Introduction Suicide is still one of the world's most important public health issues, with the World Health Organization (WHO) claiming that over 700,000 people die by suicide annually. Suicide is one of the main causes of death, with far-reaching consequences for people, families, and society. Understanding the global patterns and trends in suicide rates is critical for creating effective prevention methods and providing the required support to at-risk individuals. The purpose of this report is to visualize global data on suicides using the WHO dataset (who_suicide_statistics.csv). This dataset has statistics on the number of suicides in various countries, years, age categories, and sexes. By analyzing this data, it will guide us to learn about demographic and temporal patterns of suicide, show high-risk groups, and highlight regions facing significant challenges. The visualizations will employ various techniques such as graphs, charts, and maps to effectively convey the information and guide the viewer through the findings. Through these visualizations and insights, I suggested key points and recommendations needed to minimize suicide incidents in future. Description of the Dataset The dataset (who_suicide_statistics.csv) has extensive data on global suicide statistics collected by the World Health Organization. This dataset is an invaluable resource for analyzing the patterns and trends in suicide rates across countries, years, age groups, and genders. Below is a detailed description of the columns in the dataset and the kind of information each one provides. Columns in the Dataset • country: Description: The name of the country where the data was collected. Type: Categorical Example Values: 'United States', 'Japan', 'Germany' • year: Description: The year the data was recorded. Type: Numerical Example Values: 2000, 2005, 2010 - age: Description: The age group of the individuals whose suicide data is recorded. Type: Categorical Example Values: '15-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75+' • sex: Description: The sex of the individuals whose suicide data is recorded. Type: Categorical Example Values: 'male', 'female' • suicide_no: Description: The number of suicide cases recorded for the specified country, year, age and sex. Type: Numerical Example Values: 15, 42, 108 • population: Description: The population of the specified age group and sex in the country for that year. Type: Numerical Example Values: 345633, 785042, 3356435 Additional Information • Suicide Rate Calculation: Using the suicide_no and population columns, we can calculate the suicide rate per 100,000 population, which normalizes the data and allows for fair comparisons across different countries and demographic groups. Formula: suicides_rate = (suicide_no / population) * 100000

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Abstract

Explore global statistics on a subject that claims 800,000 lives each year.

About this dataset

Context

Suicide is a major cause of death in the world, claiming around 800,000 lives each year. It is ranked as the 14th leading cause of death worldwide as of 2017 and on average men are twice as likely to fall victim to it. It also one of the leading causes of death on young people and older people are at a higher risk as well. Source

Notes

This dataset contains data from 200+ countries on the topic of suicide and mental health infrastructure. It was created by extracting the latest data from WHO and combining it into a single dataset. Variables available range from Country, Sex, Mental health infrastructure and personnel and finally Suicide Rate (amount of suicides per 100k people). Note that the suicide rate is age-standardized, as to not bias comparisons between countries with different age compositions.

How to use

• Explore Suicide rates and their associated trends, as well as the effects of infrastructure and personnel on the suicide rates.
• Forecast suicide rates

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

@misc{Global Health Observatory data repository, title={Mental Health}, url={https://apps.who.int/gho/data/node.main.MENTALHEALTH?lang=en}, journal={WHO} }

License

CC BY NC SA IGO 3.0

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State-wise Suicides by Profession in India

Investigating National Level Trends and Patterns

By Rajanand Ilangovan [source]

About this dataset

This dataset contains data on suicides in India by state, year, profession and gender. Through this dataset, we can gain an understanding of the factors that influence suicide rates across different states, professions and genders. By examining this data we can better understand how to reduce these tragedies in India which are of great concern to citizens, families and the government alike. The columns include the State in India where the suicides occurred; Year in which the suicides occurred; Type_code of the profession of the person who committed suicide; Gender of the person who committed suicide; Age_group of such person; and Total number of suicides for a given State-Year-Typecode-Type-Gender-Agegroup combination. With this insightful data set at our disposal, we can gather valuable insights into why certain types people are more likely to take their own lives than others and look for solutions which would have meaningful implications for society at large

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How to use the dataset

This dataset contains information about the number of suicides in India by state, year, type of profession, gender, and age group. It is an important resource for understanding the trends and patterns in suicides in India. This guide will explain how to use this dataset to gain insights into suicide rates across India.

Exploring the Data

The first step to exploring this data is to examine its structure. There are 8 columns that contain information about each suicide: State (the Indian state where the suicide occurred), Year (the year of occurrence), Type_code (the code for the type of profession or activity engaged in at time of death), Gender (male or female), Age_group (groups based on age-range), Total (total number of suicides for given state/year/type_code/type/gender/age group). In addition, there are other useful descriptive stats such as aggregate totals by year and aggregate totals by state as well as null values indicating missing data points that should be accounted for during analysis.

Analyzing Trends

Once you have a good understanding of the data structure, you can begin analyzing it for patterns and trends. You can look at overall trends across all states or focus on individual states to see if certain decades witness higher suicide rates than others due to specific socioeconomic factors within those states. Similarly, you may identify distinct patterns when examining activity related causes across genders or age groups both generally and within individual states – e.g., self-immolation witnessed significantly more amongst females than males within a given decade etc.. Alternatively you could find out what types occupations had higher incidences during certain years thus ruling out otherwise unlikely ways people chose ‘suicide’!

Finally it may also be useful window shop; use this data set as research material before further framing hypotheses related too changes over time i historical events that directly caused shifts in societal norms like wars / pandemics etc.. And then corroborate results against timelines ascertained through secondary sources such newspapers / anecdotal reports or primary sources like census records summaries published by official agencies etc.. As a index towards which other activities were attempted within scope!

Overall these analyses can help policy makers understand better how best resources can be allocated while developing interventions aimed at reducing suicidal tendencies amongst different demographic segments including males & females , adolescents & elderly people respectively!

Research Ideas

• Analyzing trends in suicides across different states in India over time to identify regional disparities and support the implementation of targeted policies and interventions.
• Mapping out the suicide hotspots across age groups, genders, and profession types to better target prevention efforts in those areas.
• Examining differences by profession type among populations with higher suicide rates in order to suggest preventative measures or resources tailored specifically for such populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Suicides_in_India.csv | Column name | Description ...

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands.

The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is taken from the external causes of death (Niet-Natuurlijke dood) file. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code "jumping in front of train/metro". For these years 1950-1978 "jumping in front of train/metro" has been left empty, and it has been counted in the group "other method".

Relative figures have been calculated per 100 000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year.

Data available from: 1950

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.

Number of suicides and suicide rates by sex and age in England and Wales. Includes information on conclusion type, the proportion of suicides by method, and the median registration delay.

Fire Incident data includes all fire incident responses. This includes emergency medical services (EMS) calls, fires, rescue incidents, and all other services handled by the Fire Department.

The source of this data is the City of Cincinnati's computer aided dispatch (CAD) database.

This data is updated daily.

DISCLAIMER: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

this project to realized in Power Bi:

Suicide rates vary around the world Suicide rates vary widely between countries. The map shows this.

For some countries in Southern Africa and Eastern Europe, the estimated rates of suicide are high, with over 15 annual deaths per 100,000 people.

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Meanwhile for other countries in Europe, South America and Asia, the estimated rates of suicide are lower, with under 10 annual deaths per 100,000 people.

The wide range in suicide rates around the world is likely the result of many factors. This includes differences in underlying mental health and treatment, personal and financial stress, restrictions on the means of suicide, recognition and awareness of suicide, and other factors.

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WHAT YOU SHOULD KNOW ABOUT THIS DATA Suicide estimates come from death certificate data, using deaths that were classified under death codes for 'intentional self-harm' in the International Classification of Diseases (ICD). This includes people who had self-harmed but had not intended to die, and they may not be considered suicides by the country's particular legal definition. In many countries, deaths due to self-harm are highly underreported due to social stigma, cultural and legal concerns. Instead, these deaths are often misclassified in reported data, especially as deaths due to "events of undetermined intent", accidents, homicides, or unknown causes. To account for this, the WHO's Global Health Observatory reclassifies a proportion of deaths reported with those causes as suicides, according to the fraction that are estimated to be deaths by suicide. As a result, data on suicide rates represent a better estimate of how many people die from suicide.

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Suicides may still be underestimated after this adjustment, especially if they are misclassified as other types of deaths.2 This can also be why some countries appear to have rising suicide rates, if the rates of misclassification decline.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

Crude death rate from suicide and intentional self-harm per 100 000 people, by age group. Suicide registration methods vary between countries and over time. Figures do not include deaths from events of undetermined intent (part of which should be considered as suicides) and attempted suicides which did not result in death.

This dataset contains individual details who committed suicide in Bangladesh during the Pandemic between February, 2020 to November, 2020. This dataset includes details of every individuals who committed suicide like personal details, family & social life, profession, financial condition, methods of committing suicide, location and weather info. The dataset is freely available. The major fields included in this dataset are: age group, age, gender, profession group, reason, method, suicide date & time, addiction status, mental status, economic condition, marital status, family details, academic qualification, weather. Apart from the above data this dataset also contains a CSV file of a Bengali wordcloud built on social media posts of the suicide victims.

The access to the dataset files is kept restricted. Fill up the below form (link in the References section) to request the data.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/CAJrcG2qBzdgHFsUWHFw

This indicator is defined as the crude death rate from suicide and intentional self-harm per 100 000 people, by age group. Figures should be interpreted with care as suicide registration methods vary between countries and over time. Moreover, the figures do not include deaths from events of undetermined intent (part of which should be considered as suicides) and attempted suicides which did not result in death.

Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD-10 codes used to identify suicides are X60-X84, Y87.0, and U03. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Suicide is a leading cause of preventable death in Los Angeles County, affecting individuals of all ages and races and ethnicities. While there is a strong association between suicide and health conditions, such as mood and anxiety disorders or substance use disorders, suicide is rarely caused by a single circumstance and is more often due to a combination of individual, relational, and environmental factors. Individual factors can include history of mental illness, previous suicide attempts, adverse childhood events, or financial hardship. Relational factors include experiences of bullying, loss of relationships, or social isolation. Environmental factors include lack of access to healthcare, community violence, or social stigma associated with seeking help for a mental illness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Context

This dataset contains information on suicides which happened in India during 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4208638%2Ffab2e99b439f9780daf358511060f514%2FWorld-Suicide-Prevention-Day.jpg?generation=1598114750200382&alt=media" alt="">

The singular age-old social precept of 'Lok Kya Kahenge?' (loosely translated: "What will people say?") suppresses the much-needed psychological care in India. It's high time that we understand why suicides happen and what are the reasons behind it. This dataset aims to spread awareness about suicides in India.

Content

I acquired this dataset from here. Have a look at the website.

This dataset contains 9 files in .csv format. You can find a description for each column. Let me summarize it here as well.

1. Cause-wise distribution of suicides in Central Armed Police Force (CAPF) during 2015.
2. Economic Status-wise distribution of suicides during 2015.
3. Educational Status-wise distribution of suicides during 2015.
4. Farmer or Cultivators distribution of suicides during 2015.
5. Profession-wise distribution of suicides during 2015.
6. Social status-wise distribution of suicides during 2015.
7. Cause-wise distribution of suicides during 2015.
8. Suicides by Agricultural labourers during 2015.
9. Suicides by means adopted during 2015.

Inspiration

We now have plenty of data to explore to draw some conclusions about suicides which happened in India during 2015. Let's start by answering these questions: - What are the top 5 states where Farmers' suicides occurred the most? - What's the top reason that agricultural labourers committed suicide? - Which Profession has the most suicides? What could be the reason? - How many Transgender suicides have occurred in different categories?

I hope these questions interest you in starting to explore this dataset.

Acknowledgements

I thank the Indian Government for making it public under their Open Government Data (OGD) Platform India. Please use this dataset strictly for educational purposes. Thank you.

Suicide Rate - This indicator shows the suicide rate per 100,000 population. Suicide is a serious public health problem that can have lasting effects on individuals, families, and communities. Mental disorders and/or substance abuse have been found in the great majority of people who have died by suicide. In Maryland, approximately 500 lives are lost each year to this preventable cause of death. Link to Data Details

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Age-adjusted rate of suicide deaths by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (String): Year of data; presented as pooled years (2007 to 2016)Category (String): Lists the category representing the data: Santa Clara County is for total population, age categories as follows: <18, 18 to 44, 45 to 64, 65+; 10 to 19, 20 to 24; 10 to 24; <1, 1 to 4, 5 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United States and Healthy People 2020 targetRate per 100,000 people (Numeric): Suicide rate. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

https://i.imgur.com/Vrs6apv.png" alt="">

Context

There is a well-documented phenomenon of increased suicide rates among United States military veterans. One recent analysis, published in 2016, found the suicide rate amongst veterans to be around 20 per day. The widespread nature of the problem has resulted in efforts by and pressure on the United States military services to combat and address mental health issues in and after service in the country's armed forces.

In 2013 News21 published a sequence of reports on the phenomenon, aggregating and using data provided by individual states to typify the nationwide pattern. This dataset is the underlying data used in that report, as collected by the News21 team.

Content

The data consists of six files, one for each year between 2005 and 2011. Each year's worth of data includes the general population of each US state, a count of suicides, a count of state veterans, and a count of veteran suicides.

Acknowledgements

This data was originally published by News21. It has been converted from an XLS to a CSV format for publication on Kaggle. The original data, visualizations, and stories can be found at the source.

Inspiration

What is the geospatial pattern of veterans in the United States? How much more vulnerable is the average veteran to suicide than the average citizen? Is the problem increasing or decreasing over time?

This group of datasets describe the suicides in Scotland for the period 1982-2009. There are 4 separate datasets: All Suicides/Male Suicides/Female Suicides/All Suicide Rate (expressed per 100,000 people). The data is broken down into Local Authority Areas making it easier to investigate any spatial disparity in the suicide figures. A couple of points are worth noting are that it is unclear if the suicide data shows all suicides or just those of Adults. A recent Scottish Government report(http://www.scotland.gov.uk/Publications/2007/03/01145422/20) used deaths of people over 15 years old. Differences in the rates between this data and the results presented in the Scottish Government report may also be due to different population datasets being used. Suicide data sources form the Scottish Public Health Observatory (http://www.scotpho.org.uk/home/Healthwell-beinganddisease/suicide/suicide_data/suicide_la.asp) and the population data used to calculate the rates was sourced from ShareGeo Open (http://hdl.handle.net/10672/95) which uses mid-year estimates downloaded from Nomis (www.nomisweb.co.uk/. Datasets were joined to Local Authority (district, unitary authority and borough) boundaries downloaded from Ordnance Survey OpenData Boundary Line dataset. All spatial analysis was carried out in ArcGIS. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-13 and migrated to Edinburgh DataShare on 2017-02-21.

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.