On 31 March 2024, 91.6% of police officers were White, and 8.4% were from Asian, Black, Mixed, and Other ethnic backgrounds.

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.

In 2020, **** percent of full-time sworn officers in local police departments in the United States identified as white males, followed by **** percent of officers who identified as Hispanic males.

In 2024, approximately 8.4 percent of police officers in England and Wales were from ethnic minority backgrounds, compared with 8.1 percent in 2022, and just 3.5 percent in 2005.

This dataset contains Phoenix Police Department officer demographics as of January 1st of each year starting in 2018. All ranks of sworn employees are included.

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The data shows the number and percentage of people working for the police who come from different ethnic groups. It covers the 43 police forces in England and Wales, and the British Transport Police.

The figures are based on Home Office police workforce data, and are published on 'Ethnicity facts and figures'.

This data contains information related to officer-initated stops by the City of Boulder Police Department. Information on the demographics of the person stopped (sex, race, ethnicity, year of birth, whether they are a Boulder resident) is included in this file. See the "Outcomes of Police Stops" dataset for more details on the outcome of the stop (stop location, duration, search, and result). This demographic data is collected at the stop level, and no individual-level identifiers are recorded in the system during a stop.The data published are limited to stops where the officer initiated, or had discretion, in making a stop. Instances where an officer is responding to a community or police call are considered non-discretionary, and demographics information is not collected for those stops and not included here. There are some instances of non-discretion within a stop interaction as well. For example, there may be instances where there is an outstanding felony warrant for the person stopped, and by law the officer must arrest that person.Please read the methodology and data dictionary documents for more information. The fields for this demographics dataset are referred to as the "Main" file in the data dictionary.

Police complaints received, by police force area and ethnic appearance of complainant

Source: Ministry of Justice (MoJ)

Publisher: Ministry of Justice

Geographies: Police Force Area

Geographic coverage: England and Wales

Time coverage: 2006/07

Type of data: Administrative data

Replication data and code for Lyon and Malik, "Ethnicity & Policing in the Global South: Descriptive Representation & Expectations of Police Bias." Paper abstract: "City residents in the global south commonly encounter the police. Yet, outside of established democracies, we know little about how ethnicity shapes everyday policing in diverse urban contexts. Existing approaches generate competing expectations, with some arguing that officers are more rather than less discriminatory towards coethnics. We test these theories through a survey experiment conducted in Karachi, Pakistan – one of the world’s largest megacities. We find that civilians are only marginally less likely to expect procedural justice from non-coethnic officers, even in a context where ethnicity is highly salient. However, suggestive evidence indicates that this small effect is significantly magnified for respondents who perceive their group to be underrepresented in the police. Descriptive representation is therefore a powerful moderator of the relationship between ethnicity and expectations of police bias. These results have implications for the development of effective and legitimate police institutions in weakly institutionalized contexts."

This dataset contains arrests made by law enforcement, capturing essential information about each arrest incident. This data is important for analyzing crime trends, understanding patterns in enforcement, assessing demographic impacts, and informing policies to improve fairness and effectiveness in criminal justice practices. Arrests may include summonses, delayed referrals, or other incidents where an individual is not transported to jail or holding facility. Data is from January 1st, 2023 to December 31st, 2023.For inquiries about the data, please call 515-283-4887.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each year by police agencies in metropolitan statistical areas. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform Crime Report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses. For this collection, the arrests reported by each agency were summarized for each of the years 1960 through 1997, and the original Uniform Crime Reports data were restructured to create two separate files for each year, a header record and a detail record. Header files can be linked to detail files by the originating agency identifier (ORI). Other variables that are common to both types of files are state, census group, year, division, and metropolitan statistical agency (MSA). The header datasets also include the agency name and the population covered. The detail files also contain the offense code and the age, sex, and race of the arrestees.

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.

This dataset is about book series. It has 1 row and is filtered where the books is Black police associations : an analysis of race and ethnicity within constabularies. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Number of persons cautioned for notifiable offences, by police force area and ethnic appearance

Source: Ministry of Justice (MoJ)

Publisher: Ministry of Justice

Geographies: Police Force Area

Geographic coverage: England and Wales

Time coverage: 2006/07

Type of data: Administrative data

description:

Enforcement Action Taken by Driver's Sex, Race, and Ethnicity

Report From 1/1/2014 through 1/31/2015

Enforcement Action Taken by Driver's Sex, Race, and Ethnicity

Report From 1/1/2014 through 1/31/2015

The data shows:

• the number of stop and searches for different ethnic groups in England and Wales
• how many stop and searches there were for every 1,000 people in each ethnic group (the ‘rate’)

Data is also shown for the most police force areas.

Data comes from the Home Office and is published on 'Ethnicity facts and figures'.

Initial Purpose of Traffic Stop by Driver's Sex, Race, and Ethnicity.

Report From 1/1/2014 through 1/31/2015

The race of persons stopped by the San Diego Police Department, as perceived by the officer conducting the stop. This data is collected according to requirements set forth in Government Code section 12525.5 that was enacted as a result of the Racial and Identity Profiling Act of 2015 (AB 953), also known as RIPA. The file contains one row per perceived race per person stopped by Police. An officer may perceive more than one race for a person stopped. The person stopped is uniquely identified in the pid field, and the stop is uniquely identified in the stop_id field. These two fields can be used to join this dataset to the other RIPA datasets available at the following links: (Deprecated) Actions taken Contraband and/or evidence found Disability of persons Force Actions (Deprecated) Gender of persons Non-Force Actions Basis for property seizure Property seized Basis for searches conducted Reason for stop Result of stop Stop details For more information about RIPA regulations, see the California Code of Regulations final text.