These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Serial sexual assault is a pervasive problem: court record recidivism rates show that 10-15% of convicted sex offenders re-assault within five years and self-report studies suggest that 63-78% of males who have committed sexual assaults have raped more than one individual. The current study documents the scope of repeat sexual offending through a previously-unexplored method of documenting serial perpetration: DNA evidence in sexual assault kits (SAKs). Ultimately, the testing of N = 7,287 previously untested SAKs revealed n = 1,270 unique and identifiable perpetrators. When combined with information from lifetime criminal history records, 39.7% (n = 504) of this subsample of unique and identifiable perpetrators were found to be serial sexual offenders who committed, on average, 3.27 sexual assaults. This collection contains 10 SPSS files: ESCALATIONdata_2018-05-15.sav (1142 cases, 6 variables) Forensic_Outcomes_2018-04-03.sav (7287 cases, 7 variables) PERPdata_2018-04-03.sav (1424 cases, 12 variables) SAKdata_2018-04-03.sav (1675 cases, 6 variables) SAK_PERP_2018-04-03.sav (1691 cases, 12 variables) SSA_ARR_Arrests_Imputed_2018-09-03.sav (9826 cases, 24 variables) SSA_CHG_PA_Charges_Imputed_2018-09-03.sav (6052 cases, 24 variables) SSA_IDN_Offenders_2018-09-03.sav (1142 cases, 17 variables) SSA_INC_Incidents_Imputed_2018-09-03.sav (9550 cases, 16 variables) SSA_JUD_Judicial_Charges_Imputed_2018-09-03.sav (12522 cases, 30 variables) This collection includes demographic variables on offenders, including sex, race, age, and arrest region.

In 2023, about 187,633 perpetrators of child abuse in the United States were white. In that same year, about 82,421 perpetrators of child abuse were Hispanic, and 25,113 were of unknown ethnic origin.

In the 2024/25 reporting year there were 3.4 sexual offences per 1,000 population in England and Wales, with Cleveland police force reporting the highest rate of 4.7 sexual crimes per 1,000 people.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined the extent to which contextual factors influenced variation in sex offender sentencing decisions. By law, Pennsylvania trial courts were required to submit all felony and misdemeanor convictions under the Pennsylvania Sentencing Guidelines to the Pennsylvania Commission on Sentencing on a yearly basis. These data were supplemented with county-level data from the American Community Survey, Administrative Office of Pennsylvania Courts' Annual Caseload Statistics of the Unified Judicial System of Pennsylvania, Associated Religion Data Archives, and Pennsylvania Department of State, Voter Registration Statistics Archives. The collection contains 1 SPSS data file (Cleaned-Data-2015-R2-CX-0039.sav (n=318048; 31 variables)). Demographic variables include gender, race, and defendant's age at sentencing.

In 2024/25, there were 209,556 sexual offences recorded by the police in England and Wales, an increase when compared with the previous reporting year and a peak for this type of crime. Between 2002/03 and 2012/13 the number of sexual offences remained quite stable, but from 2013/14 onwards the number of sexual offences has risen dramatically.

Sweden has witnessed an increase in the rates of sexual crimes including rape. Knowledge of who the offenders of these crimes are is therefore of importance for prevention. We aimed to study characteristics of individuals convicted of rape, aggravated rape, attempted rape or attempted aggravated rape (abbreviated rape+), against a woman ≥18 years of age, in Sweden. By using information from the Swedish Crime Register, offenders between 15 and 60 years old convicted of rape+ between 2000 and 2015 were included. Information on substance use disorders, previous criminality and psychiatric disorders were retrieved from Swedish population-based registers, and Latent Class Analysis (LCA) was used to identify classes of rape+ offenders. A total of 3 039 offenders were included in the analysis. A majority of them were immigrants (n = 1 800; 59.2%) of which a majority (n = 1 451; 47.7%) were born outside of Sweden. The LCA identified two classes: Class A — low offending class (LOC), and Class B — high offending class (HOC). While offenders in the LOC had low rates of previous criminality, psychiatric disorders and substance use disorders, those included in the HOC had high rates of previous criminality, psychiatric disorders and substance use disorders. While HOC may be composed by more “traditional” criminals probably known by the police, the LOC may represent individuals not previously known by the police. These two separated classes, as well as our finding in regard to a majority of the offenders being immigrants, warrants further studies that take into account the contextual characteristics among these offenders. Key pointsRape, aggravated rape, attempted rape or attempted aggravated rape (rape+) are increasing in Sweden.The majority of those convicted of rape+ are immigrants.LCA identifies two classes of rape+ offenders: LOC and HOC. Rape, aggravated rape, attempted rape or attempted aggravated rape (rape+) are increasing in Sweden. The majority of those convicted of rape+ are immigrants. LCA identifies two classes of rape+ offenders: LOC and HOC.

Sexual offence numbers, prevalence and victim characteristics, including breakdowns by type of incident, sex, victim-perpetrator relationship and location based upon findings from the Crime Survey for England and Wales and police recorded crime.

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.

In 2023, the rate of forcible rapes in the United States stood at 38 per 100,000 inhabitants. As the FBI revised the definition of rape in 2013, the 2023 rate is a slight decrease from 1990, when there were 41.2 forcible rapes per 100,000 inhabitants. What is forcible rape? According to the FBI, forcible rape is defined as “sexual penetration, no matter how slight, with a body part or object without the consent of the victim.” This definition changed in 2013 from the previous definition, which specified “carnal knowledge of a female victim forcibly and against her will.” Attempted rape was included in the previous definition, but statutory rape and other sexual offenses were excluded. The old definition was seen as problematic, as people of any gender can be raped. Since the revision of the definition of rape, reported rapes increased, although it is not clear if this is due to the revised definition or if the rate itself has increased. Rape in the United States While rape and sexual assault have been extensively talked about in the U.S. in recent years, especially since the start of the #metoo movement, there is still a large number of sexual offences committed each year. Sadly, the majority of sex offences in the U.S. are carried out against individuals age 20 and under. Astoundingly, the Anchorage, Alaska metropolitan area had the highest rape rate in the United States in 2023, followed by St Joseph in Missouri and Kansas. Since rape and sexual assault continue to be underreported in the United States, it is important to find a solution to this devastating problem.

Alcohol-related sexual crimes: Persons, all ages (per 1,000 population) *This indicator is discontinued

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

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

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

How does this dataset classify victims?

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

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

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

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

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

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

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

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

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

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

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

The number of rape and sexual assault cases reported to the police in Germany peaked at ****** in 2024 during the period shown here. Previously, the highest number of cases, ******, had been recorded the year before. Based on the definition in criminal law, sexual assault includes rape, as well as other sexually driven physical attacks. Rape is defined as forcing a person to have sex. Increased crime clearance rate The question remains how high the number of unreported cases is. Reasons for not reporting a sexual assault vary among victims. In recent years, the German police reported increasing clearance rates for sexual crimes. In 2022, **** percent of rape and sexual assault cases were solved, compared to **** percent in 2016. In 2023, however this figure dropped to **** percent, perhaps due to the increase in the number of cases. Among males suspected of committing such crimes, over ** percent were young adults aged 18 to 21 years. Types of German police forces German police forces are divided into several different types, which all have clearly established tasks regulated by law. The Federal Criminal Police Office (Bundeskriminalamt, BKA) is often compared to the FBI in the U.S. and investigates federal crimes, such as kidnapping. The Federal Police (Bundespolizei), works in railway stations, at airports, and seaports. They also protect borders, government buildings, and deal with organized crime and terrorism. The criminal police (Kriminalpolizei, Kripo), the only policemen not wearing in uniform in Germany, handle assault, murder, and rape cases, as well as theft. The uniformed police (Schutzpolizei, SchuPo), or beat police, are regularly visible in streets, as they are responsible for traffic safety, among other tasks, and may be approached directly by people in need of assistance or help.

This study compares the characteristics, sentence/offence information, and criminogenic risk and need profile of gender diverse offenders with sex offence histories to offenders in men’s and women’s institutions with similar offence histories.

These data examine the relationships between childhood abuse and/or neglect and later criminal and violent criminal behavior. In particular, the data focus on whether being a victim of violence and/or neglect in early childhood leads to being a criminal offender in adolescence or early adulthood and whether a relationship exists between childhood abuse or neglect and arrests as a juvenile, arrests as an adult, and arrests for violent offenses. For this data collection, adult and juvenile criminal histories of sampled cases with backgrounds of abuse or neglect were compared to those of a matched control group with no official record of abuse or neglect. Variables contained in Part 1 include demographic information (age, race, sex, and date of birth). In Part 2, information is presented on the abuse/neglect incident (type of abuse or neglect, duration of the incident, whether the child was removed from the home and, if so, for how long, results of the placement, and whether the individual was still alive). Part 3 contains family information (with whom the child was living at the time of the incident, family disruptions, and who reported the abuse or neglect) and data on the perpetrator of the incident (relation to the victim, age, race, sex, and whether living in the home of the victim). Part 4 contains information on the charges filed within adult arrest incidents (occasion for arrest, multiple counts of the same type of charge, year and location of arrest, and type of offense or charge), and Part 5 includes information on the charges filed within juvenile arrest incidents (year of juvenile charge, number of arrests, and type of offense or charge). The unit of analysis for Parts 1 through 3 is the individual at age 11 or younger, for Part 4 the charge within the adult arrest incident, and for Part 5 the charge within the juvenile arrest incident.

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 certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

In 2023, there were about 541,713 victims of child abuse in the United States. In that year, about 129,187 children of Hispanic origin were reported to be abused in the country. A further 119,898 Black children were the victims of abuse in that year.

The VRAG-R is designed to assess the likelihood of violent or sexual reoffending among male offenders. The data set comprises demographic, criminal history, psychological assessment, and psychiatric information about the offenders gathered from institutional files together with post-release recidivism information. The VRAG-R is a twelve-item actuarial instrument and the scores on these items form part of the data set. Because one of the goals of the VRAG-R development project was to compare the VRAG-R to the VRAG, subjects' VRAG scores are included in this data set.

The data for this collection were gathered from the 1970 and 1980 Censuses and the Uniform Crime Reports for 1970 through 1980. The unit of analysis in this data collection is cities. Included are population totals by age group and arrest data for selected crimes by age group for Atlanta, Georgia, Chicago, Illinois, Denver, Colorado, Knoxville, Tennessee, San Jose, California, Spokane, Washington, and Tucson, Arizona. Population data by sex and age for all cities are contained in Part 4. The 123 variables provide data by age categories ranging from age 5 to age 69. Part 1, the arrest file for Atlanta and Chicago, provides arrest data for 1970 to 1980 by sex and age, ranging from age 10 and under to age 65 and over. The arrest data for other cities span two data files. Part 2 includes arrest data by sex for ages 15 to 24 for the years 1970 to 1980. Part 3 provides arrest data for ages 25 to 65 and over for the years 1970, 1975, and 1980. Arrest data are collected for the following crimes: murder, forcible rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, other assaults, arson, forgery, fraud, embezzlement, stolen property, vandalism, weapons, prostitution, other sex offenses, opium abuse, marijuana abuse, gambling, family offenses, drunk driving, liquor law violations, drunkenness, disorderly conduct, vagrancy, and all other offenses combined.

The National Crime Victimization Survey (NCVS), previously called 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 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. Beginning in 1992, the survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual assault, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. 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. This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2023. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.For additional information on the dataset, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 38962.