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.

Can gender-based “enclaves” facilitate women’s access to justice? I examine all-female police stations in India and test whether group-specific institutions assist victims of gender-based violence and female officers in law enforcement. I create an original dataset based on Indian police reports and leverage the manner in which all-women police stations were opened in Haryana state to estimate their causal effect.

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 "https://www150.statcan.gc.ca/n1/en/type/data?text=police+rank+and+gender">Statistics Canada.

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.

📹Project Video available on YouTube - https://youtu.be/GyUbo45mVSE

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

Police Check-posts Data

This dataset contains detailed records of police traffic stops. Each row represents a single stop, with information about the date, time, driver demographics, the reason for the stop, whether a search was conducted, and the outcome. It can be useful for analysing traffic stop patterns, demographic trends, law enforcement behaviour, and correlations with violations or arrests.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Instruction ( For Data Cleaning ) - Remove the column that only contains missing values

Q.2) For Speeding , were Men or Women stopped more often ?

Q.3) Does gender affect who gets searched during a stop ?

Q.4) What is the mean stop_duration ?

Q.5) Compare the age distributions for each violation

These are the main Features/Columns available in the dataset :

1) stop_date – The date on which the traffic stop occurred.

2) stop_time – The exact time when the stop took place.

3) driver_gender – Gender of the driver (M for male, F for female).

4) driver_age_raw – Raw recorded birth year of the driver.

5) driver_age – Calculated or cleaned driver’s age at the time of the stop.

6) driver_race – Race or ethnicity of the driver (e.g., White, Black, Asian, Hispanic).

7) violation_raw – Original recorded reason for the stop.

8) violation – Categorized reason for the stop (e.g., Speeding, Other).

9) search_conducted – Boolean value indicating whether a search was performed (True/False).

10) search_type – Type of search conducted, if any (e.g., vehicle search, driver search).

11) stop_outcome – The result of the stop (e.g., Citation, Arrest, Warning).

12) is_arrested – Boolean value indicating if the driver was arrested (True/False).

13) stop_duration – Approximate length of the stop (e.g., 0-15 Min, 16-30 Min).

14) drugs_related_stop – Boolean value indicating if the stop was related to drugs (True/False).

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 study used a mixed-methods approach to pursue five interrelated objectives: (1) to document the extent of case attrition and to identify the stages of the criminal justice process where attrition is most likely to occur; (2) to identify the case complexities and evidentiary factors that affect the likelihood of attrition in sexual assault cases; (3) to identify the predictors of case outcomes in sexual assault cases; (4) to provide a comprehensive analysis of the factors that lead police to unfound the charges in sexual assault cases; and (5) to identify the situations in which sexual assault cases are being cleared by exceptional means. Toward this end, three primary data sources were used: (1) quantitative data on the outcomes of sexual assaults reported to the Los Angeles Police Department (LAPD) and the Los Angeles County Sheriff's Department (LASD) from 2005 to 2009, (2) qualitative data from interviews with detectives and with deputy district attorneys with the Los Angeles District Attorney's Office who handled sexual assault cases during this time period, and (3) detailed quantitative and qualitative data from case files for a sample of cases reported to the two agencies in 2008. The complete case files for sexual assaults that were reported to the Los Angeles Police Department and the Los Angeles County Sheriff's Department in 2008 were obtained by members of the research team and very detailed information (quantitative and qualitative data) was extracted from the files on each case in Dataset 1 (Case Outcomes and Characteristics: Reports from 2008). The case file included the crime report prepared by the patrol officer who responded to the crime and took the initial report from the complainant, all follow-up reports prepared by the detective to whom the case was assigned for investigation, and the detective's reasons for unfounding the report or for clearing the case by arrest or by exceptional means. The case files also included either verbatim accounts or summaries of statements made by the complainant, by witnesses (if any), and by the suspect (if the suspect was interviewed); a description of physical evidence recovered from the alleged crime scene, and the results of the physical exam (Sexual Assault Response Team (SART) exam) of the victim (if the victim reported the crime within 72 hours of the alleged assault). Members of the research team read through each case file and recorded data in an SPSS data file. There are 650 cases and 261 variables in the data file. The variables in the data file include administrative police information and charges listed on the police report. There is also information related to the victim, the suspect, and the case. Datasets 2-5 were obtained from the district attorney's office and contain outcome data that resulted in the arrest of a suspect. The outcome data obtained from the agency was for the following sex crimes: rape, attempted rape, sexual penetration with a foreign object, oral copulation, sodomy, unlawful sex, and sexual battery. Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) is a subset of Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) contains 10,832 cases and 29 variables. Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) contains 891 cases and 45 variables. Similarly, Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) is a subset of Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) contains 3,309 cases and 33 variables. Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) contains 904 cases and 47 variables.

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.

List of NYPD members of service

Police Departments' Use of Lethality Assessments: An Experimental Evaluation examined the effectiveness of the Lethality Assessment Protocol (LAP), a tool used to gauge the severity of danger to victims of intimate partner violence (IPV) and determine whether to immediately connect victims with additional resources and safety options. Specifically, the evaluation focused on the effectiveness of the LAP at decreasing the rates of repeat, lethal and near lethal violence and increasing the rates of emergency safety planning and help seeking among women who experienced IPV and called the police. Additionally, the predictive and concurrent validity of the screening portion of the LAP was evaluated, as were the implementation of the LAP by officers and IPV victims' satisfaction with the police responses they experienced. The study consisted of two groups: (1) a comparison group, which included women who were victims of IPV and were referred to the study by a police officer; and (2) an intervention group which consisted of victims of IPV who were administered the LAP by police. Both groups were contacted for baseline and follow-up phone interview surveys that recorded the victims' self-reported demographic information (age, race, income, education marital status), information about the status of their relationships with their partners, as well as the type of abuse they had endured and how this affected their behavior.

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 study was an outcome evaluation of the effects of the Duluth Domestic Abuse Intervention Project Training Model for Law Enforcement Response on police officer attitudes toward domestic violence. Data on the effectiveness of the training were collected by means of an attitude survey of law enforcement officers (Part 1). Additionally, two experimental designs (Part 2) were implemented to test the effects of the Duluth model training on (1) time spent by police officers at the scene of a domestic violence incident, and (2) the number of convictions. Variables for Part 1 include the assigned research group and respondents' level of agreement with various statements, such as: alcohol is the primary cause of family violence, men are more likely than women to be aggressive, only mentally ill people batter their families, mandatory arrest of offenders is the best way to reduce repeat episodes of violence, family violence is a private matter, law enforcement policies are ineffective for preventing family violence, children of single-parent, female-headed families are abused more than children of dual-parent households, and prosecution of an offender is unlikely regardless of how well a victim cooperates. Index scores calculated from groupings of various variables are included as well as whether the respondent found training interesting, relevant, well-organized, and useful. Demographic variables for each respondent include race, gender, age, and assignment and position in the police department. Variables for Part 2 include whether the domestic violence case occurred before or after training, to which test group the case belongs, the amount of time in minutes spent on the domestic violence scene, and whether the case resulted in a conviction.

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.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Data on police officers (by high level ranks and gender), total number of civilian personnel, special constables and recruits (each by gender). The proportion of total police, civilians, special constables and recruits from total personnel, and also the proportion of high level ranks from total number of police officers. Data is provided for municipal police services, 2000 to 2023.

Description: Pursuant to the Sex Offender and Child Murderer Community Notification Law, 730 ILCS 152/101,et seq., the Chicago Police Department maintains a list of sex offenders residing in the City of Chicago who are required to register under the Sex Offender Registration Act, 730 ILCS 150/2, et seq. To protect the privacy of the individuals, addresses are shown at the block level only and specific locations are not identified. The data are extracted from the CLEAR (Citizen Law Enforcement Analysis and Reporting) system developed by the Department. Although every effort is made to keep this list accurate and current, the city cannot guarantee the accuracy of this information. Offenders may have moved and failed to notify the Chicago Police Department as required by law. If any information presented in this web site is known to be outdated, please contact the Chicago Police Department at srwbmstr@chicagopolice.org, or mail to Sex Registration Unit, 3510 S Michigan Ave, Chicago, IL 60653. Disclaimer: This registry is based upon the legislature's decision to facilitate access to publicly available information about persons convicted of specific sexual offenses. The Chicago Police Department has not considered or assessed the specific risk of re-offense with regard to any individual prior to his or her inclusion within this registry, and has made no determination that any individual included within the registry is currently dangerous. Individuals included within this registry are included solely by virtue of their conviction record and Illinois law. The main purpose of providing this data on the internet is to make the information more available and accessible, not to warn about any specific individual.

Anyone who uses information contained in the Sex Offender Database to commit a criminal act against another person is subject to criminal prosecution. Data Owner: Chicago Police Department. Frequency: Data is updated daily. Related Applications: CLEARMAP (http://j.mp/lLluSa).

Women's Crimes in India

Characteristics, Frequency, and Motives

By Rajanand Ilangovan [source]

About this dataset

This dataset contains extensive information about various types of crimes that happened in India from 2001 to 2019. Using this dataset, one can gain a deep insight into the crime trend and various factors that can be identified for analysing it. From Area_Name, Year, Sub_Group and CPA Cases Registered to Persons Acquitted- This dataset covers almost every single aspect of Crime against women in India while also giving a glance at other related aspects such as Auto-Theft Coordinated or Traced and Trials completed by courts. It is immensely helpful in understanding the crime patterns of India over time and make predictions accordingly

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How to use the dataset

Using this dataset, we can gain unparalleled insight into the prevalence and distribution of crimes against women over this period in different parts across India as well as within each state. This could be used for further research into the social impact on certain areas with heightened crime rates or for governmental organizations striving for initiatives to combat such criminal activities.

Research Ideas

• Analyzing patterns in violent crimes against women and children, such as the number of reported cases, total convictions and acquittals.
• Examining trends in different types of crime by state or city over time to identify hotspots or regional crime issues.
• Comparing police personnel performance to analyze effectiveness of action taken against certain types of crime in different areas over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: 25_Complaints_against_police.csv | Column name | Description | |:--------------------------------------------------------------------|:-------------------------------------------------------------------------------| | Area_Name | Name of the area where the crime was committed. (String) | | Year | Year in which the crime was committed. (Integer) | | Sub_group | Type of crime committed. (String) | | CPA_-_Cases_Registered | Number of cases registered in the given year. (Integer) | | CPA_-_Cases_Reported_for_Dept._Action | Number of cases reported to the department for action. (Integer) | | CPA_-_Complaints/Cases_Declared_False/Unsubstantiated | Number of complaints/cases declared false or unsubstantiated. (Integer) | | CPA_-_Complaints_Received/Alleged | Number of complaints received or alleged. (Integer) | | CPA_-_No_of_Departmental_Enquiries | Number of departmental enquiries. (Integer) | | CPA_-_No_of_Magisterial_Enquiries | Number of magisterial enquiries. (Integer) | | CPA-_Cases_Sent_for_Trials/Charge-sheeted | Number of cases sent for trial or charge-sheeted. (Integer) | | CPA-_No_of_Judicial_Enquiries | Number of judicial enquiries. (Integer) | | CPB_-_Police_Personnel_Acquitted | Number of police personnel acquitted. (Integer) | | CPB_-_Police_Personnel_Convicted ...

This dataset provides year, state and city-wise information on the police disposal of cases related to crimes against women in India's major metropolitan cities. It captures detailed statistics on the progress and outcome of police investigations, including the number of cases reported during the year, cases pending from previous years, and total cases taken up for investigation. It further covers various categories of final reports such as cases abated during investigation, found to be false, non-cognizable, involving civil disputes or mistakes of fact or law, and those closed as true but with insufficient evidence, untraced, or with no clue. The dataset also records cases reopened, withdrawn by the government, stayed, quashed, or transferred to other states or agencies. Key performance indicators such as pendency percentage, chargesheeting rate, total cases disposed of by police, and cases pending at the end of the year are also included, offering a comprehensive view of the investigative efficiency and outcomes of police handling of crimes against women across India’s metropolitan cities.

YouGov / Mayor of London Survey on women in the fire service and how this compares to the military, police and health service.

This dataset complements CSI-COP project deliverable D2.3 report: 'Framework for Engaging Citizen Scientists'.

The D2.3 deliverable was produced in 2020 by partners in CSI-COP work package 2 led by Professor Olga Stepankova of Czech Technical University, Prague (CTU).

The Stepankova et al. (2020) report is available on this Zenodo platform here:

https://zenodo.org/record/4066515#.Yrx9DezMLb0