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.

🌍 Dataset Description

This dataset provides detailed information about criminal incidents, capturing various characteristics of both the offenders and victims. It includes records of crimes along with demographic details such as age, gender, race, and the status of the individuals involved. The data also contains information on the disposition of the case (whether it was closed or open) and the nature of the crime.

The dataset covers a wide range of crime categories such as theft, vandalism, violence, sexual crimes, and drug/weapon-related offenses. This allows for an in-depth analysis of criminal activities, their impact on different demographics, and potential correlations between various factors such as age, gender, and the type of crime committed.

📊 Columns:

• Disposition: The current status of the case (Closed or Open).
• OffenderStatus: The status of the offender (e.g., ARRESTED).
• Offender_Race: The race of the offender (e.g., BLACK, WHITE, ASIAN, etc.).
• Offender_Gender: The gender of the offender (MALE or FEMALE).
• Offender_Age: The age of the offender (numerical value).
• PersonType: Type of person involved in the case (e.g., VICTIM, REPORTING PERSON, INTERVIEW).
• Victim_Race: The race of the victim (e.g., BLACK, WHITE, ASIAN, etc.).
• Victim_Gender: The gender of the victim (MALE or FEMALE).
• Victim_Age: The age of the victim (numerical value).
• Victim_Fatal_Status: Indicates if the victim’s injuries were fatal or non-fatal.
• Report Type: Type of report filed (e.g., Supplemental Report, Incident Report).
• Category: The category of crime (e.g., Theft, Vandalism, Violence, etc.).

🔍 Use Cases:

This dataset is ideal for analyzing criminal incidents, studying the relationship between various demographic factors and crime types, and performing predictive modeling for crime occurrence. It is useful for investigating crime patterns and trends, assessing how crime impacts different groups, and can assist in law enforcement resource allocation and policy-making. The data can also be utilized in machine learning applications to classify or predict crime outcomes based on offender and victim details.

Intimate partner and non-intimate partner victims of police-reported violent crime and traffic offences causing bodily harm or death, by age and gender of victim c (1, 2)Frequency: AnnualTable: 35-10-0202-01Release date: 2023-11-21Geography: Canada, Province or territory, Census metropolitan area, Census metropolitan area partTable Corrections: Date Note On December 10, 2021, the 2019 and 2020 data were revised as a result of corrections made to the populations used to calculate rates per 100 000 population. Footnotes: 1 In general, for police-reported incidents that involve violations against the person, a victim record is collected for each victim involved in the incident. If an individual is a victim in multiple incidents in the same reference year, that individual will be counted as one victim for each incident. Some victims experience violence over a period of time, sometimes years, all of which may be considered by the police to be part of one continuous incident and are counted as a single victim. Victim records are not required for all violent violations, but are accepted, for some violent offences such as uttering threats and criminal harassment. 2 Data are from the Uniform Crime Reporting (UCR2) Trend Database, which contains historical data that permit the analysis of trends since 2009 in the characteristics of incidents, and accused and victim characteristics, such as age, gender and accused–victim relationship. This database includes respondents accounting for 99% of the population of Canada. 3 A census metropolitan area (CMA) consists of one or more neighbouring municipalities situated around a major urban core. A CMA must have a total population of at least 100,000, of which 50,000 or more live in the urban core. To be included in the CMA, other adjacent municipalities must have a high degree of integration with the central urban core, as measured by commuting flows derived from census data. A CMA typically comprises more than one police service. CMA populations have been adjusted to follow policing boundaries. The Oshawa CMA is excluded from this analysis owing to the incongruity between the police service jurisdictional boundaries and the CMA boundaries. Belleville and Lethbridge became CMAs as of the 2016 Census. In 2022, coverage for each CMA was virtually 100%, except in Toronto (90%) and Hamilton (75%). As a result, counts and rates may differ from information from other sources. 4 Victim age is calculated based on the end date of an incident, as reported by the police. Some victims experience violence over a period of time, sometimes years, all of which may be considered by the police to be part of one continuous incident. 5 Excludes the portion of Halton Regional Police Service that polices the Hamilton census metropolitan area. As a result, counts and rates may differ from information from other sources. 6 The category “age of victim unknown” includes victims whose ages were reported as 80 years and older, but were identified as possible instances of miscoding, as well as victims in Quebec whose ages were unknown but were miscoded as 0. 7 Rates are calculated on the basis of 100,000 population in each age and gender group unless otherwise noted for specific relationships. Populations based on July 1 estimates from Statistics Canada, Centre for Demography. Rates for victims with unknown age or unknown gender are not available for any reference period, as population estimates cannot be applied to calculate rates where these elements are unknown. 9 The option for police to code victims as non-binary in the Uniform Crime Reporting (UCR) Survey was implemented in 2018. Given that small counts of victims identified as “non-binary” may exist, the UCR aggregate data available to the public have been recoded to assign these counts to either “male” or “female,” in order to ensure the protection of confidentiality and privacy. Victims identified as non-binary have been assigned to either male or female based on the regional distribution of victims’ gender. 8 Includes victims aged 15 years and older who were victimized by current and former legally married spouses and common-law partners. Also includes victims aged 12 years and older of current and former boyfriends and girlfriends and other intimate relationships (i.e., those with whom they had a sexual relationship but for which none of the other relationship categories apply). Spousal violence victims under the age of 15 years are included in the relationship category “other family.” Victims of non-spousal intimate partner violence under the age of 12 years are included in the relationship category “unknown relationship.” Rates for total victims are based on populations aged 12 years and older. Rates for other victim age groups are calculated on the basis of their corresponding age group populations.

Separations and men's violence against women is a study that aims to increase knowledge about the connection between men's violence against women and separations. The study is a thesis at the University of Gothenburg and has been carried out with financial support from the Crime Victims Fund. The study is based on questionnaires collected from over 350 women who have separated or gone through a divorce from a heterosexual relationship. In order to get in touch with these women, the researcher has worked together with various social agencies that women who have been subjected to separation violence can come into contact with; police, women's shelters, crime victim shelters, social services and family law agencies. Questionnaires have also been sent directly to women in the Gothenburg area who have gone through a divorce.

The study includes two questionnaires. Questionnaire 2 was sent after 6 months to those women who expressed interest in participating further via Questionnaire 1. The questionnaires are designed to cover different periods of the relationship and separation. They include questions about the man's behavior in certain situations, for example during discussions about joint children or assets, how conflicts have been handled, physical, psychological and sexual violence and the relationship as a whole.

The results of the study show that 60 percent of the women respondents have at some point been subjected to physical violence by the man they separated from. One purpose of the study is to investigate whether it is possible to predict the risk of violence after a separation. The study shows that the risk assessment model, to assess how great the risk is that the woman will be subjected to violence even after the separation, which is used by staff at women's shelters and crime victim shelters, is preferable to the police's way of working. It also shows that negotiations in family law are affected if the man has used violence. It often turns out that the woman lowers her demands, for example regarding custody of joint children, if the man has used violence against her.

Purpose: To investigate the extent of men's violence against women in connection with and after separations, separation violence, in order to be able to say something about the risk of violence that women who are considering leaving a heterosexual relationship face.

The purpose of this study was to use data from the National Crime Survey (NCS) and the National Crime Victimization Survey (NCVS) to explore whether the likelihood of police notification by rape victims had increased between 1973-2000. To avoid the ambiguities that could arise in analyses across the two survey periods, the researchers analyzed the NCS (1973-1991) and NCVS data (1992-2000) separately. They focused on incidents that involved a female victim and one or more male offenders. The sample for 1973-1991 included 1,609 rapes and the corresponding sample for 1992-2000 contained 636 rapes. In their analyses, the researchers controlled for changes in forms of interviewing used in the NCS and NCVS. Logistic regression was used to estimate effects on the measures of police notification. The analyses incorporated the currently best available methods of accounting for design effects in the NCS and NCVS. Police notification served as the dependent variable in the study and was measured in two ways. First, the analysis included a polytomous dependent variable that contrasted victim reported incidents and third-party reported incidents, respectively, with nonreported incidents. Second, a binary dependent variable, police notified, also was included. The primary independent variables in the analysis were the year of occurrence of the incident reported by the victim and the relationship between the victim and the offender. The regression models estimated included several control variables, including measures of respondents' socioeconomic status, as well as other victim, offender, and incident characteristics that may be related both to the nature of rape and to the likelihood that victims notify the police.

This dataset contains the tweet ids of 407,911 tweets, including tweets between October 1, 2021 and December 31, 2021. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.

This dataset contains the tweet ids of 464,168 tweets, including tweets between April 1, 2021 and June 30, 2021. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.

This dataset contains the tweet ids of 1,482,343 tweets with the hashtag #believesurvivors. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection, and contains tweets published between October 15, 2017 and March 31, 2020.Tweets between October 15, 2017 and December 10, 2018 were licensed from Twitter's Historical PowerTrack and received through GNIP. Tweets after December 10, 2018 were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that this is VERSION 1 of the dataset. New versions with updated data will be submitted at the end of each quarter.Because of the size of the files, the list of identifiers are split in 2 files containing 1,000,000 ids each.Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets still available (not deleted by users) tools like Hydrator are availableThere are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available in this dataverse

This dataset contains the tweet ids of 24,443,707 tweets with the hashtag #metoo. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection, and contains tweets published between October 15, 2017 and March 31, 2020.Tweets between October 15, 2017 and December 10, 2018 were licensed from Twitter's Historical PowerTrack and received through GNIP. Tweets after December 10, 2018 were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that this is VERSION 1 of the dataset. New versions with updated data will be submitted at the end of each quarter.Because of the size of the files, the list of identifiers are split in 25 files containing 1,000,000 ids each.Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets still available (not deleted by users) tools like Hydrator are availableThere are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available in this dataverse

This dataset contains the tweet ids of 765,260 tweets, including tweets between January 1, 2021 and March 31, 2021. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.

This dataset contains the tweet ids of 526,073 tweets, including tweets between July 1, 2021 and September 30, 2021. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.These tweets were collected weekly from the Twitter API through Social Feed Manager using the POST statuses/filter method of the Twitter Stream API.Please note that there will be no updates to this dataset.The following list of terms includes the hashtags used to collect data for this dataset: #metoo, #timesup, #metoostem, #sciencetoo, #metoophd, #shittymediamen, #churchtoo, #ustoo, #metooMVMT, #ARmetoo, #TimesUpAR, #metooSociology, #metooSexScience, #timesupAcademia, and #metooMedicine.Be aware that previous quarters (up to the first quarter of 2020) only include one hashtag: #metoo.Per Twitter's Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets that are still available (not deleted by users) tools like Hydrator are available.There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available by quarter, as well as a full dataset with a larger corpus of hashtags.