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 reflects incidents of crime in the City of Los Angeles from 2010 - 2019. 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.

This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

As of 2020, there were ****** violent crimes reported in Los Angeles by the Los Angeles Police Department. Within the provided time period, the highest number of robberies was reported in 2017, at ******.

***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.

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.

Locations for crime reporting and investigation in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Reporting Districts form the basis of crime reporting, organizing crimes into specific areas.

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

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.

License: CC0 1.0 Universal

The Justice Equity Need Index (JENI), by Advancement Project California, offers a means to map out the disparate burden that criminalization and a detention-first justice model place on specific communities. The index includes the following indicators:System Involvement: The system-involved population by ZIP Code results in direct needs for justice equity, as measured by adult and youth probation. Indicators: Adult Probation (per 1,000 people); Youth Probation (per 1,000 people) Inequity Drivers: Root inequities across communities that contribute to racial and economic disparities as seen in incarceration and policing. Indicators: Black, Latinx, AIAN, and NHPI Percentages of Population (average percentile); Unemployment Rate (%); Population aged 25+ without a High School Diploma (%); Population below 200% of the Federal Poverty Level (%); Violent Crime Rate (per 1,000 people) Criminalization Risk: Conditions where the criminal justice system has historically taken a detention-first, prevention-last approach. Indicators: Mental Health Hospitalizations (per 1,000 people); Substance Use-Related Hospitalizations (per 1,000 people); Homelessness Rate (per 1,000 people) Learn more at https://www.catalystcalifornia.org/campaign-tools/maps-and-data/justice-equity-need-index.Supervisorial Districts, SPAs, and CSAs determined by ZIP Code centroid.

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.

As of 2020, there were ****** burglaries reported in Los Angeles by the Los Angeles Police Department. Within the provided time period, the greatest number of burglaries in Los Angeles were in 2010, with ****** reported.

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.

The purpose of this pilot project was to determine if preliminary investigation report (PIR) data filed by patrol officers could be collected via laptop computers to allow the direct input of the data into the Los Angeles Police Department Crime and Arrest Database without adversely affecting the personnel taking or using the reports. This data collection addresses the following questions: (1) Did officers and supervisors prefer the automated reporting system (ARS) or the handwritten version of the PIR? (2) Did the ARS affect the job satisfaction or morale of officers and supervisors? (3) Did the ARS reduce the amount of time that patrol officers, supervisors, and clerks spent on paperwork? (4) Did the ARS affect the accuracy of information contained in the PIRs? (5) Did detectives and prosecuting attorneys find the ARS a more reliable source than handwritten PIRs? Officers and supervisors in two divisions of the Los Angeles Police Department, Wilshire and Hollywood, participated as control and experimental groups. The control group continued using handwritten ("existing") PIRs while the experimental group used the automated PIRs (ARS). The General Information Questionnaire collected information on each officer's rank, assignment, watch, gender, age, years with the Los Angeles Police Department, education, job morale, job demands, self-esteem, computer anxiety, and relationship with supervisor and other officers. The Job Performance Rating Form gathered data on work efforts, depth of job knowledge, work quality, oral and written skills, and capacity to learn. The Time Study Sheets collected data on investigation time, writing and editing time, travel time, approval and correction time, review time, errors by type, and data input time for both the handwritten and automated forms. The Evaluation of the Existing Form and the Evaluation of the Automated Form both queried respondents on ease of use, system satisfaction, and productivity loss. The ARS Use Questionnaire asked about ease of use, typing skills, computer skills, comfort with the system, satisfaction with training, and preference for the system. The Hollywood Detective Division ARS Use Questionnaire surveyed detectives on the system's ease of use, task improvement, support for continued use, and preference for the system. The PIR Content Evaluation Form collected data on quality of officers' observations, organization and writing skills, physical evidence, statements of victims, witnesses, and suspects, and offense classification. The Caplan Role Conflict and Role Ambiguity subscales were used in the design of the questionnaires.

As of 2020, there were ***** rapes reported in Los Angeles. Within the provided time period, the highest number of rapes reported was in 2018, at *****.

This dataset reflects arrest incidents in the City of Los Angeles from 2010 to 2019. This data is transcribed from original arrest 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 (0.0000°, 0.0000°). 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.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes. The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.

Descriptive statistics for neighborhood variables at the tract level from 2011 to 2019.