This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

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.

These data on 19th- and early 20th-century police department and arrest behavior were collected between 1975 and 1978 for a study of police and crime in the United States. Raw and aggregated time-series data are presented in Parts 1 and 3 on 23 American cities for most years during the period 1860-1920. The data were drawn from annual reports of police departments found in the Library of Congress or in newspapers and legislative reports located elsewhere. Variables in Part 1, for which the city is the unit of analysis, include arrests for drunkenness, conditional offenses and homicides, persons dismissed or held, police personnel, and population. Part 3 aggregates the data by year and reports some of these variables on a per capita basis, using a linear interpolation from the last decennial census to estimate population. Part 2 contains data for 267 United States cities for the period 1880-1890 and was generated from the 1880 federal census volume, REPORT ON THE DEFECTIVE, DEPENDENT, AND DELINQUENT CLASSES, published in 1888, and from the 1890 federal census volume, SOCIAL STATISTICS OF CITIES. Information includes police personnel and expenditures, arrests, persons held overnight, trains entering town, and population.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

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.

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.

Serious violent crimes consist of Part 1 offenses as defined by the U.S. Department of Justice’s Uniform Reporting Statistics. These include murders, nonnegligent homicides, rapes (legacy and revised), robberies, and aggravated assaults. LAPD data were used for City of Los Angeles, LASD data were used for unincorporated areas and cities that contract with LASD for law enforcement services, and CA Attorney General data were used for all other cities with local police departments. This indicator is based on location of residence. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Neighborhood violence and crime can have a harmful impact on all members of a community. Living in communities with high rates of violence and crime not only exposes residents to a greater personal risk of injury or death, but it can also render individuals more susceptible to many adverse health outcomes. People who are regularly exposed to violence and crime are more likely to suffer from chronic stress, depression, anxiety, and other mental health conditions. They are also less likely to be able to use their parks and neighborhoods for recreation and physical activity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.

This data set contains count data on domestic violence homicide and intimate partner homicide incidents from the 2015 the Federal Bureau of Investigation (FBI) Supplemental Homicide Report (SHR) and population from the SHR.

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.

This dataset reflects reported incidents of crime that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. Any use of the information for commercial purposes is strictly prohibited. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily.

This table contains data on the rate of violent crime (crimes per 1,000 population) for California, its regions, counties, cities and towns. Crime and population data are from the Federal Bureau of Investigations, Uniform Crime Reports. Rates above the city/town level include data from city, university and college, county, state, tribal, and federal law enforcement agencies. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Ten percent of all deaths in young California adults aged 15-44 years are related to assault and homicide. In 2010, California law enforcement agencies reported 1,809 murders, 8,331 rapes, and over 95,000 aggravated assaults. African Americans in California are 11 times more likely to die of assault and homicide than Whites. More information about the data table and a data dictionary can be found in the About/Attachments section.

***Starting on March 7th, 2024, the Los Angeles Police Department (LAPD) will adopt a new Records Management System for reporting crimes and arrests. This new system is being implemented to comply with the FBI's mandate to collect NIBRS-only data (NIBRS — FBI - https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs). During this transition, users will temporarily see only incidents reported in the retiring system. However, the LAPD is actively working on generating new NIBRS datasets to ensure a smoother and more efficient reporting system. *** **Update 1/18/2024 - LAPD is facing issues with posting the Crime data, but we are taking immediate action to resolve the problem. We understand the importance of providing reliable and up-to-date information and are committed to delivering it. As we work through the issues, we have temporarily reduced our updates from weekly to bi-weekly to ensure that we provide accurate information. Our team is actively working to identify and resolve these issues promptly. We apologize for any inconvenience this may cause and appreciate your understanding. Rest assured, we are doing everything we can to fix the problem and get back to providing weekly updates as soon as possible. ** This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

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

This indicator is based on location of residence. Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD 10 codes used to identify homicides are X85-Y09, Y87.1, and U01-U02. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Violence is a public health crisis in the US, with gun violence being a major driver. Almost three quarters of homicides involve firearms. In the US, the age-adjusted homicide rate from firearms is more than 20 times higher than in the European Union or in Australia. Significant disparities by age, sex, and race and ethnicity exist, with young adults ages 15-34 years, males, and Black individuals most disproportionately impacted. Comprehensive prevention strategies should work to address the underlying physical, social, economic, and structural conditions known to increase risk.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited.

The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.

Context

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.

Today, four annual publications, Crime in the United States, National Incident-Based Reporting System, Law Enforcement Officers Killed and Assaulted, and Hate Crime Statistics are produced from data received from over 18,000 city, university/college, county, state, tribal, and federal law enforcement agencies voluntarily participating in the program. The crime data are submitted either through a state UCR Program or directly to the FBI’s UCR Program.

This dataset focuses on the crime rates and law enforcement employment data in the state of California.

Content

Crime and law enforcement employment rates are separated into individual files, focusing on offenses by enforcement agency, college/university campus, county, and city. Categories of crimes reported include violent crime, murder and nonnegligent manslaughter, rape, robbery, aggravated assault, property crime, burglary, larceny-theft, motor vehicle damage, and arson. In the case of rape, data is collected for both revised and legacy definitions. In some cases, a small number of enforcement agencies switched definition collection sometime within the same year.

Acknowledgements

This dataset originates from the FBI UCR project, and the complete dataset for all 2015 crime reports can be found here.

Inspiration

• What are the most common types of crimes in California? Are there certain crimes that are more common in a particular place category, such as a college/university campus, compared to the rest of the state?
• How does the number of law enforcement officers compare to the crime rates of a particular area? Is the ratio similar throughout the state, or do certain campuses, counties, or cities have a differing rate?
• How does the legacy vs. refined definition of rape differ, and how do the rape counts compare? If you pulled the same data from FBI datasets for previous years, can you see a difference in rape rates over time?

This dataset includes selected variables and cases from the Federal Bureau of Investigation's Uniform Crime Reports, 1958-1969, and the County and City Data Books for 1962, 1967, and 1972. Data are reported for all United States cities with a population of 75,000 or more in 1960. Data from the Uniform Crime Reports include for each year the number of homicides, forcible rapes, robberies, aggravated assaults, burglaries, larcenies over 50 dollars, and auto thefts. Also included is the Total Crime Index, which is the simple sum of all the crimes listed above. Selected variables describing population characteristics and city finances were taken from the 1962, 1967, and 1972 County and City Data Books.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.