Number, percentage and rate (per 100,000 population) of homicide victims, 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.

Number, percentage and rate (per 100,000 population) of homicide victims, by gender (all genders; male; female; gender unknown) and Indigenous identity (total; Indigenous identity; non-Indigenous identity; unknown Indigenous identity), Canada, provinces and territories, 2014 to 2024.

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.

This dataset is about book series. It has 1 row and is filtered where the books is When men murder women. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

United Kingdom UK: Intentional Homicides: Female: per 100,000 Female data was reported at 0.874 Ratio in 2016. This records an increase from the previous number of 0.776 Ratio for 2015. United Kingdom UK: Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 0.825 Ratio from Dec 2005 (Median) to 2016, with 12 observations. The data reached an all-time high of 1.115 Ratio in 2007 and a record low of 0.599 Ratio in 2011. United Kingdom UK: Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

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.

About this dataset

Context

The dataset tabulates the population of Kill Devil Hills by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Kill Devil Hills. The dataset can be utilized to understand the population distribution of Kill Devil Hills by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Kill Devil Hills. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Kill Devil Hills.

Key observations

Largest age group (population): Male # 50-54 years (431) | Female # 55-59 years (445). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Kill Devil Hills population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Kill Devil Hills is shown in the following column.
• Population (Female): The female population in the Kill Devil Hills is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Kill Devil Hills for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Kill Devil Hills Population by Gender. You can refer the same here

This dataset contains information about femicide incidents in Turkey between 2008 and 2024. The data was scraped from Anıt Sayaç`(The Monument Counter), a digital memorial for women who lost their lives to violence.

Content

The dataset includes the following columns:

Name: The name of the victim.

Year: The year the incident occurred.

Age: The age of the victim (translated: Adult, Minor, Elderly, or numeric age).

City: The city in Turkey where the incident occurred.

District: The specific district (if available).

Reason: The reported motive (e.g., Wanting divorce, Jealousy, Suspicious death).

Perpetrator: The relationship of the killer to the victim (e.g., Husband, Ex-Boyfriend, Relative).

Protection_Order: Whether the victim had a protection order at the time.

Method_of_Killing: The weapon or method used.

Status: The current legal status of the perpetrator (e.g., Arrested, Suicide, Fugitive).

Source Link: The direct link to the victim's memorial page on Anıt Sayaç.

Acknowledgements

This data is entirely sourced from Anıt Sayaç (anitsayac.com). Please attribute the original source when using this data for research or visualization. The translation from Turkish to English was performed to make this critical social issue accessible to a global audience.

Number of victims of spousal homicide, Canada and regions, 1997 to 2024.

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.

This project provides a comprehensive dataset on intentional homicides in Mexico from 1990 to 2023, disaggregated by sex and state. It includes both raw data and tools for visualization, making it a valuable resource for researchers, policymakers, and analysts studying violence trends, gender disparities, and regional patterns.ContentsHomicide Data: Total number of male and female victims per state and year.Population Data: Corresponding male and female population estimates for each state and year.Homicide Rates: Per 100,000 inhabitants, calculated for both sexes.Choropleth Map Script: A Python script that generates homicide rate maps using a GeoJSON file.GeoJSON File: A spatial dataset defining Mexico's state boundaries, used for mapping.Sample Figure: A pre-generated homicide rate map for 2023 as an example.Requirements File: A requirements.txt file listing necessary dependencies for running the script.SourcesHomicide Data: INEGI - Vital Statistics MicrodataPopulation Data: Mexican Population Projections 2020-2070This dataset enables spatial analysis and data visualization, helping users explore homicide trends across Mexico in a structured and reproducible way.

Japan JP: Intentional Homicides: Female: per 100,000 Female data was reported at 0.302 Ratio in 2016. This records a decrease from the previous number of 0.333 Ratio for 2015. Japan JP: Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 0.366 Ratio from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 0.517 Ratio in 2008 and a record low of 0.291 Ratio in 2013. Japan JP: Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

Crime rate against Children-India

Investigating Crime Trends and Patterns Across India

By Bhavna Chawla [source]

About this dataset

This dataset provides an in-depth look at crime against children throughout India. The data, collected from state and union territories throughout the country, tracks arrests made in response to a variety of crimes including infanticide, murder of children, rape of Children, kidnapping and abduction of children, foeticide, abetment of suicide, exposure and abandonment. Additionally it looks at procuration of minor girls as well as buying or selling minors for prostitution. It also illustrates arrests made related to violation or prevention under the Prohibition Of Child Marriage Act (PCMA).

The dataset paints an unfortunately dark image across India with rising numbers each year - painfully representing the suffering these innocent minors have faced over time. Through this dataset we can not only get a better understanding on who is leading the charge in terms of crime rate but also uncover startling patterns about type specified categories that are particularly egregious when it comes to number of arrests made. By examining this data more closely together we can unravel meaningful solutions which ultimately could help protect our beloved child population from needless harm and distress

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How to use the dataset

This dataset is suitable for researchers interested in learning more about crime against children as well as government planners who may want to analyze which states have higher rates of various types of crimes and identify strategies for managing them.

To use this dataset, start by examining the main columns – STATE/UT, CRIME HEAD, 2001-2012 – which provide additional information about each row such as state or UT name and type of crime committed respectively. Then you can use a visualized comparison to evaluate trends across all the listed years: a look at total numbers or changes over time will help reveal how arrests vary among different categories or within a particular year; it will also identify areas with particularly high numbers that need more attention from policy makers. These visualizations can also be compared with statistics on population density or socio-economic characteristics such as literacy rate or poverty levels to get further insights into characterizing patterns for targeted interventions that could reduce criminal activities towards vulnerable communities.

Additionally, you could use this dataset combined with other external sources/variables (governance measures taken against certain categories etc.) to build predictive models that identify relationships between risks factors associated with higher rate of specific type(s) criminal activities prevailing amongst certain age group(s). Such approaches would help contribute towards evidence informed public safety interventions, public health initiatives and legal systems strengthening over time specifically targeting those districts where higher rates are taking place so that people especially women & girls are protected from any form physical abuse & harassment leading potential threat on their living condition & livelihood opportunities eventually affecting national development levels if left unchecked regularly each year progressing forward

Research Ideas

• This dataset could be used to identify the states with the highest crime rates against children, and explore any potential correlations between crime statistics and social or economic factors in those states.
• This dataset can also be used to analyze state-wise trends over time to assess whether government initiatives aimed at curbing crimes against children have been effective or not.
• The dataset can also help researchers examine which type of crimes are most prevalent in each state/UT and come up with ways to reduce these crimes via policy measures or public outreach programs, etc

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Crime head-wise persons arrested under crime against children during 2001-2012.csv | Column name | Description | |:---------------|:----------------------------------------------------------------| | STATE/UT | The state or union territory in India. (String) | | CRIME HEAD | The type of crime against chi...

The goal of the Chicago Women's Health Risk Study (CWHRS) was to develop a reliable and validated profile of risk factors directly related to lethal or life-threatening outcomes in intimate partner violence, for use in agencies and organizations working to help women in abusive relationships. Data were collected to draw comparisons between abused women in situations resulting in fatal outcomes and those without fatal outcomes, as well as a baseline comparison of abused women and non-abused women, taking into account the interaction of events, circumstances, and interventions occurring over the course of a year or two. The CWHRS used a quasi-experimental design to gather survey data on 705 women at the point of service for any kind of treatment (related to abuse or not) sought at one of four medical sites serving populations in areas with high rates of intimate partner homicide (Chicago Women's Health Center, Cook County Hospital, Erie Family Health Center, and Roseland Public Health Center). Over 2,600 women were randomly screened in these settings, following strict protocols for safety and privacy. One goal of the design was that the sample would not systematically exclude high-risk but understudied populations, such as expectant mothers, women without regular sources of health care, and abused women in situations where the abuse is unknown to helping agencies. To accomplish this, the study used sensitive contact and interview procedures, developed sensitive instruments, and worked closely with each sample site. The CWHRS attempted to interview all women who answered "yes -- within the past year" to any of the three screening questions, and about 30 percent of women who did not answer yes, provided that the women were over age 17 and had been in an intimate relationship in the past year. In total, 705 women were interviewed, 497 of whom reported that they had experienced physical violence or a violent threat at the hands of an intimate partner in the past year (the abused, or AW, group). The remaining 208 women formed the comparison group (the non-abused, or NAW, group). Data from the initial interview sections comprise Parts 1-8. For some women, the AW versus NAW interview status was not the same as their screening status. When a woman told the interviewer that she had experienced violence or a violent threat in the past year, she and the interviewer completed a daily calendar history, including details of important events and each violent incident that had occurred the previous year. The study attempted to conduct one or two follow-up interviews over the following year with the 497 women categorized as AW. The follow-up rate was 66 percent. Data from this part of the clinic/hospital sample are found in Parts 9-12. In addition to the clinic/hospital sample, the CWHRS collected data on each of the 87 intimate partner homicides occurring in Chicago over a two-year period that involved at least one woman age 18 or older. Using the same interview schedule as for the clinic/hospital sample, CWHRS interviewers conducted personal interviews with one to three "proxy respondents" per case, people who were knowledgeable and credible sources of information about the couple and their relationship, and information was compiled from official or public records, such as court records, witness statements, and newspaper accounts (Parts 13-15). In homicides in which a woman was the homicide offender, attempts were made to contact and interview her. This "lethal" sample, all such homicides that took place in 1995 or 1996, was developed from two sources, HOMICIDES IN CHICAGO, 1965-1995 (ICPSR 6399) and the Cook County Medical Examiner's Office. Part 1 includes demographic variables describing each respondent, such as age, race and ethnicity, level of education, employment status, screening status (AW or NAW), birthplace, and marital status. Variables in Part 2 include details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Variables in Part 3 deal with the woman's physical and mental health, including pregnancy, and with her social support network and material resources. Variables in Part 4 provide information on the number and type of firearms in the household, whether the woman had experienced power, control, stalking, or harassment at the hands of an intimate partner in the past year, whether she had experienced specific types of violence or violent threats at the hands of an intimate partner in the past year, and whether she had experienced symptoms of Post-Traumatic Stress Disorder related to the incidents in the past month. Variables in Part 5 specify the partner or partners who were responsible for the incidents in the past year, record the type and length of the woman's relationship with each of these partners, and provide detailed information on the one partner she chose to talk about (called "Name"). Variables in Part 6 probe the woman's help-seeking and interventions in the past year. Variables in Part 7 include questions comprising the Campbell Danger Assessment (Campbell, 1993). Part 8 assembles variables pertaining to the chosen abusive partner (Name). Part 9, an event-level file, includes the type and the date of each event the woman discussed in a 12-month retrospective calendar history. Part 10, an incident-level file, includes variables describing each violent incident or threat of violence. There is a unique identifier linking each woman to her set of events or incidents. Part 11 is a person-level file in which the incidents in Part 10 have been aggregated into totals for each woman. Variables in Part 11 include, for example, the total number of incidents during the year, the number of days before the interview that the most recent incident had occurred, and the severity of the most severe incident in the past year. Part 12 is a person-level file that summarizes incident information from the follow-up interviews, including the number of abuse incidents from the initial interview to the last follow-up, the number of days between the initial interview and the last follow-up, and the maximum severity of any follow-up incident. Parts 1-12 contain a unique identifier variable that allows users to link each respondent across files. Parts 13-15 contain data from official records sources and information supplied by proxies for victims of intimate partner homicides in 1995 and 1996 in Chicago. Part 13 contains information about the homicide incidents from the "lethal sample," along with outcomes of the court cases (if any) from the Administrative Office of the Illinois Courts. Variables for Part 13 include the number of victims killed in the incident, the month and year of the incident, the gender, race, and age of both the victim and offender, who initiated the violence, the severity of any other violence immediately preceding the death, if leaving the relationship triggered the final incident, whether either partner was invading the other's home at the time of the incident, whether jealousy or infidelity was an issue in the final incident, whether there was drug or alcohol use noted by witnesses, the predominant motive of the homicide, location of the homicide, relationship of victim to offender, type of weapon used, whether the offender committed suicide after the homicide, whether any criminal charges were filed, and the type of disposition and length of sentence for that charge. Parts 14 and 15 contain data collected using the proxy interview questionnaire (or the interview of the woman offender, if applicable). The questionnaire used for Part 14 was identical to the one used in the clinic sample, except for some extra questions about the homicide incident. The data include only those 76 cases for which at least one interview was conducted. Most variables in Part 14 pertain to the victim or the offender, regardless of gender (unless otherwise labeled). For ease of analysis, Part 15 includes the same 76 cases as Part 14, but the variables are organized from the woman's point of view, regardless of whether she was the victim or offender in the homicide (for the same-sex cases, Part 15 is from the woman victim's point of view). Parts 14 and 15 can be linked by ID number. However, Part 14 includes five sets of variables that were asked only from the woman's perspective in the original questionnaire: household composition, Post-Traumatic Stress Disorder (PTSD), social support network, personal income (as opposed to household income), and help-seeking and intervention. To avoid redundancy, these variables appear only in Part 14. Other variables in Part 14 cover information about the person(s) interviewed, the victim's and offender's age, sex, race/ethnicity, birthplace, employment status at time of death, and level of education, a scale of the victim's and offender's severity of physical abuse in the year prior to the death, the length of the relationship between victim and offender, the number of children belonging to each partner, whether either partner tried to leave and/or asked the other to stay away, the reasons why each partner tried to leave, the longest amount of time each partner stayed away, whether either or both partners returned to the relationship before the death, any known physical or emotional problems sustained by victim or offender, including the four-item Medical Outcomes Study (MOS) scale of depression, drug and alcohol use of the victim and offender, number and type of guns in the household of the victim and offender, Scales of Power and Control (Johnson, 1996) or Stalking and Harassment (Sheridan, 1992) by either intimate partner in the year prior to the death, a modified version of the Conflict Tactics Scale (CTS)

About this dataset

Context

The dataset tabulates the population of Dead Lake township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Dead Lake township. The dataset can be utilized to understand the population distribution of Dead Lake township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Dead Lake township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Dead Lake township.

Key observations

Largest age group (population): Male # 65-69 years (49) | Female # 65-69 years (47). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Dead Lake township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Dead Lake township is shown in the following column.
• Population (Female): The female population in the Dead Lake township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Dead Lake township for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Dead Lake township Population by Gender. You can refer the same here

Key Features

How to use this dataset

1. Access Case Details: Explore the descriptions and details of the cases associated with each wanted person. This can help you understand the alleged crimes, background information, and the current status of the cases.

2. Data Analysis: Perform data analysis to identify patterns or trends among wanted persons. You can analyze the data to gain insights into the demographics, crimes, or locations associated with these individuals.

3. Geospatial Analysis: Utilize geographical information to map out the locations related to wanted persons. This can help in visualizing the distribution of cases across different regions.

4. Raise Awareness: Share information about wanted persons with your community or through social media. The dataset can be used to raise awareness and assist in locating and apprehending fugitives.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

Number of homicide victims, by method used to commit the homicide (total methods used; shooting; stabbing; beating; strangulation; fire (burns or suffocation); other methods used; methods used unknown), Canada, 1974 to 2024.

7 Types of Human Segmentation

The dataset includes 7 different types of image segmentation of people in underwear. For women, 4 types of labeling are provided, and for men, 3 types of labeling are provided. The dataset solves tasks in the field of recommendation systems and e-commerce.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Types of labeling

Women I - distinctively detailed labeling of women. Special emphasis is placed on distinguishing the internal, external side, and lower breast depending on the type of underwear. The labeling also includes the face and hair, hands, forearms, shoulders, armpits, thighs, shins, underwear, accessories, and smartphones.

![https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fe157d0b7db89497f85c9b2d79d301086%2Fgirls_1_227.png?generation=1681741881080579&alt=media" alt="">

Women II - labeling of images of women with attention to the side abs area (highlighted in gray on the labeling). The labeling also includes the face and hair, hands, forearms, thighs, underwear, accessories, and smartphones.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F901d120c0273ea9a5a328fff15e26583%2Fgirls_2_-1087839647-1867563540.png?generation=1681741958025976&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F75fb0412edf631adce5f42ab6b9e8052%2Fwomen_fat_image_56570.png?generation=1681742864993159&alt=media" alt="">

Women III - primarily labeling of underwear. In addition to the underwear itself, the labeling includes the face and hair, abdomen, and arms and legs.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6f32a06f0754a5a116fc994feae8c6f1%2Fgirls_5_111.png?generation=1681742011331681&alt=media" alt="">

Women IV - labeling of both underwear and body parts. It includes labeling of underwear, face and hair, hands, forearms, body, legs, as well as smartphones and tattoos.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0dc22fcfd8b6e4fad3aa1806d14223ef%2Fgirls_6_image_4534.png?generation=1681742073295272&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4a398547e13c555fdad142f521e62a5f%2Fsports_girls_IadpBSd3mI%20(1).png?generation=1681742947264828&alt=media" alt="">

Men I - labeling of the upper part of men's bodies. It includes labeling of hands and wrists, shoulders, body, neck, face and hair, as well as phones and accessories.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F3dae9889adb2b1415353769ccdd9c01b%2Fman_regular_1532667_38709335.png?generation=1681742128995529&alt=media" alt="">

Men II - more detailed labeling of men's bodies. The labeling includes hands and wrists, shoulders, body and neck, head and hair, underwear, tattoos and accessories, nipple and navel area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa57123e41066aa277bfeac140f4457da%2Fmen_1_3046.png?generation=1681742173310957&alt=media" alt="">

Men Neuro - labeling produced by a neural network for subsequent correction by annotators.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5281cd644bc3f5949aaa9c40fb1cafd4%2F4595%20(1).png?generation=1681742187215164&alt=media" alt="">

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keywords: body segmentation dataset, human part segmentation dataset, human semantic part segmentation, human body segmentation data, human body segmentation deep learning, computer vision dataset, people images dataset, biometric data dataset, biometric dataset, images database, image-to-image, people segmentation, machine learning

Zimbabwe ZW: Intentional Homicides: Female: per 100,000 Female data was reported at 2.041 Ratio in 2012. Zimbabwe ZW: Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 2.041 Ratio from Dec 2012 (Median) to 2012, with 1 observations. Zimbabwe ZW: Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

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This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.

Dataset Characteristics: Multivariate

Subject Area: Health and Medicine

Associated Tasks: Classification, Regression, Clustering

Feature Type: Integer, Real

Instances: 299

Features: 12

Dataset Information

A detailed description of the dataset can be found in the Dataset section of the following paper:

Title: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Authors:

Davide Chicco Giuseppe Jurman Source: BMC Medical Informatics and Decision Making 20, 16 (2020)

DOI: https://doi.org/10.1186/s12911-020-1023-5

Dataset Details

Statistical quantitative description of the category features

number of patients. %: percentage of patients. Full sample: 299 individuals. Dead patients: 96 individuals. Survived patients: 203 individuals.