Data Series: Share of female police officers Indicator: IV.4 - Share of female police officers Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Public life and decision-making

Political scientists have increasingly begun to study how citizen characteristics shape whether—and how—they interact with the police. Less is known about how officer characteristics shape these interactions. In this article, we examine how one officer characteristic—officer sex—shapes the nature of police-initiated contact with citizens. Drawing on literature from multiple fields, we develop and test a set of competing expectations. Using over four million traffic stops made by the Florida State Highway Patrol and Charlotte (North Carolina) Police Department, we find that women officers are less likely to search drivers than men on the force. Despite these lower search rates, when women officers do conduct a search, they are more likely to find contraband and they confiscate the same net amount of contraband as men. These results indicate that women officers are able to minimize the number of negative interactions with citizens without losses in effectiveness.

This data set is no longer compiled by the Ministry of Solicitor Services Information on the number of police officers according to their rank and gender. Shows the number of male and female officers at each rank, as well as annual changes in these numbers. The data can be accessed from Statistics Canada.

This table contains 96 series, with data for years 1986 - 2009 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (16 items: Canada;Newfoundland and Labrador;Prince Edward Island;Nova Scotia; ...); Sex (3 items: Both sexes;Males;Females); Statistics (2 items: Total number of police officers;Percentage of total police officers).

Data on police personnel (police officers by gender, civilian and other personnel), police officers and authorized strength per 100,000 population, authorized police officer strength, population, net gain or loss from hirings and departures, police officers eligible to retire and selected crime statistics. Data is provided for municipal police services, 2000 to 2023.

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.

Data on police officers (by detailed ranks and gender), civilian personnel and special constables (by detailed duties and gender), and recruits (by gender). Data is provided for Canada, provinces, territories and the Royal Canadian Mounted Police (RCMP) headquarters, training academy depot division and forensic labs, 1986 to 2023.

Data on police personnel (police officers by gender, civilian and other personnel), police-civilian ratio, police officers and authorized strength per 100,000 population, authorized police officer strength and selected crime statistics. Data is provided for Canada, provinces, territories and the Royal Canadian Mounted Police (RCMP) headquarters, training academy depot division and forensic labs, 1986 to 2023.

This project provided the first large-scale examination of the police response to intimate partner violence and of the practice known as "dual arrest." The objectives of the project were: (1) to describe the prevalence and context of dual arrest in the United States, (2) to explain the variance in dual arrest rates throughout the United States, (3) to describe dual arrest within the full range of the police response to intimate partner violence, (4) to analyze the factors associated with no arrest, single arrest, and dual arrest, (5) to examine the reasons why women are arrested in intimate partner cases, and (6) to describe how the criminal justice system treats women who have been arrested for domestic violence. Data for the project were collected in two phases. In Phase I, researchers examined all assault and intimidation cases in the year 2000 National Incident-Based Reporting System (NIBRS) database (NATIONAL INCIDENT-BASED REPORTING SYSTEM, 2000 [ICPSR 3449]) to investigate the extent to which dual arrest is occurring nationwide, the relationship between incident and offender characteristics, and the effect of state laws on police handling of these cases for all relationship types. Because the NIBRS dataset contained a limited number of incident-specific variables that helped explain divergent arrest practices, in Phase II, researchers collected more detailed information on a subset of NIBRS cases from 25 police departments of varying sizes across four states. This phase of the study was restricted to intimate partner and other domestic violence cases. Additional data were collected for these cases to evaluate court case outcomes and subsequent re-offending. This phase also included an assessment of how closely department policy reflected state law in a larger sample of agencies within five states. The data in Part 1 (Phase I Data) contain 577,862 records from the NIBRS. This includes information related to domestic violence incidents such as the most serious offense against the victim, the most serious victim injury, the assault type, date of incident, and the counts of offenses, offenders, victims, and arrests for the incident. The data also include information related to the parties involved in the incident including demographics for the victim(s) and arrestee(s) and the relationship between victim(s) and arrestee(s). There is also information related to the jurisdiction in which the incident occurred such as population, urban/rural classification, and whether the jurisdiction is located in a metropolitan area. There are also variables pertaining to whether a weapon was used, the date of arrest, and the type of arrest. Also included are variables regarding the police department such as the number of male and female police officers and civilians employed. The data in Part 2 (Phase II Data) contain 4,388 cases and include all of the same variables as those in Part 1. In addition to these variables, there are variables such as whether the offender was on the scene when the police arrived, who reported the incident, the exact nature of injuries suffered by the involved parties, victim and offender substance use, offender demeanor, and presence of children. Also included are variables related to the number of people including police and civilians who were on the scene, the number of people who were questioned, whether there were warrants for the victim(s) or offender(s), whether citations were issued, whether arrests were made, whether any cases were prosecuted, the number of charges filed and against whom, and the sentences for prosecuted cases that resulted in conviction. The data in Part 3 (Police Department Policy Data) contain 282 cases and include variables regarding whether the department had a domestic violence policy, what the department's arrest policy was, whether a police report needed to be made, whether the policy addressed mutual violence, whether the policy instructed how to determine the primary aggressor, and what factors were taken into account in making a decision to arrest. There is also information related to the proportion of arrests involving intimate partners, the proportion of arrests involving other domestics, the proportion of arrests involving acquaintances, and the proportion of arrests involving strangers.

The purpose of this study was to use data from the National Crime Survey (NCS) and the National Crime Victimization Survey (NCVS) to explore whether the likelihood of police notification by rape victims had increased between 1973-2000. To avoid the ambiguities that could arise in analyses across the two survey periods, the researchers analyzed the NCS (1973-1991) and NCVS data (1992-2000) separately. They focused on incidents that involved a female victim and one or more male offenders. The sample for 1973-1991 included 1,609 rapes and the corresponding sample for 1992-2000 contained 636 rapes. In their analyses, the researchers controlled for changes in forms of interviewing used in the NCS and NCVS. Logistic regression was used to estimate effects on the measures of police notification. The analyses incorporated the currently best available methods of accounting for design effects in the NCS and NCVS. Police notification served as the dependent variable in the study and was measured in two ways. First, the analysis included a polytomous dependent variable that contrasted victim reported incidents and third-party reported incidents, respectively, with nonreported incidents. Second, a binary dependent variable, police notified, also was included. The primary independent variables in the analysis were the year of occurrence of the incident reported by the victim and the relationship between the victim and the offender. The regression models estimated included several control variables, including measures of respondents' socioeconomic status, as well as other victim, offender, and incident characteristics that may be related both to the nature of rape and to the likelihood that victims notify the police.

The purpose of this data collection was to investigate the effects of crime rates, city characteristics, and police departments' financial resources on felony case attrition rates in 28 cities located in Los Angeles County, California. Demographic data for this collection were obtained from the 1983 COUNTY AND CITY DATA BOOK. Arrest data were collected directly from the 1980 and 1981 CALIFORNIA OFFENDER BASED TRANSACTION STATISTICS (OBTS) data files maintained by the California Bureau of Criminal Statistics. City demographic variables include total population, minority population, population aged 65 years or older, number of female-headed families, number of index crimes, number of families below the poverty level, city expenditures, and police expenditures. City arrest data include information on number of arrests disposed and number of males, females, blacks, and whites arrested. Also included are data on the number of cases released by police, denied by prosecutors, and acquitted, and data on the number of convicted cases given prison terms.

​This dataset provides the ranges of hourly rates of pay for all City job classifications, corresponding job titles and the breakdown of staff by sex within these classifications. The dataset does not include information from the Vancouver Public Library and the Vancouver Police Department. NoteThis dataset was published in 2019 as ‘workforce pay rates and gender’. In 2022, the dataset name was corrected to ‘Workforce pay rates and sex’, which is a more accurate reflection of the data categories available at the time of collection (see below for more details). Efforts are now underway to improve and increase transparency of data collection methods, and update categories to be inclusive of those outside the gender binary, and reflect gender instead of sex. Sex breakdown and salary ranges are only shown for a classification or specific position title when there are 10 or more people in that group. This is necessary in order to preserve confidentiality. If there are fewer than 10, the groups (within the same group level) are aggregated until the total exceeds the minimum number threshold.To explain the aggregation in more details: Data for each year is aggregated separately. If necessary, groups are aggregated in this order: Exempt/Union, Classification and then by Position title. Multiple aggregations (of the same group level) may take place in order to meet the minimum number requirement of 10 people. These aggregations are labelled with *Multiple. The field Data Category explains whether a row represents a detailed group with data, an aggregated group with data, or a group where detailed data is suppressed (reported as part of an aggregation). Data accuracy ​The minimum and maximum hourly rates listed reflect the current rates of pay for the classification for the effective year. For the full salary range of each classification, refer to the relevant Collective Agreement here​. The City’s employee database currently contains the sex designations ‘male’ or ‘female’ for employees. Historically the sex information was indicated by the managers on hiring forms, and has not been explicitly asked of or verified by the employee. As such, not all employees have been provided designations as male or female, and so the total count for “Male” and “Female” combined may not equal the total employee count.

 Sex is typically understood to be a biological concept, distinct from gender which is a social concept. With updates in technology and data collection methods, the City is moving towards collecting self-disclosed gender information (woman, man, gender-diverse etc.) for all employees. This will lead to a more accurate picture of the City’s workforce and will become the basis of this reporting exercise in future years.

The data does not include employees who are unionized and currently acting in exempt positions. Data as in effect in the City’s payroll information system on April 30th of the reporting year​​

The following table, produced by the NSW Bureau of Crime Statistics and Research (BOCSAR) provides information on rates, trends and patterns in domestic violence incidents reported to, or detected by, the NSW Police Force for the period of 2017/18. The data has been aggregated to location following the 2018 Australian Statistical Geography Standard (ASGS) edition of the Local Government Areas (LGAs). Domestic violence is a serious problem which impacts many NSW families. In 2012, an estimated 16.9 per cent of Australian women aged 18 years and over had experienced partner violence since the age of 15 years (ABS Personal Safety Survey 2012). Rate calculations should also be treated very cautiously for LGAs that have high visitor numbers relative to their residential population. This is because rate calculations are based on estimated residential population and no adjustment has been made for the number of people visiting each LGA per year. For the rate calculations, specialised population data were prepared and provided to BOCSAR by the Australian Bureau of Statistics (ABS). For more information please visit the BOSCAR Portal. Please note: AURIN has spatially enabled the original data. LGAs which have populations less than 3000 has been suppressed to maintain confidentiality. Original data values of "n.c." have been set to null.

This study sought to answer the question: If a woman is experiencing intimate partner violence, does the collective efficacy and community capacity of her neighborhood facilitate or erect barriers to her ability to escape violence, other things being equal? To address this question, longitudinal data on a sample of 210 abused women from the CHICAGO WOMEN'S HEALTH RISK STUDY, 1995-1998 (ICPSR 3002) were combined with community context data for each woman's residential neighborhood taken from the Chicago Alternative Policing Strategy (CAPS) evaluation, LONGITUDINAL EVALUATION OF CHICAGO'S COMMUNITY POLICING PROGRAM, 1993-2000 (ICPSR 3335). The unit of analysis for the study is the individual abused woman (not the neighborhood). The study takes the point of view of a woman standing at a street address and looking around her. The characteristics of the small geographical area immediately surrounding her residential address form the community context for that woman. Researchers chose the police beat as the best definition of a woman's neighborhood, because it is the smallest Chicago area for which reliable and complete data are available. The characteristics of the woman's police beat then became the community context for each woman. The beat, district, and community area of the woman's address are present. Neighborhood-level variables include voter turnout percentage, organizational involvement, percentage of households on public aid, percentage of housing that was vacant, percentage of housing units owned, percentage of feminine poverty households, assault rate, and drug crime rate. Individual-level demographic variables include the race, ethnicity, age, marital status, income, and level of education of the woman and the abuser. Other individual-level variables include the Social Support Network (SSN) scale, language the interview was conducted in, Harass score, Power and Control score, Post-Traumatic Stress Disorder (PTSD) diagnosis, other data pertaining to the respondent's emotional and physical health, and changes over the past year. Also included are details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Help-seeking in the past year includes whether the woman had sought medical care, had contacted the police, or had sought help from an agency or counselor, and whether she had an order of protection. Several variables reflect whether the woman left or tried to leave the relationship in the past year. Finally, the dataset includes summary variables about violent incidents in the past year (severity, recency, and frequency), and in the follow-up period.

Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics. Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load. All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998. To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns. To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros. I created 9 arrest categories myself. The categories are: Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the

This fact sheet is based on two research publications from the Canadian Centre for Justice Statistics (CCJS) published in 2015 and 2018. In the 2015 Juristat, CCJS linked police-reported data from the Uniform Crime Reporting Survey to court data from the Adult Criminal Court Survey to identify intimate partner violence (IPV) cases and their outcomes. All figures noted in this JustFacts are based on police-reported information and therefore are likely an under-representation of the true scope of the problem.

Abstract copyright UK Data Service and data collection copyright owner. This dataset comprises data from Waves 1 and 2 of the BES 2001 panel study, part of the first 'main component' of the BES. A third wave of the panel study was conducted in May 2002, but the data from this is not yet included in the UKDA dataset. Main Topics: The following subjects were covered in the survey: political preferences and values, economic perceptions, social attitudes, dispositions to engage in different forms of political activity, individual and household socio-demographic characteristics. Constituency-level information for aggregate analysis is also included in the data file. This covers election results for 2001, percentage of votes for each party, 'swing' between parties, changes in vote and turnout since 1997, demographic characteristics of each of the major party candidates, election results for 1997 and 1992, and characteristics of the area (including levels of home ownership, ethnicity, economic activity, age of population and Socio-Economic Group (SEG) classifications of households in the area). Multi-stage stratified random sample for full details of sampling procedures, please see documentation. Face-to-face interview Self-completion 2001 AGE ASSAULT ATTITUDES BRITISH POLITICAL P... BUSINESSES CANVASSING CARE OF DEPENDANTS CENSORSHIP CENTRAL GOVERNMENT CHARITABLE ORGANIZA... CHILDREN CIVIL AND POLITICAL... CIVIL SERVICE COMMUNITIES CONSERVATIVE PARTY ... CONSTITUENCIES CULTURAL BEHAVIOUR DEATH PENALTY DECENTRALIZED GOVER... DEMOCRACY ECONOMIC ACTIVITY ECONOMIC CONDITIONS EDUCATION EDUCATIONAL BACKGROUND ELECTION BROADCASTING ELECTION CAMPAIGNS ELECTION DATA ELECTION RESULTS ELECTIONS ELECTORAL ISSUES ELECTORAL SYSTEMS ELECTORS EMPLOYEES EMPLOYMENT ENVIRONMENTAL CONSE... EQUALITY BEFORE THE... ETHNIC GROUPS EUROPEAN UNION FINANCIAL EXPECTATIONS FINANCIAL RESOURCES FINANCIAL SUPPORT GENDER GENDER ROLE GOVERNMENT POLICY GREEN PARTY UNITED ... Great Britain HOME OWNERSHIP HOUSEHOLDS IMMIGRANTS INCOME INCOME DISTRIBUTION INFLATION INTERNET LABOUR PARTY GREAT ... LANGUAGES LAW ENFORCEMENT LEGISLATURE LEISURE TIME ACTIVI... LIBERAL DEMOCRATS G... LOCAL GOVERNMENT MARITAL STATUS MEMBERS OF PARLIAMENT MEMBERSHIP NATIONAL IDENTITY NEWSPAPER READERSHIP NEWSPAPERS OCCUPATIONAL STATUS OCCUPATIONS PARLIAMENTARY CANDI... PENSIONS PLAID CYMRU POLICE SERVICES POLITICAL ACCOUNTAB... POLITICAL ALLEGIANCE POLITICAL ATTITUDES POLITICAL EXTREMISM POLITICAL INFLUENCE POLITICAL INTEREST POLITICAL ISSUES POLITICAL LEADERS POLITICAL PARTICIPA... POLITICAL POWER POLITICAL SYSTEMS POLITICIANS PRISON SENTENCES PRIVATE SECTOR PROPORTIONAL REPRES... PUBLIC EXPENDITURE PUBLIC SECTOR PUBLIC TRANSPORT QUALIFICATIONS QUALITY OF LIFE REFERENDUM PARTY GR... REFUGEES REHABILITATION OFFE... RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RISK SCOTTISH NATIONAL P... SELF EMPLOYED SINGLE EUROPEAN CUR... SOCIAL CLASS SOCIAL JUSTICE SOCIAL POLICY SOCIAL PROTEST SOCIAL SERVICES SOCIO ECONOMIC STATUS SPOUSE S ECONOMIC A... SPOUSE S OCCUPATION SPOUSES STANDARD OF LIVING STATE HEALTH SERVICES SUPERVISORY STATUS TACTICAL VOTING TAXATION TELEPHONES TELEVISION NEWS TOLERANCE TRADE UNION MEMBERSHIP TRADE UNIONS TRUST UNEMPLOYMENT VOLUNTARY WORK VOTING VOTING BEHAVIOUR VOTING INTENTION WEALTH WOMEN YOUTH