Data Series: Share of female police officers Indicator: IV.4 - Share of female police officers Source year: 2024 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Public life and decision-making

This dataset contains the details about the strength of Female Police Officers in India under different police organisations such as Assam Rifles, Border Security Force, Central Armed Police Force, Central Industrial Security Force, District Police, Central Reserve Police Force among others.

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).

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.

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.

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.

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.

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.

List of NYPD members of service

This dataset was retrieved from the U.S. Department of Justice Federal Bureau of Investigation Criminal Justice Information Services Division website on February 29, 2008. "This table provides the total number of state law enforcement employees, the total number of male officers, the total number of female officers, the total number of male civilian employees, and the total number of female civilian employees". Values for individual agencies were aggregated for each state. Estimated population was added for each state for 2006 that appeared on Table 5 of the data from 2006. "The FBI collects these data through the Uniform Crime Reporting (UCR) Program". Please see the Data Declaration for further information on the data set. Values of -1 represent no value.

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.

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.

This data set contains New York City Police Department provided felony assault count data for calendar years 2020 and 2021. The data includes counts of the number of intimate-partner felony assaults and the number of expected intimate-partner felony assaults by: race (American Indian, Asian, Black, Hispanic and White) and sex (male, female) for New York City, each borough (Bronx, Brooklyn, Manhattan, Queens and Staten Island) and community district. The following defines felony assault: Felony assault requires that a victim suffer a physical injury and covers injuries caused either intentionally or recklessly and includes injuries caused by either a deadly weapon or dangerous instrument. See New York Penal Law § § 120.05, 120.10. The expected number of felony assaults were calculated by taking the total number of actual felony assaults for a given geography (New York City, the Bronx, Brooklyn, Manhattan, Queens and Staten Island) and proportioning them by demographic breakdown of the geographic area.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

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 dataset provides information about duly sworn city, university and college, county, state, tribal, and federal law enforcement officers who were feloniously killed in the line of duty from 1997-2006 in the entire United States. More non-geographic statistics about these fatalities can be found at http://www.fbi.gov/ucr/killed/2006/feloniouslykilled.html note: Data from the past 10 years do not include the officers who died as a result of the events of September 11, 2001. http://www.fbi.gov/ucr/killed/2006/table1.html

The study set out to test the question of whether more efficacious outcomes would be gained the closer that a second response by police officers occurs to an actual domestic violence event. Researchers conducted a randomized experiment in which households that reported a domestic incident to the police were assigned to one of three experimental conditions: (a) second responders were dispatched to the crime scene within 24 hours, (b) second responders visited victims' homes one week after the call for service, or (c) no second response occurred. Beginning January 1, 2005, and continuing through December 3, 2005, incidents reported to the Redlands Police Department were reviewed each morning by a research assistant to determine whether the incidents involved intimate partners. Cases were determined to be eligible if the incident was coded as a misdemeanor or felony battery of a spouse or intimate partner. Eighty-two percent of the victims were females. For designated incidents, a team of officers, including a trained female domestic violence detective, visited households within either twenty-four hours or seven days of a domestic complaint. A written protocol guided the officer or officers making home visits. Officers also asked the victim a series of questions about her relationship with the abuser, history of abuse, and the presence of children and weapons in the home. In Part 1 (Home Visit Data), six months after the reporting date of the last incident in the study, Redlands Police crime analysis officers wrote a software program to search their database to determine if any new incidents had been reported. For Part 2 (New Incident Data), the search returned any cases associated with the same victim in the trigger incident. For any new incidents identified, information was collected on the date, charge, and identity of the perpetrator. Six months following the trigger incident, research staff attempted to interview victims about any new incidents of abuse that might have occurred. These interview attempts were made by telephone. In cases where the victim could not be reached by phone, an incentive letter was sent to the victim's home, offering a $50 stipend to call the research offices. Part 1 (Home Visit Data) contains 345 cases while Part 2 (New Incident Data) contains 344 cases. The discrepancy in the final number across the two parts is due to cases randomized into the sample that turned out to be ineligible or had been assigned previously from another incident. Part 1 (Home Visit Data) contains 63 variables including basic administrative variables such as date(s) of contact and group assignment. There are also variables related to the victim and the perpetrator such as their relationship, whether the perpetrator was arrested during the incident, and whether the perpetrator was present during the interview. Victims were also asked a series of questions as to whether the perpetrator did such things as hit, push, or threatened the victim. Part 2 (New Incident Data) contains 68 variables including dates and charges of previous incidents as well as basic administrative and demographic variables.

WORKING PAPER. ABSTRACT: By 2016, ongoing police shootings of unarmed black men had increased public awareness of policing processes; chief among those was militarization. Prior researchers explored this relatively new process, but very few had systematically analyzed it. In this paper, I conceptualize, operationalize, and describe police militarization across the United States. I transformed publicly available data, the Law Enforcement and Administrative Statistics (LEMAS) dataset, into a panel of over 4,000 police departments over an eighteen-year period. I then constructed an index based on each of three dimensions of militarization: (1) increased stock of military equipment; (2) increased usage of paramilitary tactics; and (3) increased focus on military-style policing. I explain the indicators within each sub dimension, and describe their unique trend across time. Using both this Absolute LEMAS Militarization Index (ALMI) and a summary analysis of each indicator, I describe militarization between 1990 and 2007 using hierarchical linear modeling. Among key findings are three trends. First, I provide nationally representative evidence of an increasing trend in police militarization across most departments, despite statistical controls. I also found that larger departments were both more militarized and militarized faster over time than smaller departments. Finally, I found that diverse departments (either proportionately more women or more black officers) militarized at a slower rate over the period. This paper provides the theoretical and methodological basis for follow-up analyses of processes affecting militarization, including variations in crime control, organizational characteristics and racial dynamics.

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 Juvenile
• Total Female Juvenile
• Total Male Adult
• Total Female Adult
• Total Ma

Female homicide rates from the Porto Alegre Medicolegal Department from January 2010 to December 2016 per 100,000 population.