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.

Despite making up approximately 10-12 pecent of the total population of the United States in the period between 1933 and 1970, Black people comprised roughly 20-30 percent of arrests made in these years. Today, Black people still have the highest incarceration rates relative to their population, however these rates have been declining in the past two decades.

In 2022, the prevalence of violent crime increased for all races in the United States in comparison to the previous year. In that year, around **** percent of White Americans experienced one or more violent victimizations and approximately **** percent of Black or African American people were the victims of a violent crime.

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of cert

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each year by police agencies in the United States. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform crime report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses including violent, drug, gambling, and larceny crimes.

In 2023/24, the arrest rate for people in England and Wales varied by self-identified ethnicity. People who identified as Black or Black British had an arrest rate of 17 per 1,000 population, compared with ten for people who identified as white, and eight who identified as Asian or Asian British.

In 2023, a total of 5,439 white Americans were arrested for arson in the United States in comparison to 1,876 Americans who were Black or African American.

In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.

The bar chart shows the percentage of Indiana’s total arrests by racial category. The arrest percentage is calculated by dividing the number of arrests of people within a specific racial category by the total number of statewide arrests. The baseline of “per 1000” allows for comparison of rates across categories. Selecting the “rate per 1000” view produces a line graph that shows the number of arrests per 1,000 individuals by race. The number of arrests per county and by race are compared to 2010 Census population 2014-2020. Additional facts to note: 1. This dashboard shows data from the Criminal History Records Information System (CHRIS), which comes from three main sources. Arrest data comes from the Live Scan system, which is used for finger printing and capturing other pertinent information at the time of the arrest. Criminal disposition data are maintained by prosecutors in the ProsLink system, and by courts in the Odyssey system. Arrest county is determined by the location of the booking agency. If the booking agency is missing, then the arresting agency is used. The % of IN Population will not equal 100% because we are excluding non-represented racial category "Two or More Races," which accounts for ~1.7% of Indiana's population. Because some arrests are not included in the individual race categories shown here, total counts and percentages from the individual race categories add up to less than the totals for “All” races. While most dashboards in the Data Portal use Census estimates from 2019, this dashboard uses 2010 Census data.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program each year by police agencies in the United States. These arrest reports provide data on 43 offenses including violent crime, drug use, gambling, and larceny. The data received by ICPSR were structured as a hierarchical file containing, per reporting police agency: an agency header record, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to logical record length format with the agency header record variables copied onto the detail records. Consequently, each record contains arrest counts for a particular agency-offense.

As of 2022, Black people were more likely than those of other races to be imprisoned in the United States. In that year, the rate of imprisonment for Black men stood at ***** per 100,000 of the population. For Black women, this rate stood at ** per 100,000 of the population.

These data assess the effects of the risk of local jail incarceration and of police aggressiveness in patrol style on rates of violent offending. The collection includes arrest rates for public order offenses, size of county jail populations, and numbers of new prison admissions as they relate to arrest rates for index (serious) crimes. Data were collected from seven sources for each city. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 1A (ICPSR 7941), provided county-level data on number of persons by race, age, and age by race, number of persons in households, and types of households within each county. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 3A (ICPSR 8071), measured at the city level, provided data on total population, race, age, marital status by sex, persons in household, number of households, housing, children, and families above and below the poverty level by race, employment by race, and income by race within each city. The Federal Bureau of Investigation (FBI) 1980 data provided variables on total offenses and offense rates per 100,000 persons for homicides, rapes, robbery, aggravated assault, burglary, larceny, motor vehicle offenses, and arson. Data from the FBI for 1980-1982, averaged per 100,000, provided variables for the above offenses by sex, age, and race, and the Uniform Crime Report arrest rates for index crimes within each city. The NATIONAL JAIL CENSUS for 1978 and 1983 (ICPSR 7737 and ICPSR 8203), aggregated to the county level, provided variables on jail capacity, number of inmates being held by sex, race, and status of inmate's case (awaiting trial, awaiting sentence, serving sentence, and technical violations), average daily jail populations, number of staff by full-time and part-time, number of volunteers, and number of correctional officers. The JUVENILE DETENTION AND CORRECTIONAL FACILITY CENSUS for 1979 and 1982-1983 (ICPSR 7846 and 8205), aggregated to the county level, provided data on the number of individuals being held by type of crime and sex, as well as age of juvenile offenders by sex, average daily prison population, and payroll and other expenditures for the institutions.

Youth arrest data are from the Minnesota Bureau of Criminal Apprehension special request and does not include youth arrests by ethnicity. The only options for race categories are American Indian/Alaska Native, Asian, Black/African American, Native Hawaiian/Pacific Islander (suppressed due to low numbers), and White. There is no ‘other’ or ‘mixed race’ option.

Youth population data are from the Office of Juvenile Justice and Delinquency Prevention’s Easy Access to Juvenile Populations (https://www.ojjdp.gov/ojstatbb/ezapop/). Since youth arrests are only of youth 10-17 years old, so is the youth population data.

In 2022, the incarceration rate of African Americans in local jails in the United States was *** incarcerations per 100,000 of the population -- the highest rate of any race or ethnicity. The second-highest incarceration rate was among American Indians/Alaska Natives, at *** incarcerations per 100,000 of the population.

Version 5 release notes:

• Removes support for SPSS and Excel data.
• Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
  
• Adds in agencies that report 0 months of the year.
• Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.
• Removes data on runaways.
  

Version 4 release notes:

• Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
  

Version 3 release notes:

• Add data for 2016.
• Order rows by year (descending) and ORI.

Version 2 release notes:

• Fix bug where Philadelphia Police Department had incorrect FIPS county code.

The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.

All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:

• Total Male Juvenile
• Total Female Juvenile
• Total Male Adult
• Total Female Adult
• Total Ma

Police-reported hate crime, by type of motivation (race or ethnicity, religion, sexual orientation, language, disability, sex, age), selected regions and Canada (selected police services), 2014 to 2024.