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.

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 that spans two centuries on homicides per capita for the city of Los Angeles. 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. 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. Data were derived from multiple sources, including Los Angeles court records, as well as annual reports of the coroner and daily newspapers. Part 1 (Annual Homicides and Related Data) variables include Los Angeles County annual counts of homicides, counts of female victims, method of killing such as drowning, suffocating, or strangling, and the homicide rate. Part 2 (Individual Homicide Data) variables include the date and place of the murder, the age, sex, race, and place of birth of the offender and victim, type of weapon used, and source of data.

Comprehensive crime statistics for Los Angeles County including homicides, property crime, robbery, assault, and neighborhood-by-neighborhood breakdowns with five-year trend analysis.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Los Angeles County, CA (DISCONTINUED) (FBITC006037) from 2004 to 2020 about crime; violent crime; property crime; Los Angeles County, CA; Los Angeles; CA; and USA.

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, what was the rate of increase or decrease in crime from 2020 to 2023, and which ethnicity or group of people were targeted the most.

Our data was collected from Kaggle.com at the following link:

https://www.kaggle.com/datasets/nathaniellybrand/los-angeles-crime-dataset-2020-present

It was cleaned, examined for further errors, and the analysis performed using RStudio. The results of this analysis are in the attached PDF entitled: "crime_data_analysis_report." Please feel free to review the results as well as follow along with the dataset on your own machine.

Comprehensive crime statistics for Los Angeles County's safest neighborhoods including violent crime rates, property crime rates, and annual victimization chances by neighborhood for 2024-2025.

The purpose of this data collection was to investigate the effects of crime rates, city characteristics, and police departments' financial resources on felony case attrition rates in 28 cities located in Los Angeles County, California. Demographic data for this collection were obtained from the 1983 COUNTY AND CITY DATA BOOK. Arrest data were collected directly from the 1980 and 1981 CALIFORNIA OFFENDER BASED TRANSACTION STATISTICS (OBTS) data files maintained by the California Bureau of Criminal Statistics. City demographic variables include total population, minority population, population aged 65 years or older, number of female-headed families, number of index crimes, number of families below the poverty level, city expenditures, and police expenditures. City arrest data include information on number of arrests disposed and number of males, females, blacks, and whites arrested. Also included are data on the number of cases released by police, denied by prosecutors, and acquitted, and data on the number of convicted cases given prison terms.

Comprehensive crime statistics for Los Angeles County's most dangerous neighborhoods including violent crime rates, property crime rates, gang activity, and annual victimization chances by neighborhood for 2024-2025.

This study was conducted in 1979 at the Social Science Research Institute, University of Southern California, and explores the relationship between neighborhood change and crime rates between the years 1950 and 1976. The data were aggregated by unique and consistently-defined spatial areas, referred to as dummy tracts or neighborhoods, within Los Angeles County. By combining United States Census data and administrative data from several state, county, and local agencies, the researchers were able to develop measures that tapped the changing structural and compositional aspects of each neighborhood and their interaction with the patterns of juvenile delinquency. Some of the variables included are annual income, home environment, number of crimes against persons, and number of property crimes.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Los Angeles County, CA was 21159.00000 Known Incidents in January of 2020, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Los Angeles County, CA reached a record high of 28300.00000 in January of 2007 and a record low of 20493.00000 in January of 2014. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Los Angeles County, CA - last updated from the United States Federal Reserve on October of 2025.

Percent of adults (18+ years old) who reported considering their neighborhood to be safe from crime Data Source: 2011 & 2015 Los Angeles County Health Survey; Office of Health Assessment and Epidemiology, Los Angeles County Department of Public Health. FAQS 1) What is the Los Angeles County Health Survey (LACHS)? The Los Angeles County Health Survey is a population based telephone survey that provides information concerning the health of Los Angeles County residents. The data are used for assessing health-related needs of the population, for program planning and policy development, and for program evaluation. The relatively large sample size allows users to obtain health indicator data for large demographic subgroups and across geographic regions of the County, including Service Planning Areas and Health Districts. Produced by Los Angeles County Department of Public Health, Office of Health Assessment and Epidemiology (OHAE) www.publichealth.lacounty.gov/ha 2) What are the sample sizes of the 2011 and 2015 LACHS? Estimates are based on self-reported data by random samples of 8,036 (from 2011 survey) and 8,008 (from 2015 survey) Los Angeles County adults, representative of the adult population in Los Angeles County. 3) What does the 95% CI mean? The 95% confidence intervals (CI) represent the variability in the estimate due to sampling; the actual prevalence in the population, 95 out of 100 times sampled, would fall within the range provided. 4) What is the prevalence and confidence intervals (CIs) for Los Angeles County and Los Angeles City? Findings for the County of Los Angeles: (84.1%; 95% CI=81.8-86.5)Findings for the City of Los Angeles: (79.9%; 95% CI=75.9-84.0) Note:For purposes of confidentiality, Community Plan Area results with cell sizes less than 5 are not reported and are excluded from the map display. "Field Name" = Field Definition “CPA_NUM” = Unique identifier for each Community Plan Area "NAME_ALF" = the 35 Community Plan Areas, LAX Plan Area, and the Port of Los Angeles Plan Area "Percent" = percentage of adults (18+ years old) whose reported considering their neighborhood to be safe from crime "Stable_est" = (Yes) the estimate is statistically stable (relative standard error ≤ 30%) (No) the estimate is statistically unstable (relative standard error >30%) and therefore may not be appropriate to use for planning or policy purposes "LowerCL" = the lower 95% confidence limit represents the lower margin of error that occurs with statistical sampling "UpperCL" = the upper 95% confidence limit represents the upper margin of error that occurs in statistical sampling

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Orange County, CA (DISCONTINUED) (FBITC006059) from 2004 to 2020 about Orange County, CA; crime; violent crime; property crime; Los Angeles; CA; and USA.

This study used a mixed-methods approach to pursue five interrelated objectives: (1) to document the extent of case attrition and to identify the stages of the criminal justice process where attrition is most likely to occur; (2) to identify the case complexities and evidentiary factors that affect the likelihood of attrition in sexual assault cases; (3) to identify the predictors of case outcomes in sexual assault cases; (4) to provide a comprehensive analysis of the factors that lead police to unfound the charges in sexual assault cases; and (5) to identify the situations in which sexual assault cases are being cleared by exceptional means. Toward this end, three primary data sources were used: (1) quantitative data on the outcomes of sexual assaults reported to the Los Angeles Police Department (LAPD) and the Los Angeles County Sheriff's Department (LASD) from 2005 to 2009, (2) qualitative data from interviews with detectives and with deputy district attorneys with the Los Angeles District Attorney's Office who handled sexual assault cases during this time period, and (3) detailed quantitative and qualitative data from case files for a sample of cases reported to the two agencies in 2008.

The complete case files for sexual assaults that were reported to the Los Angeles Police Department and the Los Angeles County Sheriff's Department in 2008 were obtained by members of the research team and very detailed information (quantitative and qualitative data) was extracted from the files on each case in Dataset 1 (Case Outcomes and Characteristics: Reports from 2008). The case file included the crime report prepared by the patrol officer who responded to the crime and took the initial report from the complainant, all follow-up reports prepared by the detective to whom the case was assigned for investigation, and the detective's reasons for unfounding the report or for clearing the case by arrest or by exceptional means. The case files also included either verbatim accounts or summaries of statements made by the complainant, by witnesses (if any), and by the suspect (if the suspect was interviewed); a description of physical evidence recovered from the alleged crime scene, and the results of the physical exam (Sexual Assault Response Team (SART) exam) of the victim (if the victim reported the crime within 72 hours of the alleged assault). Members of the research team read through each case file and recorded data in an SPSS data file. There are 650 cases and 261 variables in the data file. The variables in the data file include administrative police information and charges listed on the police report. There is also information related to the victim, the suspect, and the case.

Datasets 2-5 were obtained from the district attorney's office and contain outcome data that resulted in the arrest of a suspect. The outcome data obtained from the agency was for the following sex crimes: rape, attempted rape, sexual penetration with a foreign object, oral copulation, sodomy, unlawful sex, and sexual battery.

Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) is a subset of Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) contains 10,832 cases and 29 variables. Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) contains 891 cases and 45 variables.

Similarly, Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) is a subset of Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) contains 3,309 cases and 33 variables. Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) contains 904 cases and 47 variables.

In 2022, the New Orleans-Metairie, LA metro area recorded the highest homicide rate of U.S. cities with a population over 250,000, at **** homicides per 100,000 residents, followed by the Memphis, TN-MS-AR metro area. However, homicide data was not recorded in all U.S. metro areas, meaning that there may be some cities with a higher homicide rate. St. Louis St. Louis, which had a murder and nonnegligent manslaughter rate of **** in 2022, is the second-largest city by population in Missouri. It is home to many famous treasures, such as the St. Louis Cardinals baseball team, Washington University in St. Louis, the Saint Louis Zoo, and the renowned Gateway Arch. It is also home to many corporations, such as Monsanto, Arch Coal, and Emerson Electric. The economy of St. Louis is centered around business and healthcare, and boasts ten Fortune 500 companies. Crime in St. Louis Despite all of this, St. Louis suffers from high levels of crime and violence. As of 2023, it was listed as the seventh most dangerous city in the world as a result of their extremely high murder rate. Not only does St. Louis have one of the highest homicide rates in the United States, it also reports one of the highest numbers of violent crimes. Despite high crime levels, the GDP of the St. Louis metropolitan area has been increasing since 2001.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates).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.

By 1996 it became apparent that the Los Angeles county jails faced a serious overcrowding problem. Two possible solutions to the problem were to build more jail capacity or to divert a greater number of incoming inmates to community-based, intermediate sanctions. The research team for this study was asked to review a 1996 profile of inmates in the Los Angeles jail system and to determine how many of them might have been good candidates for intermediate sanctions such as electronic monitoring, work release, house arrest, and intensive supervision. The researchers selected a sample of 1,000 pre-adjudicated (or unconvicted) inmates from the total census of inmates in jail custody on January 15, 1996, to study in more detail. Of the 1,000 offenders, the researchers were able to obtain jail and recidivism data for two years for 931 inmates. For each of these offenders, information on their prior criminal history, current offense, and subsequent recidivism behavior was obtained from official records maintained by several county agencies, including pretrial services, sheriff's department, probation, and courts. Demographic variables include date of birth, race, and gender. Prior criminal history variables for each prior adult arrest include type of filing charge, case disposition, type of sentence and sentence length imposed, and total number of prior juvenile petitions sustained. Current offense variables include arrest date, crime type for current arrest, crime charge, type and date of final case disposition, and sentence type and length, if convicted. Strike information collected includes number of strikes and the offense that qualified as a strike. Jail custody variables include the jail entry and exit data for the current offense and the reason for release, if released. Lastly, two-year follow-up variables include the date, type, and disposition of each subsequent arrest between January 15, 1996, and January 15, 1998.

Part 1 crimes, as defined by the Federal Bureau of Investigation (FBI), are:

Criminal Homicide Forcible Rape Robbery Aggravated Assault Burglary Larceny Theft Grand Theft Auto Arson

Part 2 crimes, as defined by the Federal Bureau of Investigation (FBI), are:

Forgery Fraud And NSF Checks Sex Offenses Felonies Sex Offenses Misdemeanors Non-Aggravated Assaults Weapon Laws Offenses Against Family Narcotics Liquor Laws Drunk / Alcohol / Drugs Disorderly Conduct Vagrancy Gambling Drunk Driving Vehicle / Boat Vehicle / Boating Laws Vandalism Warrants Receiving Stolen Property Federal Offenses without Money Federal Offenses with Money Felonies Miscellaneous Misdemeanors Miscellaneous

Note About Date Fields:By default, the cloud database assumes all date fields are provided in UTC time zone. As a result, the system attempts to convert to the local time zone in your browser resulting in dates that appear differently than the source file. For example, a user viewing the data in PST will see times that are 8 hours behind. For an example of how dates are displayed, see the example below: Source & Download File Online Database Table Display (Example for PST User)

3/18/2023 8:07:00 AM PST 3/18/2023 8:07:00 AM UTC 3/18/2023 12:07:00 AM DATA DICTIONARY:

Field Name
Field Description


LURN_SAK
System assigned number for the case


Incident Date
Date the crime incident occurred


Incident Reported Date
Date the crime was reported to LASD


Category
Incident crime category


Stat Code
A three digit numerical coding system to identify the primary crime category for an incident


Stat Code Desc
The definition of the statistical code number


Address
The street number, street name, state and zip where the incident occurred


Street
The street number and street name where the incident occurred


City
The city where the incident occurred


Zip
The zip code of the location where the incident occurred


Incident ID
The URN #, or Uniform Report Number, is a unique # assigned to every criminal and noncriminal incident


Reporting District
A geographical area defined by LASD which is within a city or unincorporated area where the incident occurred


Sequential (per Station)
Each incident for each station is issued a unique sequence # within a given year


Gang Related
Indicates if the crime incident was gang related


Unit ID
ORI # is a number issued by the FBI for every law enforcement agency


Unit Name
Station Name


Longitude
Longitude (as plotted on the nearest half block street segment)


Latitude
Latitude (as plotted on the nearest half block street segment)


Part Category
Part I Crime or Part II Crime indicator

This dataset provides geographically filtered data from LASD: https://lasd.org/transparency/part1and2crimedata/

The information has not been altered in any way.

Incident Date = Date the crime incident occurred Incident Reported Date = Date the crime was reported to LASD Category = Incident crime category Stat = A three digit numerical coding system to identify the primary crime category for an incident Stat Desc = The definition of the statistical code number Address (last two digits of # rounded to 00) = The street number, street name, state and zip where the incident occurred Street (last two digits of # rounded to 00) = The street number and street name where the incident occurred City = The city where the incident occurred Zip = The zip code of the location where the incident occurred Incident ID = The URN #, or Uniform Report Number, is a unique # assigned to every criminal and noncriminal incident Reporting District = A geographical area defined by LASD which is within a city or unincorporated area where the incident occurred Seq = Each incident for each station is issued a unique sequence # within a given year Gang Related = Indicates if the crime incident was gang related (column added 08/02/2012) Unit ID = ORI # is a number issued by the FBI for every law enforcement agency Unit Name = Station Name Longitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Latitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Part Category = Part I Crime or Part II Crime indicator (replaced DELETED column 01/04/2021)

This data collection documents an evaluation of the Los Angeles County Sheriff's Regimented Inmate Diversion (RID) program conducted with male inmates who were participants in the program during September 1990-August 1991. The evaluation was designed to determine whether county-operated boot camp programs for male inmates were feasible and cost-effective. An evaluation design entailing both process and impact components was undertaken to fully assess the overall effects of the RID program on offenders and on the county jail system. The process component documented how the RID program actually operated in terms of its selection criteria, delivery of programs, length of participation, and program completion rates. Variables include demographic/criminal data (e.g., race, date of birth, arrest charge, bail and amount, sentence days, certificates acquired, marital status, employment status, income), historical state and county arrest data (e.g., date of crime, charge, disposition, probation time, jail time, type of crime), boot camp data (e.g., entry into and exit from boot camp, reason for exit, probation dates, living conditions, restitution order), drug history data (e.g., drug used, frequency, method), data on drug tests, and serious incidence data. The impact data were collected on measures of recidivism, program costs, institutional behavior, and RID's effect on jail crowding.

The California Substance Abuse and Crime Prevention Act (SACPA) of 2000 targeted nonviolent offenders who have a history of substance abuse and were primarily charged with misdemeanor or felony possession, excluding selling charges, for diversion from incarceration into community-based substance abuse programs. The two sites selected for this study (the El Monte Drug Court in Los Angeles County and San Joaquin County Drug Court) had SACPA programs that differed from each other and from the Drug Court model. The data for the outcome analysis were collected from administrative databases and from paper files where necessary and available. The data link an individial's criminal activity data, treatment data, and other program activity data. The outcome analysis consisted of Drug Court and Substance Abuse and Crime Prevention Act (SACPA) samples from San Joaquin and El Monte (Los Angeles) counties. Part 1, San Joaquin County Data, had a total of 725 participants and Part 2, El Monte (Los Angeles) County Data, had a total of 587 participants. The Drug Court cohort included pre- and post-SACPA Drug Court participants. The pre-SACPA Drug Court participants included all those who entered the Drug Court program July 1998 through June 1999 and included 202 participants in San Joaquin and 127 participants in El Monte. The post-SACPA Drug Court participants included all those who entered the Drug Court program in July 2002 through June 2003. This sample provided 128 participants in San Joaquin and 147 participants in El Monte who experienced the Drug Court program after any changes in eligibility and Drug Court processes due to SACPA, as well as allowing for outcome data for three years post-program entry. The SACPA samples in San Joaquin and El Monte consisted of all SACPA participants who were first time enrollees in SACPA programs between July 2002 and June 2003. These samples included 395 participants in San Joaquin and 313 participants in El Monte who experienced a reasonably well-established SACPA program while still allowing three years of outcomes post-program entry. The data for both San Joaquin county and El Monte (Los Angeles) county include the demographic variables age, race, gender, and drug of choice. Drug Court Treatment variables include dates or number of group sessions, dates or number of individual sessions, dates or number of days in residential treatment, other Drug Court service dates and types. Substance Abuse and Crime Prevention Act (SACPA) Treatment variables include dates or number of group sessions or episodes, dates or number of individual sessions or episodes, dates or number of urinalysis tests, dates or number of days in residential treatment, and other SACPA service dates and types. Other variables include arrest data, new court cases data, jail data, prison data, and probation data.