Explore detailed global homicide data with this extensive dataset, covering various aspects of intentional homicides. Compiled from multiple reliable sources, including UNODC and WHO, this dataset includes:

• Child homicide rates per 100,000 population
• Distribution of homicide rates
• Female homicide rates and victims
• Age-standardized homicide rates
• Historical homicide rates across Western Europe
• Overall homicide rates and victims

This dataset provides valuable insights for researchers, policymakers, and data scientists interested in crime analysis, public health, and social studies. Analyze trends, identify patterns, and develop predictive models to understand and mitigate the impact of homicides worldwide.

The Countries by Intentional Homicide Rate dataset provides information on the intentional homicide rate in countries around the world.

The dataset contains information on more than 150 countries and territories, including both developed and developing nations. It provides a comprehensive overview of the variation in homicide rates across different regions and countries around the world.

The dataset can be used for a variety of research purposes, including exploring the relationship between homicide rates and other social and economic indicators, identifying trends and patterns in homicide rates over time, and comparing homicide rates across different countries and regions.

Overall, the Countries by Intentional Homicide Rate dataset is a valuable resource for anyone interested in studying crime and violence, and in understanding the social and economic factors that underlie these phenomena....

Description: ChatGPT

Historical dataset showing World murder/homicide rate per 100K population by year from 2000 to 2020.

Historical dataset showing World crime rate per 100K population by year from 2000 to 2020.

Violent Crime in CA

Regional, County, City/Town Rates 2006-2010

By Health [source]

About this dataset

This dataset contains information on the rate of violent crime across California - its regions, counties, cities and towns. The data was collected as part of a larger effort by the Office of Health Equity to better understand public health indicators and ensure equitable outcomes for all.

The numbers reflect more than just a problem in California communities - it reflects a problem with unequal access to resources and opportunity across race, ethnicities and geographies. African Americans in California are 11 times more likely to die from assault or homicide compared to white Californians. Similarly, certain regions report higher crime rates than others at the county level- indicating underlying issues with poverty or institutionalized inequality.

Law enforcement agencies teamed up with the Federal Bureau of Investigations’ Uniform Crime Reports to collect this data table which includes details such as reported number of violent crimes (numerator), population size (denominator), rate per 1,000 population (ratex1000) confidence intervals (LL_95CI & UL_95CI ) standard errors & relative standard errors (se & rse) as well as ratios between city/town rates vs state rates (RR_city2state). Additionally, each record is classified according to region name/code and race/ethnicity code/name , giving researchers further insight into these troubling statistics at both macro and micro levels.

Armed with this information we can explore new ways identify inequitable areas and begin looking for potential solutions that combat health disparities within our communities like never before!

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How to use the dataset

The data is presented with twenty columns providing various segments within each row including:

• Crime definition
• Race/ethnicity code
• Region code
• Geographic area identifier
• Numerator and Denominator values of population
• Standard Error and 95% Confidence Intervals
• Relatvie Standard Error (RSE) value

• Ratios related to city/towns rate to state rate

  The information provided can be used for a variety of applications such as creating visualizations or developing predictive models. It is important to note that rates are expressed per 1,000 population for their respective geographic area during each period noted by the report year field within the dataset. Additionally CA_decile column may be useful in comparing counties due numerical grading system identifying a region’s percentile ranking when compared to other counties within the current year’s entire dataset as well as ratios present under RR_city2state which presents ratio comparison between city/town rate and state rate outside given geographic area have made this an extremely valuable dataset for further analysis

Research Ideas

• Developing a crime prediction and prevention program that uses machine learning models to identify criminal hotspots and direct resources to those areas
• Exploring the connection between race/ethnicity and rates of violence in California
• Creating visualizations and interactive maps to display types of violent crime across different counties within California

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Violent_Crime_Rate_California_2006-2010-DD.csv

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

This dataset contains information about homicide rates across 195 countries around the world. It provides data on the region, subregion, homicide rate (per 100,000 people), total homicide count, and the year of record.

You can use this dataset for data analysis, visualization, or machine learning projects to understand global crime patterns and safety levels.

Number and rate (per 100,000 population) of homicide victims, Canada and Census Metropolitan Areas, 1981 to 2024.

There has been little research on United States homicide rates from a long-term perspective, primarily because there has been no consistent data series on a particular place preceding the Uniform Crime Reports (UCR), which began its first full year in 1931. To fill this research gap, this project created a data series on homicides per capita for New York City that spans two centuries. The goal was to create a site-specific, individual-based data series that could be used to examine major social shifts related to homicide, such as mass immigration, urban growth, war, demographic changes, and changes in laws. Data were also gathered on various other sites, particularly in England, to allow for comparisons on important issues, such as the post-World War II wave of violence. The basic approach to the data collection was to obtain the best possible estimate of annual counts and the most complete information on individual homicides. The annual count data (Parts 1 and 3) were derived from multiple sources, including the Federal Bureau of Investigation's Uniform Crime Reports and Supplementary Homicide Reports, as well as other official counts from the New York City Police Department and the City Inspector in the early 19th century. The data include a combined count of murder and manslaughter because charge bargaining often blurs this legal distinction. The individual-level data (Part 2) were drawn from coroners' indictments held by the New York City Municipal Archives, and from daily newspapers. Duplication was avoided by keeping a record for each victim. The estimation technique known as "capture-recapture" was used to estimate homicides not listed in either source. Part 1 variables include counts of New York City homicides, arrests, and convictions, as well as the homicide rate, race or ethnicity and gender of victims, type of weapon used, and source of data. Part 2 includes the date of the murder, the age, sex, and race of the offender and victim, and whether the case led to an arrest, trial, conviction, execution, or pardon. Part 3 contains annual homicide counts and rates for various comparison sites including Liverpool, London, Kent, Canada, Baltimore, Los Angeles, Seattle, and San Francisco.

Measuring homicides across the world helps us understand violent crime and how people are affected by interpersonal violence.

But measuring homicides is challenging. Even homicide researchers do not always agree on whether the specific cause of death should be considered a homicide. Even when they agree on what counts as a homicide, it is difficult to count all of them.

In many countries, national civil registries do not certify most deaths or their cause. Besides lacking funds and personnel, a body has to be found to determine whether a death has happened. Authorities may also struggle to distinguish a homicide from a similar cause of death, such as an accident.

Law enforcement and criminal justice agencies collect more data on whether a death was unlawful — but their definition of unlawfulness may differ across countries and time.

Estimating homicides where neither of these sources is available or good enough is difficult. Estimates rely on inferences from similar countries and contextual factors that are based on strong assumptions. So how do researchers address these challenges and measure homicides?

In our work on homicides, we provide data from five main sources:

The WHO Mortality Database (WHO-MD)1 The Global Study on Homicide by the UN Office on Drugs and Crime (UNODC)2 The History of Homicide Database by Manuel Eisner (20033 and 20144) The Global Burden of Disease (GBD) study by the Institute for Health Metrics and Evaluation (IHME)5 The WHO Global Health Estimates (WHO-GHE)6 These sources all report homicides, cover many countries and years, and are frequently used by researchers and policymakers. They are not entirely separate, as they partially build upon each other.

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.

Historical dataset showing Taiwan murder/homicide rate per 100K population by year from N/A to N/A.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

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.

This study focused on the effect of economic resources and racial/ethnic composition on the change in crime rates from 1970-2004 in United States cities in metropolitan areas that experienced a large growth in population after World War II. A total of 352 cities in the following United States metropolitan areas were selected for this study: Atlanta, Dallas, Denver, Houston, Las Vegas, Miami, Orange County, Orlando, Phoenix, Riverside, San Bernardino, San Diego, Silicon Valley (Santa Clara), and Tampa/St. Petersburg. Selection was based on the fact that these areas developed during a similar time period and followed comparable development trajectories. In particular, these 14 areas, known as the "boomburbs" for their dramatic, post-World War II population growth, all faced issues relating to the rapid growth of tract-style housing and the subsequent development of low density, urban sprawls. The study combined place-level data obtained from the United States Census with crime data from the Uniform Crime Reports for five categories of Type I crimes: aggravated assaults, robberies, murders, burglaries, and motor vehicle thefts. The dataset contains a total of 247 variables pertaining to crime, economic resources, and race/ethnic composition.

The World Crime Index 2023 dataset provides records of crime rankings for cities worldwide, along with associated information on their respective countries. This dataset is focused on the year 2023 and includes the following columns:

• Rank: The ranking of a city based on its crime index, with lower numbers indicating higher crime rates.
• City: The name of the city for which crime data is reported.
• Country: The country to which the city belongs.
• Crime Index: A numerical value representing the overall level of crime in a city, with higher values indicating higher crime rates.
• Safety Index: An index indicating the general level of safety in a city, derived from factors such as infrastructure, healthcare, and political stability.

This dataset enables data scientists to analyze and compare crime rankings across cities and countries, providing insights into the relative safety levels of different locations in the year 2023. By leveraging this dataset, researchers can conduct exploratory data analysis, perform comparative studies, and identify potential trends and patterns in crime rates globally for the specified year.

In a world of increasing crime, many organizations are interested in examining incident details to learn from and prevent future crime. Our client, based in Los Angeles County, was interested in this exact thing. They asked us to examine the data to answer several questions; among them,
1 .what was the rate of increase or decrease in crime from 2020 to 2023, 2. which ethnicity or group of people were targeted the most

The problem involves scrapping two websites (Life Quality and Crime Rate) for collecting life quality and crime rate data, merging them by country name and conducting EDA on Tabealu for finging insights. For details find : https://github.com/NifulIslam/Life-Quality-and-Crime-Rate-Scrapping-and-EDA

organizations are becoming increasingly concerned with analyzing crime data to prevent future incidents and better understand the factors driving criminal behavior. One such organization, based in Los Angeles County, approached us with a request to conduct a comprehensive analysis of crime data in their area. They aimed to uncover key trends and insights that could inform strategies to enhance public safety and reduce the likelihood of future crimes.

The client's main focus was to assess how crime rates have evolved over time, particularly from 2020 to 2023, a period marked by numerous global and local challenges, including the COVID-19 pandemic and economic disruptions. They wanted to understand whether crime rates had increased, decreased, or remained stable during these years and what factors might have contributed to any observed changes. By examining the trends over this period, they hoped to identify patterns that could guide more effective crime prevention strategies in the future.

Another critical question the client posed was regarding the demographics of crime victims. Specifically, they sought to determine which ethnic groups or communities were most frequently targeted in criminal incidents. In an increasingly diverse city like Los Angeles, this information is essential for understanding whether certain populations are disproportionately affected by crime and, if so, why this might be the case. Identifying these patterns could allow for more targeted interventions to protect vulnerable communities and address underlying social or economic inequalities contributing to their higher victimization rates.

To address these questions, our analysis would involve a detailed examination of crime data from multiple sources, including law enforcement records and publicly available datasets. We would analyze the overall trends in crime rates over the specified time frame, breaking them down by type of crime, geographic location, and other relevant factors. Additionally, we would focus on victim demographics, including race, ethnicity, age, and gender, to determine if there were any noticeable disparities in victimization rates across different groups.

The results of this analysis would provide valuable insights not only for the client but also for policymakers, law enforcement agencies, and community organizations. By understanding the rate of increase or decrease in crime over recent years, we can gain a clearer picture of how crime is evolving and whether current measures are effective in curbing it. Furthermore, identifying which ethnic groups or populations are most affected by crime allows for a more equitable approach to crime prevention and public safety initiatives.

In conclusion, this analysis would serve as a crucial tool for the client and other stakeholders in Los Angeles County, helping them to make data-driven decisions that improve public safety and reduce the risk of future crimes. By focusing on both the overall trends and the specific demographics of crime victims, this comprehensive approach aims to address the root causes of crime and foster a safer, more inclusive community.

Jordan JO: Intentional Homicides: Male: per 100,000 Male data was reported at 2.275 Ratio in 2016. This records a decrease from the previous number of 3.348 Ratio for 2012. Jordan JO: Intentional Homicides: Male: per 100,000 Male data is updated yearly, averaging 2.609 Ratio from Dec 2007 (Median) to 2016, with 7 observations. The data reached an all-time high of 3.348 Ratio in 2012 and a record low of 1.893 Ratio in 2009. Jordan JO: Intentional Homicides: Male: per 100,000 Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Health Statistics. Intentional homicides, male are estimates of unlawful male 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.; ;

Dataset

This dataset was created by Noah Weber

Released under CC0: Public Domain

Contents

https://archive.ics.uci.edu/ml/index.php