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.

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

Since 2013, protests opposing police violence against Black people have occurred across a number of American cities under the banner of “Black Lives Matter.” We develop a new dataset of Black Lives Matter protests that took place in 2014–2015 and explore the contexts in which they emerged. We find that Black Lives Matter protests are more likely to occur in localities where more Black people have previously been killed by police. We discuss the implications of our findings in light of the literature on the development of social movements and recent scholarship on the carceral state’s impact on political engagement.

Sadly, the trend of fatal police shootings in the United States seems to only be increasing, with a total 1,173 civilians having been shot, 248 of whom were Black, as of December 2024. In 2023, there were 1,164 fatal police shootings. Additionally, the rate of fatal police shootings among Black Americans was much higher than that for any other ethnicity, standing at 6.1 fatal shootings per million of the population per year between 2015 and 2024. Police brutality in the U.S. In recent years, particularly since the fatal shooting of Michael Brown in Ferguson, Missouri in 2014, police brutality has become a hot button issue in the United States. The number of homicides committed by police in the United States is often compared to those in countries such as England, where the number is significantly lower. Black Lives Matter The Black Lives Matter Movement, formed in 2013, has been a vocal part of the movement against police brutality in the U.S. by organizing “die-ins”, marches, and demonstrations in response to the killings of black men and women by police. While Black Lives Matter has become a controversial movement within the U.S., it has brought more attention to the number and frequency of police shootings of civilians.

All cause of death rates by county, Black or African American (Non-Hispanic/Latino), both sexes, all ages, rural and urban, 2019-2023. Death data were provided by the National Vital Statistics System. Death rates (deaths per 100,000 population per year) are age-adjusted to the 2000 US standard population (20 age groups: <1, 1-4, 5-9, ... , 80-84, 85-89, 90+). Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by the National Cancer Institute. The US Population Data File is used for mortality data.

Number, percentage and rate (per 100,000 population) of persons accused of homicide, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

About this dataset

Context

The dataset tabulates the population of Dead Lake township by race. It includes the population of Dead Lake township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Dead Lake township across relevant racial categories.

Key observations

The percent distribution of Dead Lake township population by race (across all racial categories recognized by the U.S. Census Bureau): 98.44% are white and 1.56% are multiracial.

https://i.neilsberg.com/ch/dead-lake-township-mn-population-by-race.jpeg" alt="Dead Lake township population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Dead Lake township
• Population: The population of the racial category (excluding ethnicity) in the Dead Lake township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Dead Lake township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Dead Lake township Population by Race & Ethnicity. You can refer the same here

The study was a comprehensive analysis of felonious killings of officers. The purposes of the study were (1) to analyze the nature and circumstances of incidents of felonious police killings and (2) to analyze trends in the numbers and rates of killings across different types of agencies and to explain these differences. For Part 1, Incident-Level Data, an incident-level database was created to capture all incidents involving the death of a police officer from 1983 through 1992. Data on officers and incidents were collected from the Law Enforcement Officers Killed and Assaulted (LEOKA) data collection as coded by the Uniform Crime Reporting (UCR) program. In addition to the UCR data, the Police Foundation also coded information from the LEOKA narratives that are not part of the computerized LEOKA database from the FBI. For Part 2, Agency-Level Data, the researchers created an agency-level database to research systematic differences among rates at which law enforcement officers had been feloniously killed from 1977 through 1992. The investigators focused on the 56 largest law enforcement agencies because of the availability of data for explanatory variables. Variables in Part 1 include year of killing, involvement of other officers, if the officer was killed with his/her own weapon, circumstances of the killing, location of fatal wounds, distance between officer and offender, if the victim was wearing body armor, if different officers were killed in the same incident, if the officer was in uniform, actions of the killer and of the officer at entry and final stage, if the killer was visible at first, if the officer thought the killer was a felon suspect, if the officer was shot at entry, and circumstances at anticipation, entry, and final stages. Demographic variables for Part 1 include victim's sex, age, race, type of assignment, rank, years of experience, agency, population group, and if the officer was working a security job. Part 2 contains variables describing the general municipal environment, such as whether the agency is located in the South, level of poverty according to a poverty index, population density, percent of population that was Hispanic or Black, and population aged 15-34 years old. Variables capturing the crime environment include the violent crime rate, property crime rate, and a gun-related crime index. Lastly, variables on the environment of the police agencies include violent and property crime arrests per 1,000 sworn officers, percentage of officers injured in assaults, and number of sworn officers.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table displays the number of COVID-19 deaths among Cambridge residents by race and ethnicity. The count reflects total deaths among Cambridge COVID-19 cases.

The rate column shows the rate of COVID-19 deaths among Cambridge residents by race and ethnicity. The rates in this chart were calculated by dividing the total number of deaths among Cambridge COVID-19 cases for each racial or ethnic category by the total number of Cambridge residents in that racial or ethnic category, and multiplying by 10,000. The rates are considered “crude rates” because they are not age-adjusted. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts.

Of note:

This chart reflects the time period of March 25 (first known Cambridge death) through present.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020.

Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” To protect individual privacy, a category is suppressed when it has one to four people. Categories with zero cases are reported as zero. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

The "Enslaved People in the African American National Biography, 1508-1865" dataset builds on the complete print and online collection of the African American National Biography (AANB), edited by Henry Louis Gates, Jr. and Evelyn Brooks Higginbotham. The full collection contains over 6,000 biographical entries of named historical individuals, including 1,304 for subjects born before 1865 and the abolition of slavery in the United States. In making a subset of biographical entries from the multivolume work, the goal was to extract life details from those biographies into an easy-to-view database form that details whether a subject was enslaved for some or all of their lives and to provide the main biographical details of each subject for contextual analysis and comparison. 52 fields covering location data; gender; names, alternate names and suffixes; dates and places of birth and death; and up to 8 occupations were included. We also added 13 unique fields that provide biographical details on each subject: Free born in North America; Free before 13th Amendment; Ever Enslaved; How was freedom attained; Other/uncertain status; African born; Parent information; Runaways and rebels; Education/literacy; Religion; Slave narrative or memoir author; Notes; and Images.

A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI’s Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Dead Lake township by race. It includes the distribution of the Non-Hispanic population of Dead Lake township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Dead Lake township across relevant racial categories.

Key observations

Of the Non-Hispanic population in Dead Lake township, the largest racial group is White alone with a population of 602 (95.86% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Dead Lake township
• Population: The population of the racial category (for Non-Hispanic) in the Dead Lake township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Dead Lake township total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Dead Lake township Population by Race & Ethnicity. You can refer the same here

BackgroundStudies on the barriers migrant women face when trying to access healthcare services in South Africa have emphasized economic factors, fear of deportation, lack of documentation, language barriers, xenophobia, and discrimination in society and in healthcare institutions as factors explaining migrants’ reluctance to seek healthcare. Our study aims to visualize some of the outcome effects of these barriers by analyzing data on maternal death and comparing the local population and black African migrant women from the South African Development Countries (SADC) living in South Africa. The heightened maternal mortality of black migrant women in South Africa can be associated with the hidden costs of barriers migrants face, including xenophobic attitudes experienced at public healthcare institutions.MethodsOur analysis is based on data on reported causes of death (COD) from the South African Department of Home Affairs (DHA). Statistics South Africa (Stats SA) processed the data further and coded the cause of death (COD) according to the WHO classification of disease, ICD10. The dataset is available on the StatsSA website (http://nesstar.statssa.gov.za:8282/webview/) for research and statistical purposes. The entire dataset consists of over 10 million records and about 50 variables of registered deaths that occurred in the country between 1997 and 2018. For our analysis, we have used data from 2002 to 2015, the years for which information on citizenship is reliably included on the death certificate. Corresponding benchmark data, in which nationality is recorded, exists only for a 10% sample from the population and housing census of 2011. Mid-year population estimates (MYPE) also exist but are not disaggregated by nationality. For this reason, certain estimates of death proportions by nationality will be relative and will not correspond to crude death rates.ResultsThe total number of female deaths recorded from the years 2002 to 2015 in the country was 3740.761. Of these, 99.09% (n = 3,707,003) were deaths of South Africans and 0.91% (n = 33,758) were deaths of SADC women citizens. For maternal mortality, we considered the total number of deaths recorded for women between the ages of 15 and 49 years of age and were 1,530,495 deaths. Of these, deaths due to pregnancy-related causes contributed to approximately 1% of deaths. South African women contributed to 17,228 maternal deaths and SADC women to 467 maternal deaths during the period under study. The odds ratio for this comparison was 2.02. In other words, our findings show the odds of a black migrant woman from a SADC country dying of a maternal death were more than twice that of a South African woman. This result is statistically significant as this odds ratio, 2.02, falls within the 95% confidence interval (1.82–2.22).ConclusionThe study is the first to examine and compare maternal death among two groups of women, women from SADC countries and South Africa, based on Stats SA data available for the years 2002–2015. This analysis allows for a better understanding of the differential impact that social determinants of health have on mortality among black migrant women in South Africa and considers access to healthcare as a determinant of health. As we examined maternal death, we inferred that the heightened mortality among black migrant women in South Africa was associated with various determinants of health, such as xenophobic attitudes of healthcare workers toward foreigners during the study period. The negative attitudes of healthcare workers toward migrants have been reported in the literature and the media. Yet, until now, its long-term impact on the health of the foreign population has not been gaged. While a direct association between the heightened death of migrant populations and xenophobia cannot be established in this study, we hope to offer evidence that supports the need to focus on the heightened vulnerability of black migrant women in South Africa. As we argued here, the heightened maternal mortality among migrant women can be considered hidden barriers in which health inequality and the pervasive effects of xenophobia perpetuate the health disparity of SADC migrants in South Africa.

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.

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.

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 2024.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Dead Lake township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Dead Lake township, the median household income for the households where the householder is White increased by $32,693(56.82%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $57,533 in 2011 and $90,226 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in Dead Lake township, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/dead-lake-township-mn-median-household-income-by-race-trends.jpeg" alt="Dead Lake Township, Minnesota median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Dead Lake township.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

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 Dead Lake township median household income by race. You can refer the same here

Coroner’s Inquisitions are investigations into the deaths of individuals who died by a sudden, violent, unnatural, or suspicious manner, or who died without medical attendance. The coroner would summon a jury of twelve white men, usually prominent citizens of that locality, to assist him in determining cause of death. The jury viewed the body of the deceased and heard the testimony of witnesses which did include both white and Black perspectives. This witness testimony was recorded and after seeing and hearing the evidence, a white jury delivered in writing to the coroner their conclusion concerning cause of death referred to as the inquisition. These causes of death would be determined by a white perspective and Black individuals were only consulted; they were never in a position to make decisions. If a criminal act was determined to be the cause of death, the coroner delivered the guilty person to the sheriff and the inquests would be used as evidence in the criminal trial. In this case, coroner’s inquisitions were filed with the trial papers. See Commonwealth Causes for more. If there was not a trial, coroner’s inquisitions were filed separately and will likely appear in this collection as a stand alone set of documents.

Documents commonly found in coroner’s inquisitions include the inquisition, depositions, and summons. Information found in the inquisition include the name of the coroner, the names of the jurors, the name and age of the deceased if known, gender and race of the deceased, and when, how, and by what means the deceased came to his or her death. If the coroner knew the deceased person to be Black or multiracial, the inquest should identify the person as enslaved; a “free Negro”; a “person of color”; or a “mulatto.” If the coroner knew the deceased person to be enslaved, the inquest should include their name, their enslaver and the enslaver’s residence. Information found in the depositions include the name of the deponent and his or her account of the circumstances that led to the death of the deceased. Unlike many other legal proceedings in antebellum Virginia, enslaved people were permitted to provide depositions for coroners’ inquisitions.

This data is subset focusing on records where African Americans were named either as the deceased or persons of interest involved in the inquest and is a by-product of indexing done for the Virginia Untold: African American Narrative digital collection.

Some data in this collection is drawn directly from the original historical records (see column descriptions) and may contain terminology which is now deemed offensive.

Findings from the analyses based on the Homicide Index recorded by the Home Office, including long-term trends, sex of the victim, apparent method of killing and relationship to victim.

Sphero Tracking Dataset

Maxim Van de Wynckel (Dataset creator), Beat Signer (Supervisor)

Description

A Sphero Mini is a Bluetooth ball that can be controlled by a smartphone (or in our case a laptop). The Sphero Mini can be controlled by sending movement instructions to the Sphero consisting of a direction and speed. In this dataset, we placed a camera on top of a table to create a top-down view of the Sphero moving on the floor. The Sphero was instructed to move in a spiral trajectory from the bottom-right corner to the center of the area. The dataset contains the video recording of the Sphero moving, the input instructions given to the Sphero, and the sensor data retrieved from the Sphero. The dataset was used to evaluate the sensor fusion from various sources.

The dataset was recorded on Friday, November 27, 2020, at the Vrije Universiteit Brussel. The dataset was used in the paper: https://arxiv.org/abs/2101.05198

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4255950%2Fdcb88a3bba4be4a7d5483da9df600a98%2Fdemo-overview.png?generation=1740740962794602&alt=media" alt="">

Area

The dataset was recorded in a 260cm (W) x 200cm (H) area on the floor. The origin (0, 0) is at the bottom-right corner of the video frames.

Trajectory

The input trajectory is defined and visualised in the input data. A spiral trajectory was given to the Sphero toy. The Sphero toy was instructed to move in a spiral trajectory from the bottom-right corner to the center of the area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4255950%2F045e7ad9877f9d044f0b10a7c85cec89%2Finput_final.svg?generation=1740740987619685&alt=media" alt="">

Camera

Processing

The processed data is available in the processed/ folder. The processing steps are as follows:

Video processing

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Wrap the video frames to a top-down view

The video frames are wrapped to a top-down view. Yellow markers were placed on the ground to help with the wrapping process (processed/video_frames_wrapped/).

Convert the video frames to HSV

The video frames are converted to HSV to help with the object detection process (processed/video_frames_hsv/).

Convert the video frames to black and white

The (HSV) video frames are converted to black and white to help with the object detection process (processed/video_frames_bw/).

Position detection

The position of the Sphero is detected in each frame of the video recording. The X-Y coordinates of the Sphero are saved in processed/video_final.csv. Note that the origin (0, 0) is at the bottom-right corner. The position is converted from pixels (the pixel location in the video frame) to centimeters (the real-world location of the Sphero).

Sensor processing

All sensor data from the Sphero toy were processed to match the orientation of the video frames.

Files

• misc/: Images and other files that are not part of the dataset itself.
• raw/: Contains the raw data files generated by OpenHPS.
  • raw/input_frames/: Contains the raw input frames (i.e, the instructions that were given to the Sphero). Each file is a JSON file containing an OpenHPS data frame.
  • raw/sensor_frames/: Contains the raw output sensor data from the Sphero. Each file is a JSON file containing an OpenHPS data frame.
  • raw/video_frames/: Contains the individual frames of the video recording.
  • raw/output.avi: All the frames of the video recording stitched together.
  • raw/dataset_info.json: Contains information about the dataset (primarily the camera and sensor frequency).
• processed/: Contains the processed data files processed by OpenHPS.
  • processed/video_frames_wrapped/: Contains the invidiual frames of the video recording wrapped to a top-down view (step 1).
  • processed/video_frames_hsv/: Contains the individual frames of the video recording in HSV (step 2).
  • processed/video_frames_bw/: Contains the individual frames of the video recording in black and white (step 3).
  • `process...