XD-Violence is a large-scale audio-visual dataset for violence detection in videos.

Dataset for the paper "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision". The dataset is downloaded from the authors' website (https://roc-ng.github.io/XD-Violence/). Hosting this dataset on HuggingFace is just to make it easier for my own project to use this dataset. Please cite the original paper if you use this dataset.

The "Audio-based Violence Detection Dataset" is a curated collection of audio files specifically designed to aid in detecting and analyzing violent events based purely on sound. Originating from many YouTube videos, these files represent a wide range of violent incidents, most of which were captured using low-fidelity devices such as mobile phones. The predominant sounds within these recordings capture the essence of human vocal expressions during heightened aggression. These may encompass shouts, screams, aggressive verbal confrontations, and the discernible sounds of physical confrontations. Labeling the dataset was meticulously performed through a two-stage process involving dual researchers to ensure maximum objectivity.

Crowd Violence \ Non-violence Database and benchmark: A database of real-world, video footage of crowd violence, along with standard benchmark protocols designed to test both violent/non-violent classification and violence outbreak detections. The data set contains 246 videos. All the videos were downloaded from YouTube. The shortest clip duration is 1.04 seconds, the longest clip is 6.52 seconds, and the average length of a video clip is 3.60 seconds.

Introduced in: Tal Hassner, Yossi. Itcher, and Orit Kliper-Gross, Violent Flows: Real-Time Detection of Violent Crowd Behavior, 3rd IEEE International Workshop on Socially Intelligent Surveillance and Monitoring (SISM) at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Rhode Island, June 2012 .

Internet-of-Things (IoT) technology such as Surveillance cameras are becoming a widespread feature of citizens' life. At the same time, the fear of crime in public spaces (e.g., terrorism) is ever-present and increasing but currently only a small number of studies researched automatic recognition of criminal incidents featuring artificial intelligence (AI), e.g., based on deep learning and computer vision. This is due to the fact that little to none real data is available due to legal and privacy regulations. Consequently, it is not possible to train and test deep learning models. A solution to such shortcoming of datasets is through the use of generative technology and virtual gaming data. Virtual games are a compelling source of data since they can simulate many different scenarios for diverse criminal activities e.g., think of the Grand-Theft Auto (GTA) gaming platform and its opportunities. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world videos to improve the performance of deep learning models in practice. The aim of this work is to investigate the possibilities to identify criminal scenarios with a deep learning model based on video gaming data.We propose a deep learning violence detection framework using virtual gaming data. The proposed framework is based on a 3-stage end-to-end framework that can be used in crime detection systems. The deep learning framework is divided into two parts: (1) person identification and (2) violence activity recognition. In addition, we introduce a new dataset that allows supervised training of deep learning network models. First, we examine whether the virtual persons were similar enough to persons in the real world. Second, we examine to what extent video gaming data can be used to identify violent scenarios in the real world. Our results show that virtual persons are just as realistic as persons in the real world. Moreover, our research shows how a serious-gaming approach can be used to identify violent scenarios with an average accuracy 15\% higher than 3 well-known datasets from real-world scenario.

The Changing Violence dataset contains violent crime reports from eleven sweeps of Crime Survey for England and Wales data from 2006/7 to 2016/17. This has merged the victimisation reports dataset and the main dataset (containing demographic information) from the Crime Survey for England and Wales. The two datasets contain the separated demographic information for all respondents and the crime reports for those who are victims of violent crimes (including threats of violence). The Changing Violence dataset was constructed as part of a wider research project into trends in violent crime over time. 23547 records subset of violent victim forms. Offence codes 11,12,13,21,31,32,33,34,35,91,92,93,94

This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

The data contained in this study deal with incidents of political and social violence in the United States from 1819 to 1968. Three indices of political violence used are the number of violent events, the number of deaths resulting from the events, and the number of injuries resulting from the events. Data are provided on the date of the violent event, nature of the target, number of attackers, level of violence by individual attacker and by a group of attackers, respectively, motivation or reason for the attack, numbers of deaths and injuries to targeted individuals and to attackers, type of attacker, property damage, and number of pages in newspaper issue devoted to the event. The data were originally collected in connection with the National Commission on the Causes and Prevention of Violence (established in 1968).

A critical mass of information and specialised knowledge on violence against women costing techniques has emerged within the Asia-Pacific region. This report highlights selected regional research and findings. This report is limited to discussion of costing work undertaken in the region which addresses response services only. The report catalogues and elucidate the past and current efforts to cost violence against women in Asia and the Pacific and highlights the challenges and key lessons we have come across. The violence against women costing efforts highlighted in the report not only aim to help understand the impact of violence against women, but ultimately facilitate a closing of the implementation and accountability gap by determining what financial resources are needed for governments to realise the commitments they have made. The report contains an overview of costing violence against women and girls and examines costing methodologies using case study examples.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

This table regroups a series of indicators related to violence against women collected from various sources (national surveys, international databases).

Find more Pacific data on PDH.stat.

Department of State Hospitals (DSH)-wide Violence Data Annual Rates of Assault from 2010-2020 for the following groups: Patient Assault (A2), Staff Assault (A4).

A2 - Patient physical assaults are committed by another patient. Formally defined as “Aggressive Act to Another Patient - Physical: Hitting, pushing, kicking or similar acts directed against another individual to cause potential or actual injury.” This does not include verbal assault, which is coded as “A1.”

A4 – Staff physical assaults are committed by a patient. Formally defined as “Aggressive Act to Staff - Physical: Hitting, pushing, kicking, or similar acts directed against a staff person that could cause potential or actual injury.” This does not include verbal assault, which is coded as “A3.”

Please Note:

1.Please note that it is an update to the previously published dataset with additional datasets.

2.Violence Rates value (in previous publication) can be calculated as a number per 1000 Patient Days. This number is easily interpreted and enables more accurate comparisons across time.

3.Prior to January 1, 2016 DSH-Atascadero coded an assault as Patient on Staff (A4) only when physical contact was made between patient and staff. All other Department of State Hospitals (DSH)- facilities code an assault as Patient on Staff (A4) either when physical contact was made or when physical contact was attempted. On January 1, 2016 Department of State Hospitals (DSH)--Atascadero began coding assaults in the same manner as all other Department of State Hospitals (DSH)- facilities.

4.Prior to January 1, 2016 Violence incidents were not captured specifically as Physical Contact made or Physical Contact Attempted.

A comprehensive list of data sources relating to violence against women and girls, bringing together a range of different sources from across government, academia and the voluntary sector.

The FRA survey on violence against women is based on face-to-face interviews with 42,000 women across the EU. The survey was carried out between March and September 2012 and presents the most comprehensive survey worldwide on women’s experiences of violence. The survey asked women about their experiences of physical, sexual and psychological violence, including domestic violence, since the age of 15 and over the 12 months before the interview. Questions were also asked about incidents of stalking, sexual harassment, and the role played by new technologies in women’s experiences of abuse. In addition, the survey asked about respondents’ experiences of violence in childhood.

The dataset of the FRA violence against women survey is stored with the UK Data Service, which is a recognised international service that is widely used by governmental and non-governmental institutions that produce survey data. The dataset is available free of charge after registration with the service under a Special Licence in various formats. Please visit the page of the dataset on the UK Data Service website to find a description of the dataset and the accompanying documents.

Workplace Violence Statistics (Editor's Choice)

• 45% of employees in the United States are aware that their employers have workplace violence prevention initiatives in place as of January 2022.
• Nearly 41% of registered nurses have experienced incivility, bullying, or other forms of workplace violence.
• 28% of workers know about workplace violence incidents within their companies.
• Businesses suffer annual losses of $250–330 billion due to workplace violence.
• Hospitals lose $53.7 million a year due to attacks on medical personnel.
• According to workplace violence stats, nearly 251 fatal work-related injuries happen because of violence.
• More than 30% of nurses reported increased attacks against them as of Q4 of 2021.
• Kicking, hitting, pushing, or beating have been the cause of 7.7% of workplace fatalities.
• Approximately 47% of ER doctors report a physical assault.
• Approximately 4.7% of neurosurgeons have experienced at least one physical assault at the workplace in the entire country.
• Around 23.3% of workers claim they have been bullied through email.
• Approximately 44% of school teachers report workplace attacks.
• The Oklahoma postal service massacre in 1986 was the second most deadly workplace shooting by a total number of victims in United States history.
• According to the most recent stats, nearly 56,034 officers have been attacked at the workplace.
• In Wales and England, approximately 307,000 adults report experiencing violence at the workplace.
• 43% of women in Taiwan report being sexually harassed at work.
• In 2023, the United States of America observed 13 workplace shootings out of which 8 were injured and 5 experienced fatalities.
• 85% of the death ratio in workplace violence has been caused by robbery in the United States of America.
• Every year, in the United States of America, 400,000 aggravated assaults take place in the workplace.
• 68% of employees do not feel safe at their workplaces.
• Every year in the United States of America, 2 million employees are injured because of various reasons including intentional violence, handling, lifting, and carrying accidents, slips, trips, falls, and being struck or caught in machinery.

General Workplace Violence Statistics

#1. 45% of employees in the United States are aware that their employers have workplace violence prevention initiatives in place as of January 2022.

According to statistics on workplace violence for 2022, more than half of all US workers are unaware of their employer's safety plan or violence prevention. Due to the ambiguity of workplace violence policies, up to 24% of employees claim they are unsure if they even exist. These policies cover things like shootings, fire emergencies, and medical emergencies. (Source: SHRM, Zippia)

#2. Nearly 41% of registered nurses have experienced incivility, bullying, or other forms of workplace violence.

An analysis shows the safety issues nurses face every day across the country. While 27% of nurses have been victims of workplace violence, 10% of nurses believe their organization has addressed the problem well. Unfortunately, 63% of the nurses claim that the organization did not respond well. (Source: AMN Healthcare)

http://www.enterpriseappstoday.com/wp-content/uploads/2022/09/Workplace-Violence-in-Healthcare--1024x576.jpg" alt="Workplace Violence Statistics" width="1024" height="576">

#3. 28% of workers know about workplace violence incidents within their companies.

More than one-fourth of the workers polled know that occupational violence happens in the workplace. Around 20% of workers claim to have seen workplace violence between colleagues, and 8% claim to have engaged in hostile interactions. Additionally, 19% of workers are unsure if workplace violence exists among their coworkers. (Source: SHRM)

#4. The number of sexual assaults at work that women report each year is 30,000.

Tragically, there is nothing new about violence against women. Most females endure attacks while working, too, from offensive comments to straight-up rape. Also, remember that the number of reported attacks is 30, 000. We do not even want to guess how many cases go undetected.

The best solution is to run a background check on your coworkers to prevent similar instances. You shouldn't mingle with sexual predators. There is more, though. According to statistics on workplace sexual assault, the coronavirus seems to be making most things worse. (Source: What to Become)

#5. Businesses suffer annual losses of $250–330 billion due to workplace violence.

One research shows that work-related violence is a significant problem for millions of employees in the United States. Sadly, around 25% of workers remain silent about the issue. This results in employees failing to address this issue. Their businesses suffer significant losses due to absenteeism, lawsuits, injuries, and decreased productivity. Furthermore, a damaged reputation also results in a decline in clients. (Source: Forbes)

#6. Hospitals lose $53.7 million a year due to attacks on medical personnel.

According to statistics on workplace violence in the healthcare industry, doctors spend around 112.8 hours away from the hospital due to violence committed on the job. The employees who must cover shifts and deal with hostile patients get more stressed. As a result, healthcare employees experience significant job dissatisfaction, burnout, depression, and yearly losses of $53.7 million. (Source: 911Celluar)

#7. According to workplace violence stats, nearly 251 fatal work-related injuries happen because of violence.

The information comes from a recent poll of adults 55 and older. In addition, workplace violence by humans or animals is the 4th most typical reason for fatal work injuries in this age group. (Source: US Bureau of Labor Statistics)

#8. More than 30% of nurses reported increased attacks against them as of Q4 of 2021.

These statistics on nursing injuries are somewhat alarming. They show that as of September 2021, the main reason for the rise in violence against nurses was tight politics around vaccines. According to 31% of nurses—up from 22% in March 2021—patients are more violent toward nurses due to their close contact and the prolonged time they spend together. (Source: Business Insider)

#9. According to workplace violence stats, female employees are 5 times more likely to be slain by a family member or domestic partner within the workplace than men employees.

The 3rd most common reason for job fatalities is workplace violence. According to studies, homicide accounts for 35% of deaths of women at work. However, females are more at risk than males. (Source: AFL-CIO, JRank)

#10. Each year, nearly 1.5 million workplace attacks are reported.

According to national statistics, many workplace violence incidents are not reported. Of the total, 396,000 are aggravated assaults. 51,000 of those are sexual assaults or rapes. Annually, there are approximately 84,000 robberies as well as 1,000 homicides. (Source: The Balance Careers)

#11. Around 20% of nurses are dealing with increased violence in the workplace.

These findings come from a recent survey of 15,000 registered nurses across the country. The nurses reported a higher level of violence within the workplace during the COVID-19 pandemic, and they stated most of the verbal and physical violence came from patients. (Source: Healthcare Finance News)

#12. One in seven workers expresses a lack of safety at work.

Can you imagine going to work and being afraid to arrive at the office? Even I am unable. But for some people, that is the truth. And as you would have guessed, being anxious a lot results in being less productive. (Source: Legal Jobs)

#13. Head injuries occur in 35% of workplace violence events.

Although attackers frequently target the head, statistics on workplace violence also demonstrate that:

• 14% had an impact on the trunk.
• 7% were targeted at the lower body.
• Upper body injuries account for 21% of all injuries.

(Source: Legal Jobs)

#14. Approximately 47% of ER doctors report a physical assault.

The ER is one of the most frequent healthcare workplaces experiencing violence. Out of 3,500 ER doctors, nearly 97% claim that patients are the primary source of assault. Additionally, patients threatened to harm 83% of doctors. (Source: Marketing General Incorporated)

#15. Nearly 43.10% of homicides happen in the workplace during robberies.

According to research, robbers are most likely to commit workplace homicide. Additionally, coworkers come in second with roughly 25.93%, and consumers come in third with nearly 17.17%. Finally, about 9.43% of incidents of workplace violence are caused by family members or domestic partners. (Source: Zippia)

<img class="size-full wp-image-27437 aligncenter" src="http://www.enterpriseappstoday.com/wp-content/uploads/2022/09/P4cHk-who-is-most-likely-to-commit-murder-in-the-workplace-.png"

This study contains data on the attitudes of 1,374 American men aged 16-64 toward violence in 1969. The study was undertaken to examine the levels of violence that can be viewed as justified to bring about social control or social change. Also emphasized were the role of the respondents' personal values, their definitions of violence, and their identification with the groups involved in violence. Some of the open-ended questions in the structured interview probed the respondents' general concerns, their attitudes toward violence, and their views on the causes of and ways of preventing violence. In questions grouped into categories of "violence for social control" and "violence for social change", respondents were asked to react to situations involving protests and other disturbances such as hoodlum gang disturbances, students' protests, and Black protest demonstrations. Repondents' opinions were sought on the appropriate police actions in these situations and the frequency with which certain control measures should be utilized. Respondents were also asked in three different situations whether they believed change could be effected without action involving property damage or injury, or if change could only be effected with protests in which some people were killed. Demographic variables describe age, sex, date of birth, nationality, occupation, education, religion, and family income. A supplementary sample of Black men is also included in this study in order to permit separate analysis on the basis of race.

This dataset contains individual-level homicide and non-fatal shooting victimizations, including homicide data from 1991 to the present, and non-fatal shooting data from 2010 to the present (2010 is the earliest available year for shooting data). This dataset includes a "GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized-access dataset, but with "UNKNOWN" in the shooting column.

Each row represents a single victimization, i.e., a unique event when an individual became the victim of a homicide or non-fatal shooting. Each row does not represent a unique victim—if someone is victimized multiple times there will be multiple rows for each of those distinct events.

The dataset is refreshed daily, but excludes the most recent complete day to allow the Chicago Police Department (CPD) time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

A version of this dataset with additional crime types is available by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Violence Reduction Victims Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset. For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.” Officer-involved shootings are not included.

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Note: In some instances, CPD's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most reliable crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: Homicide victims names are delayed by two weeks to allow time for the victim’s family to be notified of their passing.

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Note: This dataset includes variables referencing administrative or political boundaries that are subject to change. These include Street Outreach Organization boundary, Ward, Chicago Police Department District, Chicago Police Department Area, Chicago Police Department Beat, Illinois State Senate District, and Illinois State House of Representatives District. These variables reflect current geographic boundaries as of November 1st, 2021. In some instances, current boundaries may conflict with those that were in place at the time that a given incident occurred in prior years. For example, the Chicago Police Department districts 021 and 013 no longer exist. Any historical violent crime victimization that occurred in those districts when they were in existence are marked in this dataset as having occurred in the current districts that expanded to replace 013 and 021."

The International Dating Violence Study (IDVS) was conducted by a consortium of researchers in 32 nations. It includes data on both perpetration and being a victim of violence. The data were obtained using questionnaires completed by university students in all major world regions. The term "violence" refers to maltreatment of a partner, including physical assault, injury as a result of assault by a partner, psychological aggression, and sexual coercion. The questionnaires, although completed by one person, include data on the behavior of both partners as reported by the student who completed questionnaire. The study questionnaire includes two scales, the Conflict Tactics Scales or CTS (Straus, 1996) to obtain data on violence between the respondent and his or her partner, and the Personal And Relationships Profile (PRP) to obtain data on 25 risk factors for partner violence and a scale to measure "socially desirable" response bias (Straus, Hamby, Boney-McCoy, and Sugarman, 2010). Using the CTS, the respondents were queried about personal and social relationships. This included emotional attachments to partners, parents, and family. They were then asked about conflicts with and opinions of their partner. In addition, they were asked whether or not they attended religious services. Respondents were also queried about conflict with, and anger toward, their partners. Questions included whether the respondent could control his or her anger, how they coped with it, and if they assigned blame for becoming angry to their partner. Further questions focused on communication, including disagreements about relationships with others and with partners. Respondents were further asked if they experienced jealousy and exhibited controlling behavior toward their partner. They were then asked about their personal beliefs and attitudes toward others, including how they interact with people. Respondents were asked about their life satisfaction and emotional state, including whether they have had mood swings, as well as feelings of emptiness and/or depression. Suicidal thoughts or statements were also included in the questions. Respondents were queried about their experiences with fear of past events and whether those experiences still affected their life. Another focus of the CTS was violence and criminal behavior. Respondents were asked about whether they witnessed violence between others, including those within their own families. They were asked about violence they had experienced, their attitudes and beliefs toward violence, violent influences when growing up, and their personal past violent and/or criminal behavior. Another focus of the CTS was sexual abuse. Respondents were queried about sexual abuse experienced in their childhood as well as adulthood, whether that abuse was committed by a family member or within an adult relationship. They were then asked about their attitudes toward the opposite sex and opinions on sexual crime. Another topic included drugs and alcohol. Respondents were asked if they used drugs and alcohol, and whether their level of use was significant enough to endanger their health. The second major instrument in the study, the Personal and Relationships Profile (PRP), examined interpersonal interaction with the partner of the respondent. The scale included items the partner did to the respondent or the respondent did to their partner, as well as the frequency of those incidents over the past year. Items included physical violence such as throwing objects, pushing or shoving, use of weapons, slapping, burning or scalding, and other types of physical assault. Questions regarding verbal abuse were also included, such as name-calling, accusations, and threats. Other communication related questions were also included, such as compromising to reach a solution and respecting the other's opinion. Sexual abuse was another focus of the PRP. Respondents were asked if they used threats, coercion, or force to make their partner have sex, or if their partner did this to the respondent. The data is available in three parts. The first part, the Individual-level dataset, provides data for each respondent. The second part, the Nation-level dataset, was aggregated to create data files in which the cases are the 32 nations where IDVS data was gathered. The third part, the Gender-level dataset, divided respondents for analysis by s

US Organic Damage from Gun Violence - Statistics from 2014 to May 2021