race - the race of the officer involved in the incident (W-White, B-Black, A-Asian)Officer Sex - the gender of the officer involved in the incidentOfficer Age - the age of the officer involved in the incidentOfficer Ethnicity - the ethnicity of the suspect involved in the incident (H-Hispanic, N-Not Hispanic)Officer Years of Service - the number of years the officer has been serving at the time of the incidentLethal Y/N - whether or not the incident involved a death (Y-Yes, N-No, continued-pending)Narrative - a description of what was determined from the investigationContact:Carol Boylecarol.boyle@louisvilleky.gov

An Officer Involved Shooting (OIS) is the discharge of a firearm, whether accidental or intentional, by a police officer, whether on or off duty. For the purposes of this posting, an OIS will only refer to the instances in which an officer discharged a firearm at a person. CMPD posts Officer Involved Shootings in an effort to create greater transparency of the actions of our employees. It is important to us that members of the community are informed whenever an officer discharges his/her firearm at a person and whether the shooting follows department policies and procedures. We believe that your trust and confidence in the Charlotte-Mecklenburg Police Department will increase as you understand what our officers encounter and how we hold them accountable for their actions. The CMPD is continuously reviewing and improving our practices to reduce the likelihood of deadly force incidents. The following is a complete list of OIS tables posted on the Charlotte Open Data Portal:1) Officer Involved Shootings – Incidents: contains INCIDENT_ID (for joining to other OIS tables), year and month recorded, location, district attorney legal review status, descriptive narrative of incident2) Officer Involved Shootings – Individuals: contains INCIDENT_ID (for joining to other OIS tables) as well as individual’s race, gender, age, and injury type3) Officer Involved Shootings – Officers: contains INCIDENT_ID (for joining to other OIS tables) as well as officer’s race, gender, years of service, and CMPD policy violation status. Please note that the relationship of the Incidents table to the Individuals table is one-to-many, and the relationship of the Incidents table to the Officers table is one-to-many.

Data and Code for Does Race Matter for Police Use of Force? Evidence from 911 Calls This paper examines race and police use of force using data on 1.6 million 911 calls in two cities, neither of which allows for discretion in officer dispatch. Results indicate white officers increase force much more than minority officers when dispatched to more minority neighborhoods. Estimates indicate black (Hispanic) civilians are 55 (75) percent more likely to experience any force, and five times as likely to experience a police shooting, compared to if white officers scaled up force similarly to minority officers. Additionally, 14 percent of white officers use excess force in black neighborhoods relative to our statistical benchmark.