THIS DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON JULY 12

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.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Victims of gang-related homicides (total number of homicide victims; number of homicide victims - unknown gang-relation; number of homicide victims - known gang relation; number of gang-related homicide victims; percentage of gang-related homicide victims; rate (per 100,000 population) of gang-related homicide victims), Canada and regions, 1999 to 2023.

This indicator is based on location of residence. Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify homicides are X85-Y09, Y87.1, and U01-U02. 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.Violence is a public health crisis in the US, with gun violence being a major driver. Almost three quarters of homicides involve firearms. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults ages 15-34 years, males, and Black individuals most disproportionately impacted. Comprehensive prevention strategies should work to address the underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The aim of this study was to examine the factors related to different patterns of male violence against women. Employing both intra-individual and sociocultural perspectives, the project focused on the relationship between violence against women and previously established risk factors for intimate partner violence including stressors related to work, economic status, and role transitions (e.g., pregnancy), as well as family power dynamics, status discrepancies, and alcohol use. The following research questions were addressed: (1) To what extent do Caucasian, Black, and Hispanic individuals engage in physical violence with their partners? (2) How are socioeconomic stressors associated with violent relationships among Caucasian, Black, and Hispanic couples? (3) To what extent are changes in patterns of physical violence against women associated with different stages of a relationship (e.g., cohabitation, early marriage, pregnancy, marriage)? (4) To what extent do culturally linked attitudes about family structure (family power dynamics) predict violence among Caucasian, Black, and Hispanic couples? (5) To what extent do family strengths and support systems contribute to the cessation of violence among Caucasian, Black, and Hispanic couples? (6) What is the role of alcohol use in violent relationships among Caucasian, Black, and Hispanic couples? The data used for this project came from the first and second waves of the National Survey of Families and Households (NSFH) conducted by the Center for Demography and Ecology at the University of Wisconsin-Madison [NATIONAL SURVEY OF FAMILIES AND HOUSEHOLDS: WAVE I, 1987-1988, AND WAVE II, 1992-1994 (ICPSR 6906)]. The NSFH was designed to cover a broad range of family structures, processes, and relationships with a large enough sample to permit subgroup analysis. For the purposes of this study, the analytical sample focused on only those couples who were cohabiting or married at the time of the first wave of the study and still with the same person at the time of the second wave (N=3,584). Since the study design included oversamples of previously understudied groups (i.e., Blacks, Mexicans, Puerto Ricans), racial and ethnic comparisons were possible. In both waves of the NSFH several identical questions were asked regarding marital conflicts. Both married and cohabiting respondents were asked how often they used various tactics including heated arguments and hitting or throwing things at each other to resolve their conflicts. In addition, respondents were asked if any of their arguments became physical, how many of their fights resulted in either the respondent or their partner hitting, shoving, or throwing things, and if any injuries resulted as a consequence of these fights. This data collection consists of the SPSS syntax used to recode variables from the original NSFH dataset. In addition, new variables, including both composite variables (e.g., self-esteem, hostility, depression) and husband and wife versions of the variables (using information from both respondent and partner), were constructed. New variables were grouped into the following categories: demographic, personality, alcohol and drug use, relationship stages, gender role attitudes, division of labor, fairness in household chores, social support, and isolation. Psychological well-being scales were created to measure autonomy, positive relations with others, purpose in life, self-acceptance, environmental mastery, and personal growth. Additional scales were created to measure relationship conflict, sex role gender attitudes, personal mastery, alcohol use, and hostility. The Rosenberg Self Esteem Scale and the Center for Epidemiological Studies Depression Scale (CES-D) were also utilized.

Analysis of ‘Police Killings US’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/azizozmen/police-killings-us on 13 February 2022.

--- Dataset description provided by original source is as follows ---

"In 2015, The Washington Post began to log every fatal shooting by an on-duty police officer in the United States. In that time there have been more than 5,000 such shootings recorded by The Post. After Michael Brown, an unarmed Black man, was killed in 2014 by police in Ferguson, Mo., a Post investigation found that the FBI undercounted fatal police shootings by more than half. This is because reporting by police departments is voluntary and many departments fail to do so. The Washington Post’s data relies primarily on news accounts, social media postings, and police reports. Analysis of more than five years of data reveals that the number and circumstances of fatal shootings and the overall demographics of the victims have remained relatively constant..." SOURCE ==> Washington Post Article

For more information about this story

This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

2016 PoliceKillingUS DATASET
2017 PoliceKillingUS DATASET
2018 PoliceKillingUS DATASET
2019 PoliceKillingUS DATASET
2020 PoliceKillingUS DATASET

Features at the Dataset:

The file fatal-police-shootings-data.csv contains data about each fatal shooting in CSV format. The file can be downloaded at this URL. Each row has the following variables:

• id: a unique identifier for each victim
• name: the name of the victim
• date: the date of the fatal shooting in YYYY-MM-DD format
• manner_of_death: shot, shot and Tasered
• armed: indicates that the victim was armed with some sort of implement that a police officer believed could inflict harm
  • undetermined: it is not known whether or not the victim had a weapon
  • unknown: the victim was armed, but it is not known what the object was
  • unarmed: the victim was not armed
• age: the age of the victim
• gender: the gender of the victim. The Post identifies victims by the gender they identify with if reports indicate that it differs from their biological sex.
  • M: Male
  • F: Female
  • None: unknown
• race:
  • W: White, non-Hispanic
  • B: Black, non-Hispanic
  • A: Asian
  • N: Native American
  • H: Hispanic
  • O: Other
  • None: unknown
• city: the municipality where the fatal shooting took place. Note that in some cases this field may contain a county name if a more specific municipality is unavailable or unknown.
• state: two-letter postal code abbreviation
• signs of mental illness: News reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.
• threat_level: The threat_level column was used to flag incidents for the story by Amy Brittain in October 2015. http://www.washingtonpost.com/sf/investigative/2015/10/24/on-duty-under-fire/ As described in the story, the general criteria for the attack label was that there was the most direct and immediate threat to life. That would include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The other and undetermined categories represent all remaining cases. Other includes many incidents where officers or others faced significant threats.
• flee: News reports have indicated the victim was moving away from officers
  • Foot
  • Car
  • Not fleeing

The threat column and the fleeing column are not necessarily related. For example, there is an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer. Also, attacks represent a status immediately before fatal shots by police while fleeing could begin slightly earlier and involve a chase. - body_camera: News reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.

SOURCE

--- Original source retains full ownership of the source dataset ---

In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

What is the NCIC?

The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

Missing people in the United States

A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

This research project examined rural and urban trends in family and intimate partner homicide for the 20-year period from 1980 through 1999. The construct of place served as a backdrop against which changes in trends in family/partner homicide were tracked, and against which various independent measures that purportedly explain variation in the rates were tested. The project merged data from several sources. The offender data file from the Federal Bureau of Investigation's (FBI) Supplementary Homicide Report (SHR) series for 1980 through 1999 was the primary data source. Data for arrests for violent crime, drug, and alcohol-related offenses were obtained from the FBI Report A Arrest File. Population, population density, and race (and racial segregation) data from the decennial U.S. Census for 1980, 1990, and 2000 were also obtained. Data on hospitals, educational attainment, unemployment, and per capita income were obtained from the 2002 Area Resource File (ARF). The total number of proprietors (farm and non-farm) in the United States by state and county for each year were provided by the Regional Economic Profiles data. The project's population and proximity indicator used four categories: metropolitan, nonmetropolitan populations adjacent to a metropolitan area, nonmetropolitan populations not adjacent to a metropolitan area, and rural. Data include homicide rates for 1980 through 1999 for intimate partner homicide, family homicide, all other homicide, and all homicide. Additional variables are included as measures of community socioeconomic distress, such as residential overcrowding, isolation, traditionalist views of women and family, lack of access to health care, and substance abuse. Five-year averages are included for each of the rates and measures listed above.

About this dataset

The Washington Post compiled a dataset of every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

In 2015, The Post began tracking more than a dozen details about each killing by culling local news reports, law enforcement websites and social media and by monitoring independent databases such as Killed by Police and Fatal Encounters. The available features are: - Race of the deceased; - Circumstances of the shooting; - Whether the person was armed; - Whether the victim was experiencing a mental-health crisis; - Among others.

In 2016, The Post is gathering additional information about each fatal shooting that occurs this year and is filing open-records requests with departments. More than a dozen additional details are being collected about officers in each shooting.

The Post is documenting only those shootings in which a police officer, in the line of duty, shot and killed a civilian — the circumstances that most closely parallel the 2014 killing of Michael Brown in Ferguson, Mo., which began the protest movement culminating in Black Lives Matter and an increased focus on police accountability nationwide. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths.

The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. In 2015, The Post documented more than two times more fatal shootings by police than had been recorded by the FBI. Last year, the FBI announced plans to overhaul how it tracks fatal police encounters.

How to use this dataset

• Understanding US Police Shootings by Curtis Thompson
• Dataset Questions and Suggestions
• More datasets

Acknowledgements

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

BibTeX

@misc{wapo-police-shootings-bot , author = {The Washington Post}, title = {data-police-shootings}, month = jan, year = 2015, publisher = {Github}, url = {https://github.com/washingtonpost/data-police-shootings} }

License

CC BY NC SA 4.0

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We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

Abstract (en): These data provide official index crime rates and social and economic indicators of crime rates at three levels of aggregation (city, state, and metropolitan areas) for four decennial years: 1950, 1960, 1970, and 1980. Information is provided on Uniform Crime Reports murder, rape, robbery, aggravated assault, burglary, larceny theft, and vehicle theft rates per 100,000 population. Social and economic indicators include percent black population, percent divorced males, the mean and median family incomes, families below the poverty line, and percent unemployed for each area. The availability of the data for the crime rates in 1980 determined the geographic locations included in the data collection. Data from earlier years do not exist for all geographic locations for which data were available in 1980. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. 2006-01-18 File CB6151.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Funding insitution(s): National Science Foundation (SES8217865). The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

List of every criminal summons issued in NYC during the current calendar year.

This is a breakdown of every criminal summons issued in NYC by the NYPD during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a criminal summons issued in NYC by the NYPD and includes information about the type of crime, the location and time of enforcement. In addition, information related to suspect demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Mortality rate from firearms includes homicides, suicides, accidental deaths, deaths by law enforcement, and deaths for which intent was undetermined. Mortality rate is based on the location of residence and has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify firearm deaths are W32-W34, X72-X74, X93-X95, Y22-Y24, Y35.0, and U01.4. 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.Violence is a public health crisis in the US, with gun violence being a major driver. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults (ages 15-34 years), males, and Black individuals most disproportionately impacted. Firearm-related suicides disproportionately impact older, White men. Comprehensive prevention strategies should work to address underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This study was designed to collect college student victimization data to satisfy four primary objectives: (1) to determine the prevalence and nature of campus crime, (2) to help the campus community more fully assess crime, perceived risk, fear of victimization, and security problems, (3) to aid in the development and evaluation of _location-specific and campus-wide security policies and crime prevention measures, and (4) to make a contribution to the theoretical study of campus crime and security. Data for Part 1, Student-Level Data, and Part 2, Incident-Level Data, were collected from a random sample of college students in the United States using a structured telephone interview modeled after the redesigned National Crime Victimization Survey administered by the Bureau of Justice Statistics. Using stratified random sampling, over 3,000 college students from 12 schools were interviewed. Researchers collected detailed information about the incident and the victimization, and demographic characteristics of victims and nonvictims, as well as data on self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 3, School Data, the researchers surveyed campus officials at the sampled schools and gathered official data to supplement institution-level crime prevention information obtained from the students. Mail-back surveys were sent to directors of campus security or campus police at the 12 sampled schools, addressing various aspects of campus security, crime prevention programs, and crime prevention services available on the campuses. Additionally, mail-back surveys were sent to directors of campus planning, facilities management, or related offices at the same 12 schools to obtain information on the extent and type of planning and design actions taken by the campus for crime prevention. Part 3 also contains data on the characteristics of the 12 schools obtained from PETERSON'S GUIDE TO FOUR-YEAR COLLEGES (1994). Part 4, Census Data, is comprised of 1990 Census data describing the census tracts in which the 12 schools were located and all tracts adjacent to the schools. Demographic variables in Part 1 include year of birth, sex, race, marital status, current enrollment status, employment status, residency status, and parents' education. Victimization variables include whether the student had ever been a victim of theft, burglary, robbery, motor vehicle theft, assault, sexual assault, vandalism, or harassment. Students who had been victimized were also asked the number of times victimization incidents occurred, how often the police were called, and if they knew the perpetrator. All students were asked about measures of self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 2, questions were asked about the _location of each incident, whether the offender had a weapon, a description of the offense and the victim's response, injuries incurred, characteristics of the offender, and whether the incident was reported to the police. For Part 3, respondents were asked about how general campus security needs were met, the nature and extent of crime prevention programs and services available at the school (including when the program or service was first implemented), and recent crime prevention activities. Campus planners were asked if specific types of campus security features (e.g., emergency telephone, territorial markers, perimeter barriers, key-card access, surveillance cameras, crime safety audits, design review for safety features, trimming shrubs and underbrush to reduce hiding places, etc.) were present during the 1993-1994 academic year and if yes, how many or how often. Additionally, data were collected on total full-time enrollment, type of institution, percent of undergraduate female students enrolled, percent of African-American students enrolled, acreage, total fraternities, total sororities, crime rate of city/county where the school was located, and the school's Carnegie classification. For Part 4, Census data were compiled on percent unemployed, percent having a high school degree or higher, percent of all persons below the poverty level, and percent of the population that was Black.

This data collection effort was undertaken to analyze the outcomes of capital appeals in the United States between 1973 and 1995 and as a means of assessing the reliability of death penalty verdicts (also referred to herein as "capital judgments" or "death penalty judgments") imposed under modern death-sentencing procedures. Those procedures have been adopted since the decision in Furman v. Georgia in 1972. The United States Supreme Court's ruling in that case invalidated all then-existing death penalty laws, determining that the death penalty was applied in an "arbitrary and capricious" manner and violated Eighth Amendment protections against cruel and unusual punishment. Data provided in this collection include state characteristics and the outcomes of review of death verdicts by state and year at the state direct appeal, state post-conviction, federal habeas corpus, and all three stages of review (Part 1). Data were compiled from published and unpublished official and archived sources. Also provided in this collection are state and county characteristics and the outcome of review of death verdicts by county, state, and year at the state direct appeal, state post-conviction, federal habeas corpus, and all three stages of review (Part 2). After designing a systematic method for identifying official court decisions in capital appeals and state and federal post-conviction proceedings (no official or unofficial lists of those decisions existed prior to this study), the authors created three databases original to this study using information reported in those decisions. The first of the three original databases assembled as part of this project was the Direct Appeal Database (DADB) (Part 3). This database contains information on the timing and outcome of decisions on state direct appeals of capital verdicts imposed in all years during the 1973-1995 study period in which the relevant state had a valid post-Furman capital statute. The appeals in this database include all those that were identified as having been finally decided during the 1973 to 1995 period (sometimes called "the study period"). The second original database, State Post-Conviction Database (SPCDB) (Part 4), contains a list of capital verdicts that were imposed during the years between 1973 and 2000 when the relevant state had a valid post-Furman capital statute and that were finally reversed on state post-conviction review between 1973 and April 2000. The third original database, Habeas Corpus Database (HCDB) (Part 5), contains information on all decisions of initial (non-successive) capital federal habeas corpus cases between 1973 and 1995 that finally reviewed capital verdicts imposed during the years 1973 to 1995 when the relevant state had a valid post-Furman capital statute. Part 1 variables include state and state population, population density, death sentence year, year the state enacted a valid post-Furman capital statute, total homicides, number of African-Americans in the state population, number of white and African-American homicide victims, number of prison inmates, number of FBI Index Crimes, number of civil, criminal, and felony court cases awaiting decision, number of death verdicts, number of Black defendants sentenced to death, rate of white victims of homicides for which defendants were sentenced to death per 100 white homicide victims, percentage of death row inmates sentenced to death for offenses against at least one white victim, number of death verdicts reviewed, awaiting review, and granted relief at all three states of review, number of welfare recipients and welfare expenditures, direct expenditures on the court system, party-adjusted judicial ideology index, political pressure index, and several other created variables. Part 2 provides this same state-level information and also provides similar variables at the county level. Court expenditure and welfare data are not provided in Part 2, however. Part 3 provides data on each capital direct appeal decision, including state, FIPS state and county code for trial court county, year of death verdict, year of decision, whether the verdict was affirmed or reversed, and year of first fully valid post-Furman statute. The date and citation for rehearing in the state system and on certiorari to the United States Supreme Court are provided in some cases. For reversals in Part 4 information was collected about state of death verdict, FIPS state and county code for trial court county, year of death verdict, date of relief, basis for reversal, stage of trial and aspect of verdict (guilty of aggravated capital murder, death sentence) affected by reversal, outcome on retrial, and citation. Part 5 variables include state, FIPS state and county codes for trial court county, year of death verdict, defendant's history of alcohol or drug abuse, whether the defendant was intoxicated at the time of the crime, whether the defense attorney was from in-state, whether the defendant was connected to the community where the crime occurred, whether the victim had a high standing in the community, sex of the victim, whether the defendant had a prior record, whether a state evidentiary hearing was held, number of claims for final federal decision, whether a majority of the judges voting to reverse were appointed by Republican presidents, aggravating and mitigating circumstances, whether habeas corpus relief was granted, what claims for habeas corpus relief were presented, and the outcome on each claim that was presented. Part 5 also includes citations to the direct appeal decision, the state post-conviction decision (last state decision on merits), the judicial decision at the pre-penultimate federal stage, the decision at the penultimate federal stage, and the final federal decision.

The purpose of the study was to assess the impact of Latino ethnicity on pretrial release decisions in large urban counties. The study examined two questions: Are Latino defendants less likely to receive pretrial releases than non-Latino defendants? Are Latino defendants in counties where the Latino population is rapidly increasing less likely to receive pretrial releases than Latino defendants in counties where the Latino population is not rapidly increasing? The study utilized the State Court Processing Statistics (SCPS) Database (see STATE COURT PROCESSING STATISTICS, 1990-2004: FELONY DEFENDANTS IN LARGE URBAN COUNTIES [ICPSR 2038]). The SCPS collects data on felony cases filed in state courts in 40 of the nation's 75 largest counties over selected sample dates in the month of May of every even numbered year, and tracks a representative sample of felony case defendants from arrest through sentencing. Data in the collection include 118,556 cases. Researchers supplemented the SCPS with county-level information from several sources: Federal Bureau of Investigation Uniform Crime Reporting Program county-level data series of index crimes reported to the police for the years 1988-2004 (see UNIFORM CRIME REPORTS: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1998 [ICPSR 9335], UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1990 [ICPSR 9785], 1992 [ICPSR 6316], 1994 [ICPSR 6669], 1996 [ICPSR 2389], 1998 [ICPSR 2910], 2000 [ICPRS 3451], 2002 [ICPSR 4009], and 2004 [ICPSR 4466]). Bureau of Justice Statistics Annual Survey of Jails, Jurisdiction-Level data series for the years 1988-2004 (see ANNUAL SURVEY OF JAILS: JURISDICTION-LEVEL DATA, 1990 [ICPSR 9569], 1992 [ICPSR 6395], 1994 [ICPSR 6538], 1996 [ICPSR 6856], 1998 [ICPSR 2682], 2000 [ICPSR 3882], 2002 [ICPSR 4428], and 2004 [ICPSR 20200]). Bureau of Justice Statistics National Prosecutors Survey/Census data series 1990-2005 (see NATIONAL PROSECUTORS SURVEY, 1990 [ICPSR 9579], 1992 [ICPSR 6273], 1994 [ICPSR 6785], 1996 [ICPSR 2433], 2001 census [ICPSR 3418], and 2005 [ICPSR 4600]). United States Census Bureau State and County Quickfacts. National Center for State Courts, State Court Organization reports, 1993 (see NCJ 148346), 1998 (see NCJ 178932), and 2004 (see NCJ 212351). Bureau of Justice Statistics Felony Defendants in Large Urban Counties reports, 1992 (see NCJ 148826), 1994 (see NCJ 164616), 1996 (see NCJ 176981), 1998 (see NJC 187232), 2000 (see NCJ 202021), and 2002 (see NJC 210818). The data include defendant level variables such as most serious current offense charge, number of charges, prior felony convictions, prior misdemeanor convictions, prior incarcerations, criminal justice status at arrest, prior failure to appear, age, gender, ethnicity, and race. County level variables include region, crime rate, two year change in crime rate, caseload rate, jail capacity, two year change in jail capacity, judicial selection by election or appointment, prosecutor screens cases, and annual expenditure on prosecutor's office. Racial threat stimuli variables include natural log of the percentage of the county population that is Latino, natural log of the percentage of the county population that is African American, change in the percentage of the county population that is Latino over the last six years and change in the percentage of the county population that is African American over the last six years. Cross-level interaction variables include percentage minority (Latino/African American) population zero percent to 15 percent, percentage minority (Latino/African American) population 16 percent to 30 percent, and percentage minority (Latino/African American) population 31 percent or higher.