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.

In recent years, gun violence in the United States has become an alarmingly common occurrence. From 2016, there has been over ****** homicides by firearm in the U.S. each year and firearms have been found to make up the majority of murder weapons in the country by far, demonstrating increasing rates of gun violence occurring throughout the nation. As of 2025, Mississippi was the state with the highest gun violence rate per 100,000 residents in the United States, at **** percent, followed by Louisiana, at **** percent. In comparison, Massachusetts had a gun violence rate of *** percent, the lowest out of all the states. The importance of gun laws Gun laws in the United States vary from state to state, which has been found to affect the differing rates of gun violence throughout the country. Fewer people die by gun violence in states where gun safety laws have been passed, while gun violence rates remain high in states where gun usage is easily permitted and even encouraged. In addition, some states suffer from high rates of gun violence despite having strong gun safety laws due to gun trafficking, as traffickers can distribute firearms illegally past state lines. The right to bear arms Despite evidence from other countries demonstrating that strict gun control measures reduce rates of gun violence, the United States has remained reluctant to enact gun control laws. This can largely be attributed to the Second Amendment of the Constitution, which states that citizens have the right to bear arms. Consequently, gun control has become a highly partisan issue in the U.S., with ** percent of Democrats believing that it was more important to limit gun ownership while ** percent of Republicans felt that it was more important to protect the right of Americans to own guns.

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).

These data were prepared in conjunction with a project using Bureau of Labor Statistics data (not provided with this collection) and the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data to examine the relationship between unemployment and violent crime. Three separate time-series data files were created as part of this project: a national time series (Part 1), a state time series (Part 2), and a time series of data for 12 selected cities: Baltimore, Buffalo, Chicago, Columbus, Detroit, Houston, Los Angeles, Newark, New York City, Paterson (New Jersey), and Philadelphia (Part 3). Each data file was constructed to include 82 monthly time series: 26 series containing the number of Part I (crime index) offenses known to police (excluding arson) by weapon used, 26 series of the number of offenses cleared by arrest or other exceptional means by weapon used in the offense, 26 series of the number of offenses cleared by arrest or other exceptional means for persons under 18 years of age by weapon used in the offense, a population estimate series, and three date indicator series. For the national and state data, agencies from the 50 states and Washington, DC, were included in the aggregated data file if they reported at least one month of information during the year. In addition, agencies that did not report their own data (and thus had no monthly observations on crime or arrests) were included to make the aggregated population estimate as close to Census estimates as possible. For the city time series, law enforcement agencies with jurisdiction over the 12 central cities were identified and the monthly data were extracted from each UCR annual file for each of the 12 agencies. The national time-series file contains 82 time series, the state file contains 4,083 time series, and the city file contains 963 time series, each with 228 monthly observations per time series. The unit of analysis is the month of observation. Monthly crime and clearance totals are provided for homicide, negligent manslaughter, total rape, forcible rape, attempted forcible rape, total robbery, firearm robbery, knife/cutting instrument robbery, other dangerous weapon robbery, strong-arm robbery, total assault, firearm assault, knife/cutting instrument assault, other dangerous weapon assault, simple nonaggravated assault, assaults with hands/fists/feet, total burglary, burglary with forcible entry, unlawful entry-no force, attempted forcible entry, larceny-theft, motor vehicle theft, auto theft, truck and bus theft, other vehicle theft, and grand total of all actual offenses.

Crime Investigation

## Overview

Crime Investigation is a dataset for object detection tasks - it contains Blood,fake Face,gun,knife,real Face annotations for 583 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).

📄 Dataset Description This dataset contains reported crime incidents across various regions in India, structured to enable exploratory analysis and machine learning applications. It includes multiple features representing crime types, locations, timelines, and related attributes. The dataset is suitable for classification tasks (e.g., predicting the type of crime) and visual pattern analysis.

🔍 Use Cases Crime type prediction using machine learning models

Analysis of crime trends by region, type, or time

Understanding feature importance in crime pattern detection

Training classification or clustering algorithms for real-world safety applications

📌 Column Descriptors | Column Name | Description | | ------------------- | ---------------------------------------------------------------------- | | Date | Date when the crime was reported or occurred | | State/UT | State or Union Territory where the crime took place | | District | District within the state where the incident occurred | | Crime_Type | Category of the crime (e.g., Theft, Assault, Cyber Crime, etc.) | | Victim_Age | Age of the victim (if available) | | Victim_Gender | Gender of the victim (Male, Female, or Other) | | Weapon_Used | Type of weapon used (if any), such as Knife, Gun, or None | | Location | General description of the location (e.g., public place, home) | | Time_of_Day | Time bucket (e.g., Morning, Evening, Night) when the incident occurred | | Reporting_Agency | Police station or agency that registered the complaint | | Status | Status of the case (e.g., Open, Closed, Under Investigation) | | Crime_Severity | Ordinal indicator of severity (e.g., Low, Medium, High) | | Target or Label | Target variable used for classification — e.g., crime category |

This table contains the number of persons died as a result of murder or manslaughter, where the crime scene is located in the Netherlands. The victims can be residents or non-residents of the Netherlands. The data can be split by location of the crime, method, age and sex. The criterion is the date of death, the date of the criminal act can be in the previous year. Since 2013 Statistics Netherlands is using Iris for automatic coding for causes of death. This improved the international comparison of the data. The change in coding did cause a considerable shift in the statistics. Since 2013 the (yearly) ICD-10 updates are applied. However for murder and manslaughter no changes in coding have taken place. The ICD-10 codes that belong to murder and manslaughter are X85-Y09.

Data available from: 1996

Status of the figures: The figures up to and including 2023 are final, the figures for 2024 are provisional.

Changes as of August 28th 2025: The provisional figures for 2024 are added.

When will new figures be published: In the first quarter of 2026 the final figures for 2024 will be published.

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).

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.

Raw data on crime supplied by the Metropolitan Police Service and the Mayors Office for Policing and Crime (MOPAC). Pan-London data includes: - Total Notifiable Offences - Total Victim-based crime (and Sanctioned Detection Rates) - Violence against the Person - Violence with injury (VWI) (and SDR) - Serious Youth Violence - Female victims of robbery & Violence with Injury - Rape - 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 - Racist & Religious Hate Victims - Faith Hate Victims - Disability Hate Victims - Stop & Search Totals (and related Arrest rate) - Police Strengths - Officer/Sergeant/Staff/Special Constable/PCSO - Satisfaction/Confidence in the Metropolitan Police Service (ease of contact/satisfaction with action taken/well-informed/fairly treated/overall satisfaction/overall confidence) NB. Quarterly data - Crime-related calls to Police by category - Anti-Social Behaviour-related calls to Police by category Borough data includes: - Fear of crime ("to what extent are you worried about crime in this area?") NB. Quarterly data NB. Action Fraud have taken over the recording of fraud offences nationally on behalf of individual police forces. This process began in April 2011 and was rolled out to all police forces by March 2013. Data for Greater London is available from Action Fraud here.

The data contained within is governed by an Open Government Licence (OGL). link

Introduction

The history of knife carrying in England is a complex issue interwoven with societal changes, economic conditions, and evolving cultural attitudes. While knives have been tools for millennia, their perception and regulation as potential weapons has fluctuated dramatically over the past century. I will delve into the factors influencing knife carrying, the legislative responses, the socio-cultural implications and visualising the data from the Home Office.

The 20th Century: A Shift in Perception

• Early 20th Century: A Tool, Not a Weapon
  • Knives were primarily utilitarian objects, used in agriculture, trades, and domestic life.
  • Carrying a knife was commonplace, particularly in rural areas.
  • Legal restrictions were minimal, reflecting the societal norm of knives as essential tools.
• Post-World War II: Urbanisation and Social Change
  • The rapid growth of urban centres and industrialisation led to a shift in societal structure.
  • The perception of knives began to evolve. While still essential tools for many, they were increasingly associated with urban crime and violence.
  • The emergence of youth subcultures and gang-related activities contributed to this changing image.
• Late 20th Century: The Rise of Knife Crime
  • A surge in knife-related violence gained media attention, fuelling public fear and concern.
  • Government and law enforcement responded with stricter regulations and increased policing.
  • The introduction of the Criminal Justice Act 1988 marked a significant turning point, with harsher penalties for knife possession and use. ###The 21st Century: A Complex Issue
• Early 21st Century: Intensified Focus
  • Knife crime remained a persistent issue, leading to further legislative measures.
  • The Anti-social Behaviour, Crime and Policing Act 2014 introduced stricter penalties for carrying knives.
  • Campaigns targeting young people were launched to deter knife carrying.
• Factors Influencing Knife Carrying
  • Risk factors associated with knife-crime in the United Kingdom among young people aged 10–24 years: a systematic review. link
  • Knife Crime - Home Affairs Committee (Parliament). link
  • Socio-economic conditions: Poverty, unemployment, and social deprivation have been linked to higher rates of knife crime.
  • Gang culture: Gang-related violence often involves knives as weapons of choice.
  • Media portrayal: The media's coverage of knife crime can influence public perception and behaviour.
  • Peer pressure: Young people may carry knives for protection or status.
• Challenges and Controversies
  • Balancing public safety with individual rights: Restrictions on knife carrying have raised concerns about infringing on the rights of law-abiding citizens.
  • Effectiveness of legislation: The impact of stricter laws on reducing knife crime is debated.
  • Root causes: Addressing the underlying social and economic factors contributing to knife crime is complex and challenging.
• Laws about knives
  • In the UK, it is illegal to sell knives to anyone under the age of 18, as per the Criminal Justice Act 1988. This includes knife blades, razor blades, axes, and other sharply pointed items that could cause injury. The police and Trading Standards enforce this law.
  • It is illegal to carry any knife in public without a good purpose, carrying a sentence of up to 4 years in prison.
  • In 2016, the government banned zombie knives, whilst cyclone knives were banned in 2019.
  • A new law to ban zombie-style knives and machetes, will come into force on Tuesday, September 24, 2024. The law will make it illegal to own knives over 8 inches that have any two of the following three characteristics: A fine edge, A serrated edge, and Two or more holes in the blade. link
  • The Criminal Justice Bill will go further by increasing the maximum sentence for the possession of banned weapons from 6 months to 2 years, while anyone caught selling knives to under-18s, including online, will also face 2 years behind bars.
  • Police will also be given new powers to seize and destroy knives found on private premises if there are reasonable grounds to suspect the blade will be used in a serious crime. Previously, police could not seize knives found during a search on a property, even if they had suspic...