An interactive Excel-based data tool for domestic abuse statistics. It allows users to explore data for their police force area in more detail and compare with other areas.

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.

In 2024/25, there were approximately 815,941 domestic violence incidents recorded by the police in England and Wales, compared with the 851,062 offences reported in the previous year.

These Bloomington Police Department cases have been identified as Domestic Battery using the State Statue definition of 'domestic'.

Key code for Race:

A- Asian/Pacific Island, Non-Hispanic B- African American, Non-Hispanic C- Hawaiian/Other Pacific Island, Hispanic H- Hawaiian/Other Pacific Island, Non-Hispanic I- Indian/Alaskan Native, Non-Hispanic K- African American, Hispanic L- Caucasian, Hispanic N- Indian/Alaskan Native, Hispanic P- Asian/Pacific Island, Hispanic S- Asian, Non-Hispanic T- Asian, Hispanic U- Unknown W- Caucasian, Non-Hispanic

Key Code for Reading Districts:

Example: LB519

L for Law call or incident B stands for Bloomington 5 is the district or beat where incident occurred All numbers following represents a grid sector.

Disclaimer: The Bloomington Police Department takes great effort in making open data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided by many people and that cannot always be verified. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data, or for the use or interpretation of the results of any research conducted.

Domestic violence injuries by type as reported by the City of Clarkston Police Department to NIBRS (National Incident-Based Reporting System), Group A.

This project was an 18-month long research-practitioner partnership to conduct a process evaluation of the State College Police Department's implementation of a grant to encourage arrest policies for domestic violence. The general goals of the process evaluation were to assess how and to what extent the State College Police Department's proposed activities were implemented as planned, based on the rationale that such activities would enhance the potential for increasing victim safety and perpetrator accountability systemically. As part of the grant, the police department sought to improve case tracking and services to victims by developing new specialized positions for domestic violence, including: (1) a domestic violence arrest coordinator from within the State College Police Department who was responsible for monitoring case outcomes through the courts and updating domestic violence policies and training (Part 1, Victim Tracking Data from Domestic Violence Coordinator), (2) a victims service attorney from Legal Services who was responsible for handling civil law issues for domestic violence victims, including support, child custody, employment, financial, consumer, public benefits, and housing issues (Part 2, Victim Tracking Data From Victim Services Attorney), and (3) an intensive domestic violence probation officer from the Centre County Probation and Parole Department who was responsible for providing close supervision and follow-up of batterers (Part 3, Offender Tracking Data). Researchers worked with practitioners to develop databases suitable for monitoring service provision by the three newly-created positions for domestic violence cases. Major categories of data collected on the victim tracking form (Parts 1 and 2) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided at initial contact, meetings, and referrals out. Types of services provided include reporting abuse, filing a Protection from Abuse order, legal representation, and assistance with court procedures. Major categories of data collected on the offender tracking form (Part 3) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided, charges, sentence received, relationship between the victim and perpetrator, marital status, children in the home, referrals out, presentencing investigation completed, prior criminal history, and reason for termination. Types of services provided include pre-sentence investigation, placement on supervision, and assessment and evaluation. In addition to developing these new positions, the police department also sought to improve how officers handled domestic violence cases through a two-day training program. The evaluation conducted pre- and post-training assessments of all personnel training in 1999 and conducted follow-up surveys to assess the long-term impact of training. For Part 4, Police Training Survey Data, surveys were administered to law enforcement personnel participating in a two-day domestic violence training program. Surveys were administered both before and after the training program and focused on knowledge about domestic violence policies and protocols, attitudes and beliefs about domestic violence, and the background and experience of the officers. Within six months after the training, the same participants were contacted to complete a follow-up survey. Variables in Part 4 measure how well officers knew domestic violence arrest policies, their attitudes toward abused women and how to handle domestic violence cases, and their opinions about training. Demographic variables in Part 4 include age, sex, race, education, and years in law enforcement.

The PSNI produces statistics on the number of domestic abuse incidents and crimes recorded by the police in Northern Ireland. Statistics are published on a financial year basis and a comparable data series has been available since 2004/05. These statistics are collected in accordance with the definition of domestic abuse outlined in the Northern Ireland Government Strategy ‘Stopping Domestic and Sexual Violence and Abuse in Northern Ireland’.

This publication can be accessed here

The Domestic Violence Research Project was a pilot study designed to examine the dynamics of domestic violence within two of the ten police districts that comprise the city of Boston. The objectives were to collect data on domestic violence in greater detail than previously possible, conduct various analyses on this information, and determine how the findings could best be used to improve the police, prosecutorial, and social service responses to domestic violence. Data for 1993 are a stratified random sample of reported domestic violence incidents occurring throughout the year. The sample represents approximately 27 percent of the domestic violence incidents reported in 1993 for the two districts studied, B3 and D4. The 1994 data include all reported incidents occurring in the two districts during the period May to July. After the incident selection process was completed, data were collected from police incident reports, follow-up investigation reports, criminal history reports, and court dockets. Variables include arrest offenses, time of incident, location of incident, witnesses (including children), nature and extent of injuries, drug and alcohol use, history of similar incidents, whether there were restraining orders in effect, and basic demographic information on victims and offenders. Criminal history information was coded into five distinct categories: (1) violent offenses, (2) nonviolent offenses, (3) domestic violence offenses, (4) drug/alcohol offenses, and (5) firearms offenses.

The purpose of the study was to better understand the factors associated with police decisions to make an arrest or not in cases of heterosexual partner violence and how these decisions vary across jurisdictions. The study utilized data from three large national datasets: the National Incident-Based Reporting System (NIBRS) for the year 2003, the Law Enforcement Management and Administrative Statistics (LEMAS) for the years 2000 and 2003, and the United States Department of Health and Human Services Area Resource File (ARF) for the year 2003. Researchers also developed a database of domestic violence state arrest laws including arrest type (mandatory, discretionary, or preferred) and primary aggressor statutes. Next, the research team merged these four databases into one, with incident being the unit of analysis. As a further step, the research team conducted spatial analysis to examine the impact of spatial autocorrelation in arrest decisions by police organizations on the results of statistical analyses. The dependent variable for this study was arrest outcome, defined as no arrest, single male arrest, single female arrest, and dual arrest for an act of violence against an intimate partner. The primary independent variables were divided into three categories: incident factors, police organizational factors, and community factors.

Domestic abuse numbers, prevalence and types, by region and police force area, using annual data from the Crime Survey for England and Wales, police recorded crime and a number of different organisations.

Family and Domestic violence (FDV) is a global problem with significant social, economic, and health consequences for victims including increased health care costs, mental trauma, and social stigmatization. In Australia, the estimated annual cost of FDV is $22 billion, with one woman being murdered by a current or former partner every week. Despite this, tools that can predict future FDV based on the features of the person of interest (POI) and victim are lacking. The New South Wales Police Force attends thousands of FDV events each year and records details as fixed fields (e.g., demographic information for individuals involved in the event) and as text narratives which describe abuse types, victim injuries, threats, including the mental health status for POIs and victims. This information within the narratives is mostly untapped for research and reporting purposes. After applying a text mining methodology to extract information from 492,393 FDV event narratives (abuse types, victim injuries, mental illness mentions), we linked these characteristics with the respective fixed fields and with actual mental health diagnoses obtained from the NSW Ministry of Health for the same cohort to form a comprehensive FDV dataset. These data were input into five deep learning models (MLP, LSTM, Bi-LSTM, Bi-GRU, BERT) to predict three FDV offense types (“hands-on,” “hands-off,” “Apprehended Domestic Violence Order (ADVO) breach”). The transformer model with BERT embeddings returned the best performance (69.00% accuracy; 66.76% ROC) for “ADVO breach” in a multilabel classification setup while the binary classification setup generated similar results. “Hands-off” offenses proved the hardest offense type to predict (60.72% accuracy; 57.86% ROC using BERT) but showed potential to improve with fine-tuning of binary classification setups. “Hands-on” offenses benefitted least from the contextual information gained through BERT embeddings in which MLP with categorical embeddings outperformed it in three out of four metrics (65.95% accuracy; 78.03% F1-score; 70.00% precision). The encouraging results indicate that future FDV offenses can be predicted using deep learning on a large corpus of police and health data. Incorporating additional data sources will likely increase the performance which can assist those working on FDV and law enforcement to improve outcomes and better manage FDV events.

Domestic abuse numbers, prevalence, types and victim characteristics, based upon findings from the Crime Survey for England and Wales and police recorded crime.

This study was designed to address deficiencies in the existing literature on police work stress and especially on police stress-related domestic violence. The study sought to answer the following questions: (1) What is the relationship between police stress and domestic violence in police families? (2) What is the extent of domestic violence in police families? (3) What are the current stressors that contribute to police stress? (4) What are some of the tools available to measure or evaluate domestic violence in police families? (5) Can potentially effective interventions be identified to address the risk factors for stress-related domestic violence in police families? The study was a collaboration among the Baltimore City Fraternal Order of Police, the Baltimore Police Department, and a research team from the Johns Hopkins School of Public Health. Self-administered questionnaires were distributed to approximately 1,100 law enforcement officers who volunteered to participate in the study. Major variables focus on stressors, workplace/stress environment, coworker environment, unfair treatment, work satisfaction, administrative support, health problems, behavior problems, and psychological problems. Demographic variables include gender, age, ethnicity, education, current rank, military service, marital status, and if spouse/partner was a police officer.

In the CEE region, Slovenia recorded the highest proportion of women who experienced physical or sexual violence from their partners. At the same time, Slovenia recorded the highest percentage of women who reported domestic violence to the police in 2018. The lowest rate of women reporting domestic violence to the police was in Estonia and Czechia.

In 2023, about 44.7 percent of all violent crime committed in the United States was reported to the police. Aggravated assault was the most likely to be reported to police, at 57.1 percent. Simple assault, however, was the least likely to be reported to police, with 40.9 percent of such crimes reported to police in that year.

This Indicator measures the rate of domestic violence victimization in Oakland by race/ethnicity. Rate is calculated as the number of domestic violence incidents per 100,000 people of the same race/ethnicity (of any age).

Victims of police-reported family and non-family violence, by gender and age group of victim, and detailed relationship of accused to victim, Canada, 2009 to 2024.

This data was collected to support the HMIC report "Everyone’s business: Improving the police response to domestic abuse".