This study was undertaken to obtain information on the characteristics of gun ownership, gun-carrying practices, and weapons-related incidents in the United States -- specifically, gun use and other weapons used in self-defense against humans and animals. Data were gathered using a national random-digit-dial telephone survey. The respondents were comprised of 1,905 randomly-selected adults aged 18 and older living in the 50 United States. All interviews were completed between May 28 and July 2, 1996. The sample was designed to be a representative sample of households, not of individuals, so researchers did not interview more than one adult from each household. To start the interview, six qualifying questions were asked, dealing with (1) gun ownership, (2) gun-carrying practices, (3) gun display against the respondent, (4) gun use in self-defense against animals, (5) gun use in self-defense against people, and (6) other weapons used in self-defense. A "yes" response to a qualifying question led to a series of additional questions on the same topic as the qualifying question. Part 1, Survey Data, contains the coded data obtained during the interviews, and Part 2, Open-Ended-Verbatim Responses, consists of the answers to open-ended questions provided by the respondents. Information collected for Part 1 covers how many firearms were owned by household members, types of firearms owned (handguns, revolvers, pistols, fully automatic weapons, and assault weapons), whether the respondent personally owned a gun, reasons for owning a gun, type of gun carried, whether the gun was ever kept loaded, kept concealed, used for personal protection, or used for work, and whether the respondent had a permit to carry the gun. Additional questions focused on incidents in which a gun was displayed in a hostile manner against the respondent, including the number of times such an incident took place, the location of the event in which the gun was displayed against the respondent, whether the police were contacted, whether the individual displaying the gun was known to the respondent, whether the incident was a burglary, robbery, or other planned assault, and the number of shots fired during the incident. Variables concerning gun use by the respondent in self-defense against an animal include the number of times the respondent used a gun in this manner and whether the respondent was hunting at the time of the incident. Other variables in Part 1 deal with gun use in self-defense against people, such as the location of the event, if the other individual knew the respondent had a gun, the type of gun used, any injuries to the respondent or to the individual that required medical attention or hospitalization, whether the incident was reported to the police, whether there were any arrests, whether other weapons were used in self-defense, the type of other weapon used, location of the incident in which the other weapon was used, and whether the respondent was working as a police officer or security guard or was in the military at the time of the event. Demographic variables in Part 1 include the gender, race, age, household income, and type of community (city, suburb, or rural) in which the respondent lived. Open-ended questions asked during the interview comprise the variables in Part 2. Responses include descriptions of where the respondent was when he or she displayed a gun (in self-defense or otherwise), specific reasons why the respondent displayed a gun, how the other individual reacted when the respondent displayed the gun, how the individual knew the respondent had a gun, whether the police were contacted for specific self-defense events, and if not, why not.

The share of American households owning at least one firearm has remained relatively steady since 1972, hovering between ** percent and ** percent. In 2024, about ** percent of U.S. households had at least one gun in their possession. Additional information on firearms in the United States Firearms command a higher degree of cultural significance in the United States than any other country in the world. Since the inclusion of the right to bear arms in the Second Amendment to the Constitution of the United States, firearms have held symbolic power beyond their already obvious material power. Despite many Americans being proud gun-owners, a large movement exists within the country in opposition to the freedom afforded to those in possession of these potentially deadly weapons. Those opposed to current gun regulation have sourced their anger from the large number of deaths due to firearms in the country, as well as the high frequency of gun violence apparent in comparison to other developed countries. Furthermore, the United States has fallen victim to a number of mass shootings in the last two decades, most of which have raised questions over the ease at which a person can obtain a firearm. Although this movement holds a significant position in the public political discourse of the United States, meaningful change regarding the legislation dictating the ownership of firearms has not occurred. Critics have pointed to the influence possessed by the National Rifle Association through their lobbying of public officials. The National Rifle Association also lobbies for the interests of firearm manufacturing in the United States, which has continued to rise since a fall in the early 2000s.

People with Guns Segmentation & Object Detection dataset

The dataset consists of photos depicting individuals holding guns. It specifically focuses on the segmentation of guns within these images and the detection of people holding guns.

Each image in the dataset presents a different scenario, capturing individuals from various backgrounds, genders, and age groups in different poses while holding guns.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

The dataset is an essential resource for the development and evaluation of computer vision models and algorithms in fields related to firearms recognition, security systems, law enforcement, and safety analysis.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2497edebcdd1b7c4bc5471262bf5bd16%2FFrame%2029.png?generation=1696334547549518&alt=media" alt="">

OTHER DATASETS WITH SEGMENTATION:

• Body Segmentation - 5,300 Photos
• Bald People Segmentation Dataset
• GENERATED USA Passports Segmentation
• Vehicle Images & Segmentation
• Agricultural Image Segmentation

🧩 This is just an example of the data. Leave a request here to learn more

Dataset structure

• images - contains of original images with people holding guns
• labels - includes visualized labeling created for the original images
• annotations.xml - contains coordinates of the polygons and bounding boxes, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes and polygons. For each point, the x and y coordinates are provided.

Сlasses:

• person: person, who holds the gun, detected with a bounding box,
• gun: gun, labeled with a polygon

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F96bbe14c80f4b494f97136f8ffdbaa44%2Fcarbon.png?generation=1696335385101390&alt=media" alt="">

People with Guns Segmentation & Detection might be made in accordance with your requirements.

🚀 You can learn more about our high-quality unique datasets here

keywords: body segmentation dataset, human segmentation dataset, human body segmentation, people images dataset, biometric data dataset, biometric dataset, object detection, public safety, gun detection dataset, weapon detection, pistols object detection, handgun, pistols in-hand, state firearm database, firearm safety, short guns, annotated gun, cropped gun chip, automatic weapon detection system, annotation, semantic segmentation, computer vision, deep learning, machine learning, image dataset, image classification, human images

This nationally representative, anonymous, household telephone survey was conducted to explore the distribution of privately owned firearms in the United States, as well as firearm acquisition, disposal, and storage in households with guns. The study updates an earlier (1994) study by Cook and Ludwig that examined household firearm ownership in the United States (Cook P.J., Ludwig J. Guns in America: Results of a comprehensive national survey of firearms ownership and use. Washington DC: Police Foundation 1997.) Other domains of the survey included (1) past year firearm use both by respondents with firearms in their households and those without (e.g., "In the past 12 months, have you handled any gun"); (2) guns and youth (e.g., "In the last 12 months, have you ever asked another parent whether their home contains guns?"); (3) awareness of and opinions regarding state and federal firearm laws (e.g., "To the best of your knowledge, does your state have a law that holds adults liable for misuse of their guns by children or minors"; "Do you favor or oppose the sale of military style firearms?"); (4) depression and suicide (e.g., "If the Golden Gate Bridge had a barrier to prevent suicide, about how many of the 1,000 jumpers (who have committed suicide by jumping off the bridge since 1937) do you think would have found some other way to kill themselves?") and (5) aggressive driving (e.g., "In the past 12 months, have you made obscene or rude gestures at another motorist"). The survey also included extensive demographic information about the respondent and his or her family. The demographic information that was collected includes respondents' sex, age, race, education level, household income, criminal arrest history, armed forces membership status, type of residential area (e.g., urban or rural), and political philosophy.

People With Gun

## Overview

People With Gun is a dataset for object detection tasks - it contains Gun annotations for 878 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).

Simuletic Weapon Detection Dataset

Overview

This is an open-source synthetic dataset for computer vision (CV) object detection tasks, focusing on people holding weapons in public areas viewed from CCTV camera perspectives. The dataset consists of high-quality, realistic synthetic images.

Key features:

Classes: "person" and "weapon" (e.g., guns, knives in various poses and scenarios). Annotations: Bounding boxes (rectangles) in YOLO format for easy integration with models like YOLOv8. Resolution: Mixed resolutions. Purpose: Designed for training and evaluating AI models in security, surveillance, and threat detection. Addresses data scarcity and privacy issues with synthetic alternatives. This dataset is a sample done by Simuletic. We are working on open sourced dataset to help with weapon and threat detection.

For custom scenarios, larger datasets, or videos, visit https://simuletic.com

Dataset Structure

images/: Folder containing .jpg or .png files (e.g., image001.jpg). labels/: Folder with YOLO .txt files (one per image, e.g., image001.txt). Each line: class_id center_x center_y width height (normalized 0-1). annotations.csv (optional): A CSV summary with columns like image_name, class, x_min, y_min, x_max, y_max for quick reference. Example YOLO label line: 0 0.45 0.55 0.20 0.30 # person 1 0.60 0.70 0.10 0.15 # weapon text##

Sample dataset.yaml (create in root): yamlpath: /path/to/dataset train: images val: images # Use same for small datasets names: 0: person 1: weapon

For Hugging Face integration: See our repo on Hugging Face.

Preprocess resolutions if needed (e.g., via OpenCV for resizing).

Ethics and Limitations

This is fully synthetic data—no real individuals or events are depicted. Intended for ethical use in research/security (e.g., improving detection models). Do not use for harmful purposes. Potential biases: Scenarios may not cover all real-world diversity; audit for fairness. For production, combine with real data and validate.

License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt it, provided you give appropriate credit to Simuletic. Citation If you use this dataset, please cite: text@dataset{simuletic_weapon_detection_2025, author = {Your Name / Simuletic Team}, title = {Simuletic Synthetic Weapon Detection Dataset}, year = {2025}, url = {https://github.com/yourusername/cctv-weapon-dataset} } Links

Huggingface: https://huggingface.co/datasets/Simuletic/cctv-weapon-dataset Github: Links coming Feedback? Reach out via https://simuletic.com or issues here.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gun Barrel City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gun Barrel City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gun Barrel City, the median household income stands at $84,878 for householders within the 45 to 64 years age group, followed by $73,449 for the 65 years and over age group. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $60,393.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Barrel City median household income by age. You can refer the same here

This project investigated the experiences of New York City youth ages 16-24 who were at high risk for gun violence (e.g., carried a gun, been shot or shot at). Youth participants were recruited from three neighborhoods with historically high rates of gun violence when compared to the city as a whole--Brownsville (Brooklyn), Morrisania (Bronx), and East Harlem (Manhattan). This study explores the complex confluence of individual, situational, and environmental factors that influence youth gun acquisition and use. This study is part of a broader effort to build an evidence-based foundation for individual and community interventions, and policies that will more effectively support these young people and prevent youth gun violence. Through interviews with 330 youth, this study seeks to answer these questions: What are the reasons young people carry guns? How do young people talk about having and using guns? What are young people's social networks like, and what roles do guns play in thesenetworks? Interviews covered the following topics: neighborhood perceptions; perceptions of and experiences with the police, gangs, guns, and violence; substance use; criminal history; and demographics: race, gender, age, legal status, relationship status, living situation, location, number of children, drug use, and education.

Weapons And People 2

## Overview

Weapons And People 2 is a dataset for object detection tasks - it contains Weapon People Fcfx annotations for 5,016 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).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gun Plain township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gun Plain township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gun Plain township, the median household income stands at $126,667 for householders within the 25 to 44 years age group, followed by $99,718 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $41,492.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Plain township median household income by age. You can refer the same here

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.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Gun Barrel City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 71(2.74%) households where the householder is under 25 years old, 510(19.66%) households with a householder aged between 25 and 44 years, 1,035(39.90%) households with a householder aged between 45 and 64 years, and 978(37.70%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Gun Barrel City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Barrel City median household income by age. You can refer the same here

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Context

Mass Shootings in the United States of America (1966-2017)

The US has witnessed 398 mass shootings in last 50 years that resulted in 1,996 deaths and 2,488 injured. The latest and the worst mass shooting of October 2, 2017 killed 58 and injured 515 so far. The number of people injured in this attack is more than the number of people injured in all mass shootings of 2015 and 2016 combined.

The average number of mass shootings per year is 7 for the last 50 years that would claim 39 lives and 48 injured per year.

Content

Geography: United States of America

Time period: 1966-2017

Unit of analysis: Mass Shooting Attack

Dataset: The dataset contains detailed information of 398 mass shootings in the United States of America that killed 1996 and injured 2488 people.

Variables: The dataset contains Serial No, Title, Location, Date, Summary, Fatalities, Injured, Total Victims, Mental Health Issue, Race, Gender, and Lat-Long information.

Acknowledgements

I’ve consulted several public datasets and web pages to compile this data.

Some of the major data sources include Wikipedia, Mother Jones, Stanford, USA Today and other web sources.

Inspiration

With a broken heart, I like to call the attention of my fellow Kagglers to use Machine Learning and Data Sciences to help me explore these ideas:

• How many people got killed and injured per year?

• Visualize mass shootings on the U.S map

• Is there any correlation between shooter and his/her race, gender

• Any correlation with calendar dates? Do we have more deadly days, weeks or months on average

• What cities and states are more prone to such attacks

• Can you find and combine any other external datasets to enrich the analysis, for example, gun ownership by state

• Any other pattern you see that can help in prediction, crowd safety or in-depth analysis of the event

• How many shooters have some kind of mental health problem? Can we compare that shooter with general population with same condition

Mass Shootings Dataset Ver 3

This is the new Version of Mass Shootings Dataset. I've added eight new variables:

Incident Area (where the incident took place), Open/Close Location (Inside a building or open space) Target (possible target audience or company), Cause (Terrorism, Hate Crime, Fun (for no obvious reason etc.) Policeman Killed (how many on duty officers got killed) Age (age of the shooter) Employed (Y/N) Employed at (Employer Name) Age, Employed and Employed at (3 variables) contain shooter details

Mass Shootings Dataset Ver 4

Quite a few missing values have been added

Mass Shootings Dataset Ver 5

Three more recent mass shootings have been added including the Texas Church shooting of November 5, 2017

I hope it will help create more visualization and extract patterns.

Keep Coding!

Introduction: Firearm legality and ownership have been contentious topics in American culture, due to the well-documented, yet preventable, health and safety risks. States vary in the amount of firearm ownership, as well as firearm mortality and injury rates. Objectives: The primary aim of this project is to compare two states, New Jersey and Texas, on the likelihood of firearm violence occurring to each state's citizens. The variables of gun ownership, firearm mortalities, and firearm injuries are compared and visualized to understand if living in one state is safer than living in the other. Methods: Data analysis focused on connecting and comparing the two states with variables pointing to firearm safety/danger. Line graphs compare the two states and firearm injuries and mortalities over a sixteen-year period as well as number of firearms per state. Scatterplots show a correlation, if any, between number of firearms and injuries/mortalities in the two states. Results: Texas had a consistently higher mortality rate by firearms (excluding suicides) for each year of the seventeen years. Texas also led in firearm injuries from the years 2000-2010, 2012, and 2014-2016, but not in 2011 and 2013. New Jersey consistently has a lower mortality rate (3.5 and under per 100,000) and lower gun ownership (.11 and under per household). Texas’ data has both a higher mortality rate (between 3.8 and 4.8 per 100,000) and a higher gun ownership rate (.34 to .40 per household). With a few exceptions from the years 2011 and 2013, the state data points are clustered to show the relationship between gun ownership and firearm injuries to be high/high for Texas and low/low for New Jersey. Conclusions: From the years 2000-2016 it is, on average, 20% less likely that one will be injured by a firearm and 30% less likely that one will be killed by a firearm if one were to live in New Jersey instead of Texas, causing the conclusion that it is safer to live in New Jersey than in Texas.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Gun Plain Township, Michigan, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Gun Plain Township, Michigan reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Gun Plain township households based on income levels.

Key observations

• For Family Households: In Gun Plain township, the majority of family households, representing 19.44%, earn $75,000 to $99,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $10,000 to $14,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Gun Plain township, the majority of non-family households, accounting for 26.4%, have income $25,000 to $29,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $10,000 to $14,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Gun Plain Township, Michigan (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Gun Plain Township, Michigan
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Gun Plain Township, Michigan
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Gun Plain Township, Michigan

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gun Plain township median household income. You can refer the same here

This dataset is about book subjects. It has 2 rows and is filtered where the books is The gun, the law and the Irish people. It features 2 columns including publication dates.

The Gun Violence Archive (GVA) is an online archive of gun violence incidents collected from over 7,500 law enforcement, media, government and commercial sources daily in an effort to provide near-real time data about the results of gun violence. GVA was established in 2013 an independent data collection and research group to provide comprehensive data for the national conversation regarding gun violence.

GVA catalogs both incidents of gun-related deaths and incidents where a victim was injured by shooting or by a victim who was the subject of an armed robber or home invader. Incidents of defensive gun use, homeowners who stop a home invasion, store clerks who stop a robbery, individuals who stop an assault or rape with a gun are also collected. The two exceptions to the near real-time collection are suicides by gun, which are collected quarterly and annually due to differing distribution methods by government agencies, and for armed robberies with no injuries or DGU characteristics, which are collected in aggregate with law enforcement quarterly and annual reports. GVA also records incidents of Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF) and local law enforcement involvement in recovering illegal or stolen weapons; incidents where guns were reported stolen from homes, vehicles, and businesses; incidents where Airsoft or BB guns are used as weapons (but not where they are used in general vandalism or delinquency); and TSA data of guns illegally taken through airport security points. Incident data are categorized by number of deaths, number of injuries, number of children, number of teens, mass shootings, officers shot, suspect shot by officer, home invasion, defensive use, and unintentional shooting.

Weapons Sales in Australia decreased to 75 SIPRI TIV Million in 2024 from 80 SIPRI TIV Million in 2023. Australia Weapons Sales - values, historical data, forecasts and news - updated on December of 2025.

People With Helmet

## Overview

People With Helmet is a dataset for object detection tasks - it contains Helmet annotations for 767 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).