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.

In the United States, gun laws vary from one state to the next; whether residents need a permit or a background check to purchase a firearm, whether residents must undergo firearm training before making this purchase, and whether residents can openly carry their guns in public is dependent upon state legislation. As of January 15, 2025, ** U.S. states required background checks and/or permits for the purchase of a handgun. A further ** states had regulations on openly carrying firearms in public; however, only California, Connecticut, Florida, and Illinois had completely prohibited open carry for all firearms. In comparison, Maryland, New Jersey, and New York prohibited open carry for handguns but either did not have regulations in place or required a permit for other types of guns. A constitutional right The Second Amendment of the Constitution, which states that citizens have the right to bear arms, has made it difficult for any gun control legislation to be passed on a national level in the United States. As a result, gun control laws in the U.S. are state-based, and often differ based on political perspectives. States with strong gun laws in place, such as Massachusetts, generally experience less gun violence, however, some states with strong gun laws, such as Maryland, continue to face high rates of gun violence, which has largely been attributed to gun trafficking activity found throughout the nation. A culture of gun owners In comparison to other high-income countries with stricter gun control laws, the United States has the highest gun homicide rate at **** gun homicides per 100,000 residents. However, despite increasing evidence that easy access to firearms, whether legal or illegal, encourages higher rates of gun violence, the United States continues to foster an environment in which owning a firearm is seen as personal freedom. Almost **** of U.S. households have reported owning at least one firearm and ** percent of registered voters in the U.S. were found to believe that it was more important to protect the right of Americans to own guns, compared to ** percent who said it was more important to limit gun ownership.

Gun ownership is a highly a consequential political behavior. It often signifies a belief about the inadequacy of state-provided security and leads to membership in a powerful political constituency. As a result, it is important to understand why people buy guns and how shifting purchasing patterns affect the composition of the broader gun owning community. We address these topics by exploring the dynamics of the gun-buying spike that took place during the COVID-19 pandemic, which was one of the largest in American history. We find that feelings of diffuse threat prompted many individuals to buy guns. Moreover, we show that new gun owners, even more than buyers who already owned guns, exhibit strong conspiracy and anti-system beliefs. These findings have substantial consequences for the subsequent population of gun owners and provide insight into how social disruptions can alter the nature of political groups.

Context

The Gun Violence Archive is an online archive of gun violence incidents collected from over 2,000 media, law enforcement, government and commercial sources daily in an effort to provide near-real time data about the results of gun violence. GVA in an independent data collection and research group with no affiliation with any advocacy organization.

Content

This dataset includes files that separate gun violence incidents by category, including deaths and injuries of children and teens, and a collection of mass shootings.

Inspiration

• What has been the trend of gun violence in the past few years?
• What states have the highest incidents per capita per year? How has this metric changed over time?
• Are officer involved shootings on the rise? Where are they most concentrated? Do they correlate with the rates of accidental deaths and mass shootings?

Acknowledgements

This dataset is owned by the Gun Violence Archive, and can be accessed in its original form 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.

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:

The State Firearm Laws project aims to provide researchers with the data necessary to evaluate the effectiveness of various firearm laws. By carefully monitoring how gun legislation impacts firearm-related violence, we can provide policymakers with the evidence they need to make gun ownership safer for everyone.

Ammunition regulations establish rules for anyone in the business of buying or selling firearm ammunition. Federal regulation of firearm ammunition usually accompanies the regulation of firearms, rather than existing independently. For example, the federal age requirements for ammunition purchase, by type of firearm and type of dealer, are the same as those for the purchase of firearms, and the populations that are prohibited from possessing firearms are also prohibited from possessing firearm ammunition.

Content:

Data covers all 50 US States, 1991-2017 and includes:

• Vendor license required to sell ammunition.
• Records of ammunition sales must be retained by the dealer.
• Permit required to purchase ammunition.
• Background checks required for ammunition purchases.
• Sale of ammunition is restricted to the same categories of those who are legally allowed to purchase firearms.
• Purchase of any type of ammunition restricted to those ages 18 and older.
• Purchase of handgun ammunition restricted to those ages 21 and older.

Acknowledgements:

One-hundred of the 133 provisions were coded by Michael Siegel, MD, MPH, Boston University School of Public Health, with funding from the Robert Wood Johnson Foundation, Evidence for Action: Investigator-Initiated Research to Build a Culture of Health program (grant #73337), using data derived from the Thomson Reuters Westlaw legislative database. The other 33 provisions were coded using a database created by Everytown for Gun Safety and Legal Science, LLC. Shared in accordance with the Creative Commons Attribution-4.0 International License, which is incorporated herein by this reference. No changes were made to the original coding, but the data were adapted for use in this database. See the codebook for a list of which provisions were coded by which source. Additional materials include an associated report and related publications.

Inspiration:

• Which states have seen the most increase in regulation? Any decrease?
• Can you correlate gun laws and homicides from this dataset?

Context

There's currently a lack of large and easily-accessible amounts of detailed data on gun violence.

Content

This project aims to change that; we make a record of more than 260k gun violence incidents, with detailed information about each incident, available in CSV format. We hope that this will make it easier for data scientists and statisticians to study gun violence and make informed predictions about future trends.

The CSV file contains data for all recorded gun violence incidents in the US between January 2013 and March 2018, inclusive.

Acknowledgements

Where did you get the data?

The data was downloaded from gunviolencearchive.org. From the organization's description:

Gun Violence Archive (GVA) is a not for profit corporation formed in 2013 to provide free online public access to accurate information about gun-related violence in the United States. GVA will collect and check for accuracy, comprehensive information about gun-related violence in the U.S. and then post and disseminate it online.

How did you get the data?

Because GVA limits the number of incidents that are returned from a single query, and because the website's "Export to CSV" functionality was missing crucial fields, it was necessary to obtain this dataset using web scraping techniques.

Stage 1: For each date between 1/1/2013 and 3/31/2018, a Python script queried all incidents that happened at that particular date, then scraped the data and wrote it to a CSV file. Each month got its own CSV file, with the exception of 2013, since not many incidents were recorded from then.

Stage 2: Each entry was augmented with additional data not directly viewable from the query results page, such as participant information, geolocation data, etc.

Stage 3: The entries were sorted in order of increasing date, then merged into a single CSV file.

Inspiration

I believe there are plenty of ways this dataset can be put to good use. If you have an interesting idea or feel like messing around with the data, then go for it.

I was originally inspired to compile it in the wake of the Parkland shooting and the mass media coverage that followed. Reports like this and this showed that Nikolas Cruz had exhibited plenty of warning signs on social media before the shooting; what if we could build a machine learning system that preemptively detected such signs?

Firearms background checks for the USA for 2012 (Jan-Nov) and since 1999.These statistics represent the number of firearm background checks initiated through the NICS. They do not represent the number of firearms sold. NICS is used by Federal Firearms Licensees (FFLs) to instantly determine whether a prospective buyer is eligible to buy firearms or explosives. Before ringing up the sale, cashiers call in a check to the FBI or to other designated agencies to ensure that each customer does not have a criminal record or isn't otherwise ineligible to make a purchase. More than 100 million such checks have been made in the last decade, leading to more than 700,000 denials. More information on NICS - http://www.fbi.gov/about-us/cjis/nics Some really useful informations such as the rate of checks per 1000 people. All data is provided by state. Downloaded from the Guardian Datablog - http://www.guardian.co.uk/news/datablog/2012/dec/17/how-many-guns-us and then joined to USA States data http://geocommons.com/overlays/21424. Gun data originally from FBI http://www.fbi.gov/about-us/cjis/nics. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-17 and migrated to Edinburgh DataShare on 2017-02-21.

The dataset is sourced and edited from
data.world

Description is given as below:

Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death with U.S. - Mexico Border Regions 1999-2019 on CDC WONDER Online Database, released in 2020. Data are from the Multiple Cause of Death Files, 1999-2019, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. U.S. - Mexico border counties has been demarcated as the 44 counties that are located within 100 kilometers (62 miles) defined under the 1983 La Paz Agreement. Accessed at http://wonder.cdc.gov/ucd-border.html on Nov 6, 2021 12:22:30 AM

Query Parameters: Title: Gun Deaths by County MCD - ICD-10 Codes: W32 (Handgun discharge); W33 (Rifle, shotgun and larger firearm discharge); W34 (Discharge from other and unspecified firearms); X72 (Intentional self-harm by handgun discharge); X73 (Intentional self-harm by rifle, shotgun and larger firearm discharge); X74 (Intentional self-harm by other and unspecified firearm discharge); X93 (Assault by handgun discharge); X94 (Assault by rifle, shotgun and larger firearm discharge); X95 (Assault by other and unspecified firearm discharge); Y22 (Handgun discharge, undetermined intent); Y23 (Rifle, shotgun and larger firearm discharge, undetermined intent); Y24 (Other and unspecified firearm discharge, undetermined intent); Y35.0 (Legal intervention involving firearm discharge)

Group By: Year; County Show Totals: True Show Zero Values: False Show Suppressed: False Standard Population: 2000 U.S. Std. Population Calculate Rates Per: 100,000 Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)

picture sourced from peterplit

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Gun Plain township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Gun Plain township, the median income for all workers aged 15 years and older, regardless of work hours, was $53,731 for males and $29,536 for females.

These income figures highlight a substantial gender-based income gap in Gun Plain township. Women, regardless of work hours, earn 55 cents for each dollar earned by men. This significant gender pay gap, approximately 45%, underscores concerning gender-based income inequality in the township of Gun Plain township.

- Full-time workers, aged 15 years and older: In Gun Plain township, among full-time, year-round workers aged 15 years and older, males earned a median income of $69,698, while females earned $61,513, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Gun Plain township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Gun Plain township.

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 race. You can refer the same here

This data set contains the data of gun violences that has happened across the USA, world's superpower country. The data set contains the past data and based on it data scientists, data analyst and any ML engineers can work on it and can extract the hidden patterns for this data set. The patterns extracted from this data set will help people in decision making. Coders and programmers can also use this dataset for their projects.

When and to what extent do crises and significant events induce changes in political attitudes? Theories of public opinion and policymaking predict that major events restructure public opinion and pry open new political opportunities. We examine the effect of major events on support for public policies in the context of the Sandy Hook Elementary School mass shooting in December 2012 using a nationally representative panel survey of U.S.\ adults. Across both cross-sectional and within-subject analyses, we find no evidence that Americans granted greater support for gun control after the Sandy Hook shooting. Our null findings persist across a range of political and demographic groups. We also find no evidence of attitude polarization as a result of Sandy Hook. Our results suggest that elite polarization in a particular issue area leads citizens to employ motivated reasoning when interpreting critical events, thereby reducing the capacity for attitude change. Our findings have important implications for identifying the conditions under which major events affect support for public policies and create political opportunities for policy change.

Context

1 in 4 mass shooting victims were children and teens. In the years between 2009 and 2020, the horrific scenes of mass shootings have haunted the nation’s collective conscience.US states with weaker gun laws and higher gun ownership rates have higher rates of mass shootings. Mass shooting is defined as any incident in which four or more people are shot and killed, excluding the shooter. The number of mass shootings that plague this country is far too high, and the counts are just a small fraction of the lives left forever changed after the tragedy of a mass shooting. So here is the data for list of mass shootings in United States from 2018 - 2022.

Content

This dataset has five csv files of years 2018 - 2022. Each data contains following attributes - Date : The date on which the mass shooting incident happened - State : The state where the incident took place - Dead : total number of people died in mass shooting - Injured: total number of people who got injured in mass shooting- - Total : total of dead and injured people - Description : description/short report of the incident which may include information like gender/place etc. Data for 2022 Mass shootings will be updated every 15 days!

Acknowledgements

This data was scraped from https://en.wikipedia.org/wiki/List_of_mass_shootings_in_the_United_States using BeautifulSoup.

Image banner by Wall Street Journal

Inspiration

• EDA and Viz.
• Which state has highest number of mass shootings
• Contextual analysis of the description column
• Trend of mass shooting in past years
• Number of people killed/injured on an average in a month.

Check out my other works

• Datasets
• Notebooks

Content

Poll Quiz - Guns

This folder contains the data behind the quiz Do You Know Where America Stands On Guns?

guns-polls.csv contains the list of polls about guns that we used in our quiz. All polls have been taken after February 14, 2018, the date of the school shooting in Parkland, Florida.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

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.

Description:

This comprehensive dataset offers an in-depth look at gun-related deaths in the United States from 2012 to 2014, as reported by the Centers for Disease Control and Prevention (CDC). It provides a rich source of information for public health research, policy development, and sociological studies, offering a nuanced understanding of the dynamics and demographics of gun-related fatalities.

Features:

• Year: The year when the death occurred, providing a temporal context.
• Month: The month of the incident, adding more granularity to the timeline.
• Intent: Categorizes the death by intent, such as suicide or homicide, essential for understanding the circumstances.
• Police: Indicates whether a police officer was involved in the death.
• Sex: The gender of the deceased, crucial for demographic analysis.
• Age: The age of the deceased, providing insights into age-related trends.
• Race: The race of the deceased, essential for understanding racial disparities.
• Place: The location of the incident, such as home or street, which can influence the context of the death.
• Education: Educational background of the deceased, offering a socio-economic dimension.

Use Case:

This dataset is highly valuable for tasks such as public health research, policy formulation in gun control, and sociological studies. It can be employed to analyze trends and patterns in gun-related deaths, assist in crafting informed laws and public safety measures, and provide a foundation for educational and awareness initiatives about gun violence and its impact on different demographic groups.

Note: - Entries are organized chronologically, capturing each recorded incident in detail. - The dataset is especially significant for examining year-on-year trends and demographic variances in gun-related fatalities, serving as a critical resource for comprehensive analysis and research.

Aggregated information of over three thousand incidents of gun violence within the United States from Jan 2014 through Dec 2021.

Address of the incident is mentioned along with the number of causalities or injuries.

To see further details of the incident including participants, incident details and news sources use: https://www.gunviolencearchive.org/incident/{incident_id}.

This data along with the latest events available at the Gun Violence Archive.

One can easily find postings, articles, etc… in social network about mass shooting, gun violence, it's frequency, impacts, severity, and so on. However, one might be curious in its details of what the root causes are? where are common incident areas? Are those shooters mostly have mental health problem or some kind of severe psycho as everyone has mentioned about? What are those shooters features? How are their behaviors, age range, race, etc? or is there any correlational relationships between those features that helps us understand more about them and their motives? By having more insight of US Mass shooting cases, its common root cause, features of shooter and other related aspects, it might help every single individual to recognize sign of a mass shooter or can even have solutions on education, mental balance support, and obligation for preventing such tragedy to contribute to a better healthier society. For my world fellow friends, hope you all have a quality data source for your references, analysis and learning.

Content

This data covers raw data of mass shooting cases in US from 1966-2019. In fact it does not cover all cases but it gives analyst/ viewer a general viewpoint of gun violence situation in US. For my analysis on this data please find in links below: • US Mass Shooting 1: Overview Analysis and Root Cause 1966-2019 • US Mass Shooting 1: Shooter’s Analysis 1966-2019

Data structure shown in sheet "Data Structure". Data includes: • Fields: 24 • Rows: 339 (339 cases) • Data coverage: partial (not included all mass shooting cases but typical ones) data from 1966-2019 • Cleaned: Yes • Data cleaning procedure: click here • Original data source: (1) https://www.kaggle.com/zusmani/us-mass-shootings-last-50-years?select=Mass+Shootings+Dataset.csv , (2) https://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data/ • Original data problems: Missing data, duplicate data, null data, partial or incomplete data (incomplete location name, missing latitude, longtitude, etc.), incorrect information (number of victims, etc.).

Acknowledgements

Thanks Zeeshan-ul-hassan Usmani and motherjones.com team for your contribution and wish everyone all the best.