Gun Knife Thesis

## Overview

Gun Knife Thesis is a dataset for object detection tasks - it contains Guns Knives annotations for 9,918 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Number and percentage of homicide victims, by type of firearm used to commit the homicide (total firearms; handgun; rifle or shotgun; fully automatic firearm; sawed-off rifle or shotgun; firearm-like weapons; other firearms, type unknown), Canada, 1974 to 2018.

Number of homicide victims, by method used to commit the homicide (total methods used; shooting; stabbing; beating; strangulation; fire (burns or suffocation); other methods used; methods used unknown), Canada, 1974 to 2024.

Gun Knife Stick Detection

## Overview

Gun Knife Stick Detection is a dataset for object detection tasks - it contains Knife Pistol Rifle Stick annotations for 9,007 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Violence And Weapons Detection 2

## Overview

Violence And Weapons Detection 2 is a dataset for object detection tasks - it contains Violence NonViolence Violence NonViolence Knife Gun JiWK annotations for 9,974 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This study of violent incidents among middle- and high-school students focused not only on the types and frequency of these incidents, but also on their dynamics -- the locations, the opening moves, the relationship between the disputants, the goals and justifications of the aggressor, the role of third parties, and other factors. For this study, violence was defined as an act carried out with the intention, or perceived intention, of physically injuring another person, and the "opening move" was defined as the action of a respondent, antagonist, or third party that was viewed as beginning the violent incident. Data were obtained from interviews with 70 boys and 40 girls who attended public schools with populations that had high rates of violence. About half of the students came from a middle school in an economically disadvantaged African-American section of a large southern city. The neighborhood the school served, which included a public housing project, had some of the country's highest rates of reported violent crime. The other half of the sample were volunteers from an alternative high school attended by students who had committed serious violations of school rules, largely involving illegal drugs, possession of handguns, or fighting. Many students in this high school, which is located in a large city in the southern part of the Midwest, came from high-crime areas, including public housing communities. The interviews were open-ended, with the students encouraged to speak at length about any violent incidents in school, at home, or in the neighborhood in which they had been involved. The 110 interviews yielded 250 incidents and are presented as text files, Parts 3 and 4. The interview transcriptions were then reduced to a quantitative database with the incident as the unit of analysis (Part 1). Incidents were diagrammed, and events in each sequence were coded and grouped to show the typical patterns and sub-patterns in the interactions. Explanations the students offered for the violent-incident behavior were grouped into two categories: (1) "justifications," in which the young people accepted responsibility for their violent actions but denied that the actions were wrong, and (2) "excuses," in which the young people admitted the act was wrong but denied responsibility. Every case in the incident database had at least one physical indicator of force or violence. The respondent-level file (Part 2) was created from the incident-level file using the AGGREGATE procedure in SPSS. Variables in Part 1 include the sex, grade, and age of the respondent, the sex and estimated age of the antagonist, the relationship between respondent and antagonist, the nature and _location of the opening move, the respondent's response to the opening move, persons present during the incident, the respondent's emotions during the incident, the person who ended the fight, punishments imposed due to the incident, whether the respondent was arrested, and the duration of the incident. Additional items cover the number of times during the incident that something was thrown, the respondent was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, cut, or bruised, was threatened with a knife or gun, or a knife or gun was used on the respondent. Variables in Part 2 include the respondent's age, gender, race, and grade at the time of the interview, the number of incidents per respondent, if the respondent was an armed robber or a victim of an armed robbery, and whether the respondent had something thrown at him/her, was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, was threatened with a knife or gun, or had a knife or gun used on him/her.

GTA Crime

## Overview

GTA Crime is a dataset for object detection tasks - it contains Crime annotations for 2,270 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ATM Crime Detection

## Overview

ATM Crime Detection is a dataset for object detection tasks - it contains Gun Knife annotations for 6,044 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Addressing the under-researched issue of weapon tolerance, the paper examines factors behind male knife and gun tolerance across four different cultures, seeking to rank them in terms of predictive power and shed light on relations between them. To this end, four regression and structural equation modelling analyses were conducted using samples from the US (n = 189), India (n = 196), England (n = 107) and Poland (n = 375). Each sample of male participants indicated their standing on several dimensions (i.e., predictors) derived from theory and related research (i.e., Psychoticism, Need for Respect, Aggressive Masculinity, Belief in Social Mobility and Doubt in Authority). All four regression models were statistically significant. The knife tolerance predictors were: Aggressive Masculinity (positive) in the US, Poland and England, Belief in Social Mobility (negative) in the US and England, Need for Respect (positive) in India and Psychoticism (positive) in Poland. The gun tolerance predictors were: Psychoticism (positive) in the US, India and Poland, Aggressive Masculinity (positive) in the US, England and Poland, and Belief in in Social Mobility (negative) in the US, Belief in Social Mobility (positive) and Doubt in Authority (negative) in Poland. The Structural Equation Weapon Tolerance Model (WTM) suggested an indirect effect for the latent factor Perceived Social Ecological Constraints via its positive relation with the latent factor Saving Face, both knife and gun tolerance were predicted by Psychoticism.

(StatCan Product) Customization details: This information product has been customized to present information on victims of spousal violence and self-reported spousal violence by relationship, type of violence, age group, marital status, family type, household income, education and place of residence for Alberta. Status of spousal relationship within the past 12 months and 5 years: - Current relationship - Previous relationship - Current and previous relationship Types of violence: - Threatened to hit, thrown anything - Pushed, grabbed, shoved or slapped - Kicked, bit, hit. Hit with something that could hurt - Beaten, choked, threatened / used gun or knife or forced into sexual activity Age groups: -15 to 24 - 25 to 34 - 35 to 44 - 45 and older Marital statuses: - Married - Common-law Family types: - Intact - Blended - Couple without children - Lone parent Household incomes: - Less than $30,000 - $30,000 to $59,999 - $60,000 or more - Not stated / don't know Education of victim or of spouse/partner: - High School diploma or less - Some post secondary - University degree Victim's place of residence: - Census Metropolitan Area - Non Census Metropolitan Area Service contacted: - Told informal sources - Contacted or used informal sources - Police found out about the incident - Respondent reported to the police - Police found out some other way General Social Survey: An Overview, 2009

• Knife Crime (and SDR) - Knife Crime with Injury - Gun Crime (and SDR) - Gun Crime with firearm discharged - Gang violence indicator - Dog Attacks (and SDR) - Homicide - Sexual Offences - Burglary (all) - Burglary (residential) - Robbery (all) - Theft & Handling - Theft from Person* - Theft of Motor Vehicle* - Theft from Motor Vehicle* - Criminal Damage* - Domestic Offences - Homophobic Hate Victims

This study examined the implementation of a specialized domestic violence unit within the San Diego County Sheriff's Department to determine whether the creation of the new unit would lead to increased and improved reporting, and more filings for prosecution. In order to evaluate the implementation of the specialized domestic violence unit, the researchers conducted the following tasks: (1) They surveyed field deputies to assess their level of knowledge about domestic violence laws and adherence to the countywide domestic violence protocol. (2) They studied a sample from the case tracking system that reported cases of domestic violence handled by the domestic violence unit to determine changes in procedures compared to an earlier case tracking study with no specialized unit. (3) They interviewed victims of domestic violence by phone to explore the responsiveness of the field deputies and the unit detectives to the needs of the victims. Part 1 (Deputy Survey Data) contains data on unit detectives' knowledge about the laws concerning domestic violence. Information includes whether or not the person considered the primary aggressor was the person who committed the first act of aggression, if a law enforcement officer could decide whether or not to complete a domestic violence supplemental report, whether an arrest should be made if there was reasonable cause to believe that a misdemeanor offense had been committed, and whether the decision to prosecute a suspect lay within the discretion of the district or city attorney. Demographic variables include deputy's years of education and law enforcement experience. Part 2 (Case Tracking Data) includes demographic variables such as race and sex of the victim and the suspect, and the relationship between the victim and the suspect. Other information was collected on whether the victim and the suspect used alcohol and drugs prior to or during the incident, if the victim was pregnant, if children were present during the incident, highest charge on the incident report, if the reporting call was made at the same place the incident occurred, suspect actions described on the report, if a gun, knife, physical force, or verbal abuse was used in the incident, if the victim or the suspect was injured, and if medical treatment was provided to the victim. Data were also gathered on whether the suspect was arrested or booked, how the investigating officer decided whether to request that the prosecutor file charges, type of evidence collected, if a victim or witness statement was collected, if the victim had a restraining order, prior history of domestic violence, if the victim was provided with information on domestic violence law, hotline, shelter, transportation, and medical treatment, highest arrest charge, number of arrests for any drug charges, weapon charges, domestic violence charges, or other charges, case disposition, number of convictions for the charges, and number of prior arrests and convictions. Part 3 (Victim Survey Data) includes demographic variables such as victim's gender and race. Other variables include how much time the deputy spent at the scene when s/he responded to the call, number of deputies the victim interacted with at the scene, number of deputies at the scene that were male or female, if the victim used any of the information the deputy provided, if the victim used referral information for counseling, legal, shelter, and other services, how helpful the victim found the information, and the victim's rating of the performance of the deputy.