In 2023, the violent crime rate in the United States was 363.8 cases per 100,000 of the population. Even though the violent crime rate has been decreasing since 1990, the United States tops the ranking of countries with the most prisoners. In addition, due to the FBI's transition to a new crime reporting system in which law enforcement agencies voluntarily submit crime reports, data may not accurately reflect the total number of crimes committed in recent years. Reported violent crime rate in the United States The United States Federal Bureau of Investigation tracks the rate of reported violent crimes per 100,000 U.S. inhabitants. In the timeline above, rates are shown starting in 1990. The rate of reported violent crime has fallen since a high of 758.20 reported crimes in 1991 to a low of 363.6 reported violent crimes in 2014. In 2023, there were around 1.22 million violent crimes reported to the FBI in the United States. This number can be compared to the total number of property crimes, roughly 6.41 million that year. Of violent crimes in 2023, aggravated assaults were the most common offenses in the United States, while homicide offenses were the least common. Law enforcement officers and crime clearance Though the violent crime rate was down in 2013, the number of law enforcement officers also fell. Between 2005 and 2009, the number of law enforcement officers in the United States rose from around 673,100 to 708,800. However, since 2009, the number of officers fell to a low of 626,900 officers in 2013. The number of law enforcement officers has since grown, reaching 720,652 in 2023. In 2023, the crime clearance rate in the U.S. was highest for murder and non-negligent manslaughter charges, with around 57.8 percent of murders being solved by investigators and a suspect being charged with the crime. Additionally, roughly 46.1 percent of aggravated assaults were cleared in that year. A statistics report on violent crime in the U.S. can be found here.

***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.

This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year.

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 project was designed to isolate the effects that individual crimes have on wage rates and housing prices, as gauged by individuals' and households' decisionmaking preferences changing over time. Additionally, this project sought to compute a dollar value that individuals would bear in their wages and housing costs to reduce the rates of specific crimes. The study used multiple decades of information obtained from counties across the United States to create a panel dataset. This approach was designed to compensate for the problem of collinearity by tracking how housing and occupation choices within particular locations changed over the decade considering all amenities or disamenities, including specific crime rates. Census data were obtained for this project from the Integrated Public Use Microdata Series (IPUMS) constructed by Ruggles and Sobek (1997). Crime data were obtained from the Federal Bureau of Investigation's Uniform Crime Reports (UCR). Other data were collected from the American Chamber of Commerce Researchers Association, County and City Data Book, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Independent variables for the Wages Data (Part 1) include years of education, school enrollment, sex, ability to speak English well, race, veteran status, employment status, and occupation and industry. Independent variables for the Housing Data (Part 2) include number of bedrooms, number of other rooms, building age, whether unit was a condominium or detached single-family house, acreage, and whether the unit had a kitchen, plumbing, public sewers, and water service. Both files include the following variables as separating factors: census geographic division, cost-of-living index, percentage unemployed, percentage vacant housing, labor force employed in manufacturing, living near a coastline, living or working in the central city, per capita local taxes, per capita intergovernmental revenue, per capita property taxes, population density, and commute time to work. Lastly, the following variables measured amenities or disamenities: average precipitation, temperature, windspeed, sunshine, humidity, teacher-pupil ratio, number of Superfund sites, total suspended particulate in air, and rates of murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, violent crimes, and property crimes.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

This dataset was compiled by the Illinois Criminal Justice Information Authority (ICJIA) at the request of the Governor’s Children’s Cabinet. This data contains the population of youth ages 13-26 in each county, the total population of each county, and the number and rate of index crimes reported, with domestic violence offenses and rates reported separately for every year between 2006 and 2015.

For the purpose of this analysis the crime data was gathered from the Illinois State Police Annual report Crime in Illinois. This publication is produced by the Illinois State Police every year using the UCR data that is submitted to them by individual jurisdictions throughout the state. The accuracy of this data presented is dependent on the local jurisdictions reporting their index crime and domestic violence offenses to ISP, so it can be included in the annual report.

Therefore, if there is large decrease in number of index crimes reported in the dataset it is likely that one or more jurisdictions did not report data for that year to ISP. If there is a large increase from year to year within a county it is likely that a jurisdiction within the county, who previously had not reported crime data, did report crime data for that year. If there is no reported crime in a certain year that means no jurisdictions, or a small jurisdiction with no crime from that county reported data to the Illinois State Police. The annual Crime in Illinois reports can be found on the ISP website www.isp.state.il.us.

A direct link to that annual reports is: http://www.isp.state.il.us/crime/ucrhome.cfm#anlrpts.

The Illinois Criminal Justice Information Authority did not record the data that is expressed in the dataset. ICJIA simply used the ISP reports to compile that yearly crime data into one chart that could be provided to the Illinois Governor’s Children’s Cabinet. This data set has be critically examined to be accurate according to the annual Crime in Illinois Reports. If there are issues with the data set provided please contact the Illinois State Police or the individual jurisdictions within a specific county.

**Index offenses do not include every crime event that occurs. Prior to 2014 there were 8 index crimes reported by the Illinois State Police in their annual reports, Criminal Homicide, Rape, Robbery, Aggravated Battery/Aggravated Assault, Burglary, Theft, Motor Vehicle Theft, and Arson. In 2014 there were two new offenses added to the list of index crimes these were Human Trafficking – Commercial Sex Acts and Human Trafficking – Involuntary Servitude. These are the index crimes that are recorded in the chart provided.

**“Domestic offenses are defined as offenses committed between family or household members. Family or household members include spouses; former spouses; parents; children; foster parents; foster children; legal guardians and their wards; stepchildren; other persons related by blood (aunt, uncle, cousin) or by present or previous marriage (in-laws); persons who share, or formerly shared, a common dwelling; persons who have, or allegedly have, a child in common; persons who share, or allegedly share, a blood relationship through a child; persons who have, or have had, a dating or engagement relationship; and persons with disabilities, their personal care assistants, or care givers outside the context of an employee of a public or private care facility. Every offense that occurs, when a domestic relationship exists between the victim and offender, must be reported (Illinois State Police).”

**“Offenses reported are not limited to domestic battery and violations of orders of protection; offenses most commonly associated with domestic violence (Illinois State Police).”

The crime rate was compiled using the total population, and the index crime. The Index crime whether all crime or Domestic Violence crime was divided by the total population then multiplied by 10,000, hence crime rate per 10,000.

The sources of data are the Illinois Uniform Crime Reporting Program and the U.S. Census Bureau.

The source of the description is the Illinois State Police and their Reporting guidelines and forms.

Dataset

This dataset was created by Sammy V Rajan

Contents

The Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs' departments. DCJS compiles these reports as New York's official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

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 reports National Incident Based Reporting System (NIBRS) crime statistics from most Washington law enforcement agencies since 2016. The data is a "shapshot" of crime statistics as they were originally reported to the Washington Association of Sheriffs and Police Chiefs (WASPC) and the FBI. Data displayed should not be compared to statistics reported by the FBI as those statistics have been converted to the Summary Reporting System (SRS).

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1972. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, and motor vehicle theft. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.

The primary purpose of the second wave of the National Neighborhood Crime Study (NNCS2) was to develop a panel dataset of serious reported crimes in urban neighborhoods circa two time points - 2000 and 2010. These data offer the opportunity to assess the sources and consequences of neighborhood crime change for "communities" of different ethno-racial and economic compositions across the United States. The study also sought to examine the role of a neighborhood's broader ecology on crime levels and crime change by integrating indicators of city and/or metropolitan conditions. The NNCS2 includes two datasets. The first dataset, the NNCS2-Panel file (NNCS2-P), contains information on the Federal Bureau of Investigation's (FBI) Part 1 Index crimes (except arson), socioeconomic and demographic characteristics, and a variety of other neighborhood and city level controls for circa 2000 and 2010 for tracts in 81 of the 91 cities in the NNCS, wave 1. The second dataset, the NNCS2-Cross-Sectional file (NNCS2-CS), allows for examination of the local and contextual sources of neighborhood crime inequality circa 2010. The NNCS2-CS incorporates parallel data for census tracts and cities as in the Panel file, but includes a few additional cities for which panel data could not be compiled, as well information on the metropolitan areas within which cities are located.

In 2024, about 64 percent of Americans felt there is more crime now in the United States than there was a year ago. A further 29 percent of survey respondents said that there was less crime in the U.S. in 2024 than there was the year previous.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

This dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at http://bit.ly/Nb3R7m.

In 2023, the state with the highest crime rate in the United States per 100,000 inhabitants was New Mexico. That year, the crime rate was ******** crimes per 100,000 people. In comparison, New Hampshire had the lowest crime rate at ****** crimes per 100,000 people. Crime rate The crime rate in the United States has generally decreased over time. There are several factors attributed to the decrease in the crime rate across the United States. An increase in the number of police officers and an increase in income are some of the reasons for a decrease in the crime rate. Unfortunately, people of color have been disproportionately affected by crime rates, as they are more likely to be arrested for a crime versus a white person. Crime rates regionally The District of Columbia had the highest rate of reported violent crimes in the United States in 2023 per 100,000 inhabitants. The most common crime clearance type in metropolitan counties in the United States in 2020 was murder and non-negligent manslaughter. The second most dangerous city in the country in 2020 was Detroit. Detroit has faced severe levels of economic and demographic declines in the past years. Not only has the population decreased, the city has filed for bankruptcy. Despite the median household income increasing, the city still struggles financially.

Statistical breakdown by citywide, borough, and precinct.

The Seattle Police Department (SPD) replaced its Records Management System (RMS) in May 2019. To preserve data quality and continuity between systems (2008-Present), SPD relied on the National Incident-Based Reporting System (NIBRS). The standardization of crime classifications allows for comparison over time. For more information on definitions and classifications, please visit https://www.fbi.gov/services/cjis/ucr/nibrs.

Additional groupings are used to analyze crime in SPD’s Crime Dashboard. Violent and property crime categories align with best practices. For additional inquiries, we encourage the use of the underline data to align with the corresponding query.

Disclaimer: Only finalized (UCR approved) reports are released. Those in draft, awaiting approval, or completed after the update, will not appear until the subsequent day(s). Data is updated once every twenty-four hours. Records and classification changes will occur as a report makes its way through the approval and investigative process.

The National Crime Victimization Survey (NCVS), previously the National Crime Survey (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The survey is administered by the United States Census Bureau (under the United States Department of Commerce) on behalf of the Bureau of Justice Statistics (under the United States Department of Justice). Occasionally there have been extract or supplement files created from the NCVS and NCS data series. This extract contains two data files, a weighted person-based file, and a weighted incident-based file, which contain the "core" counties within the top 40 National Crime Victimization Survey Metropolitan Statistical Areas (MSAs). Core counties within these MSAs are defined as those self-representing primary sampling units that are common to the MSA definitions determined by the Office of Management and Budget for the 1970-based, 1980-based, and 1990-based sample designs. Each MSA is comprised of only the core counties and not all counties within the MSA. The person-based file contains select household and person variables for all people in NCVS-interviewed households in the core counties of the 40 largest MSAs from January 1979 through December 2004. The incident-based file contains select household, person, and incident variables for persons who reported a violent crime within any of the core counties of the 40 largest MSAs from January 1979 through December 2004. Household, person, and incident information for persons reporting non-violent crime are excluded from this file. The 40 largest MSAs were determined based on the number of household interviews in an MSA.