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.

Los Angeles Crime Data (2020 - Present)

📌 Updated: February 7, 2025

Overview

This dataset contains reported crime incidents in the City of Los Angeles from 2020 to the present, provided by the Los Angeles Police Department (LAPD). It includes key details such as crime type, location (anonymized), and date. The dataset is derived from official LAPD records and is regularly updated.

⚠️ Note: LAPD transitioned to a new Records Management System (RMS) on March 7, 2024, to comply with the FBI’s NIBRS (National Incident-Based Reporting System). During this transition, some crime data may still reflect the older system.

Dataset Highlights

✔ Crime Incidents: Reported cases from 2020 onwards ✔ Location Details: Anonymized to the nearest hundred block ✔ Reporting System: Transition to FBI's NIBRS compliance ✔ Data Accuracy: Transcribed from original LAPD reports

Important Notes

🔹 Temporary Reporting Delays – LAPD is experiencing technical issues affecting data updates. Until resolved, updates will be bi-weekly instead of weekly. 🔹 Data Limitations – Some missing location fields are recorded as (0°, 0°) due to privacy constraints. 🔹 Possible Inaccuracies – Crime reports are transcribed manually, leading to potential data errors.

Usage & Applications

✅ Crime trend analysis over time ✅ Crime hotspot detection & mapping ✅ Law enforcement and policy research ✅ Machine learning applications (predictive modeling)

Column Descriptions for Los Angeles Crime Data (2020 - Present)

Column Name and Description

DR_NO: Unique crime report number assigned by LAPD. Date Rptd: Date when the crime was reported to the LAPD (MM/DD/YYYY HH:MM:SS AM/PM). DATE OCC: Date when the crime occurred (MM/DD/YYYY HH:MM:SS AM/PM). TIME OCC: Time when the crime occurred, in 24-hour format (e.g., 2130 = 9:30 PM). AREA: Numerical code representing the LAPD division where the crime occurred. AREA NAME: Name of the LAPD division (e.g., Wilshire, Central, Southwest, etc.). Rpt Dist No: Reporting district number used internally by LAPD. Part 1-2: Crime category: 1 = Serious (violent/property crimes), 2 = Less serious crimes. Crm Cd: Crime classification code assigned by LAPD. Crm Cd Desc: Description of the crime, such as "Vehicle - Stolen" or "Burglary from Vehicle". Mocodes: Modus Operandi (MO) codes, which indicate methods used by criminals. Vict Age: Age of the victim (0 may indicate missing data). Vict Sex: Gender of the victim (M = Male, F = Female, X = Unknown). Vict Descent: Ethnicity of the victim, encoded as: W (White), B (Black), H (Hispanic), A (Asian), O (Other), etc. Premis Cd: Numerical code representing the type of location where the crime occurred. Premis Desc: Description of the location, such as "Street," "Bus Stop," "Apartment," etc. Weapon Used Cd: Weapon code, if a weapon was used in the crime (NaN if no weapon was involved). Weapon Desc: Description of the weapon (e.g., "Handgun", "Knife", "None"). Status: Case status, such as IC (Investigation Continued) or AA (Adult Arrest). Status Desc: Description of the case status, e.g., "Investigation Continued" or "Adult Arrest". Crm Cd 1 - Crm Cd 4: Additional crime codes, if multiple offenses occurred in the same incident. LOCATION: Nearest street address where the crime occurred. Cross Street: Cross street (if available) for additional location context. LAT Latitude: of the crime location. LON Longitude: of the crime location.

License & Attribution

Source: Los Angeles Police Department (LAPD) Terms of Use: This dataset follows specific non-federal licensing rules different from Data.gov. Attribution: If you use this dataset, please credit LAPD & Data.gov.

💬 Feedback & Discussion

If you notice any inconsistencies or have questions, please leave a comment below. Let's collaborate to improve crime data transparency! 🚀

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.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 15 release notes:Adds 2021 data.Version 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "p

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.

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 contains the tweet ids of 1,360,764 tweets, including tweets between April 1, 2020 and June 30, 2020. 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.Because of the size of the files, the list of identifiers are split into 2 files containing up to 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 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.