In 2023, around **** percent of persons between the ages of 12 and 17 years old in the United States experienced one or more violent victimizations. This was a decrease from the previous year, when **** percent of children in the same age group were the victim of a violent crime.

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.

In 2024, approximately ******* crimes were committed by people aged between 51 and 60 years in South Korea. The middle-aged population in South Korea recorded the highest number of crimes that year.

In 2022, the majority of 11.65 thousand crime victims in Finland belonged to the age groups 21 to 29, according to the source information. Furthermore, there were nearly 11.2 thousand victims aged 30 to 39 years during that year.

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 data contains the number of criminal incidents, the clearance status of those incidents and persons-charged, by MCYS region (Central, East, North, Toronto, West, Other). The survey was designed to measure the incidence of crime in our society and its characteristics. The Canadian Centre for Justice Statistics, in co-operation with the policing community, collects police-reported crime statistics through the UCR survey. Adapted from Statistics Canada, CANSIM Table 252-0077, 2015. This does not constitute an endorsement by Statistics Canada of this product. *[MCYS]: Ministry of Children and Youth Services *[ CANSIM]: Canadian Socio-Economic Information Management System *[UCR]: Uniform Crime Reporting

The number of new inmates in prison and the number of crimes committed vary by age.

This statistic shows the annual number of people in the United States who were victims of a violent crime in 2021, by age and type of crime. In 2021, over ***** children aged 12 to 14 were the victims of rape or sexual assault.

This collection focuses on how changes in the legal drinking age affect the number of fatal motor vehicle accidents and crime rates. The principal investigators identified three areas of study. First, they looked at blood alcohol content of drivers involved in fatal accidents in relation to changes in the drinking age. Second, they looked at how arrest rates correlated with changes in the drinking age. Finally, they looked at the relationship between blood alcohol content and arrest rates. In this context, the investigators used the percentage of drivers killed in fatal automobile accidents who had positive blood alcohol content as an indicator of drinking in the population. Arrests were used as a measure of crime, and arrest rates per capita were used to create comparability across states and over time. Arrests for certain crimes as a proportion of all arrests were used for other analyses to compensate for trends that affect the probability of arrests in general. This collection contains three parts. Variables in the Federal Bureau of Investigation Crime Data file (Part 1) include the state and year to which the data apply, the type of crime, and the sex and age category of those arrested for crimes. A single arrest is the unit of analysis for this file. Information in the Population Data file (Part 2) includes population counts for the number of individuals within each of seven age categories, as well as the number in the total population. There is also a figure for the number of individuals covered by the reporting police agencies from which data were gathered. The individual is the unit of analysis. The Fatal Accident Data file (Part 3) includes six variables: the FIPS code for the state, year of accident, and the sex, age group, and blood alcohol content of the individual killed. The final variable in each record is a count of the number of drivers killed in fatal motor vehicle accidents for that state and year who fit into the given sex, age, and blood alcohol content grouping. A driver killed in a fatal accident is the unit of analysis.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99

In 2021, a total of 24 people aged 10 years and under were arrested for arson in the United States. For property crimes overall, 113,379 people between the ages of 26 and 30 years old were arrested in that year - the most out of any age group.

Prisoner's new entry robbery offender number by age group

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.

Number of homicide victims and persons accused of homicide, by age group (total all ages; 0 to 11 years; 12 to 17 years; 18 to 24 years; 25 to 29 years; 30 to 39 years; 40 to 49 years; 50 to 59 years; 60 years and over; age unknown) and sex (both sexes; male; female; sex unknown), Canada, 1974 to 2017.

Information on the publication: The table presented is based on the data of the Police Crime Statistics (PKS) of the Land Schleswig-Holstein. These are the results of the police investigation before submission to the public prosecutor’s office or the court. The PKS contains the illegal offences that have become known to the police, including attempts to punish them, the number of suspects identified and a number of other information on cases, victims or suspects. The data relate to a completed reporting year and shall be published annually for the previous calendar year. ##Table-specific information: Table 91 (Disaggregation of victims by age and sex) This data set contains figures on victims by age and sex in Schleswig-Holstein, which were recorded in the corresponding reporting year. ##Construction of the table: The following columns are included: — Key number of the offences — criminal code number (keys of the respective offences or total keys) — Criminal offence — plain text of the offence or the sum key — Completed/Trial/Total — Numbers of victims according to completed, attempted actions and total — Total victims — Number of total victims — Sex — division into male and female and total — Age groups — Number of victims divided by age groups — The preceding number in brackets (1) — (18), indicates the column numbering. Character set: Western European (Windows — 1252/WinLatin 1)

Between November 2023 and April 2024, the share of the population that reported being victims of crimes varied among the age group populations. The group with the highest rate were of those between 15 and 29 years of age, with **** percent. Meanwhile, only **** percent of the population over 60 years of age reported being the victim of a crime.

Information on victims - broken down by age and gender and separated for completed acts, attempts and overall by selected offences with additional age groups, per circle

The dataset contains year-, gender- and age-group-wise number of juveniles arrested for various types of crimes under Indian Penal Code (IPC) and Special and Local Laws (SLL)

This table contains data on registered suspects of a crime recorded in the reporting year, distinguished by crime group, age, gender and municipality of residence.

The sex, age and place of residence of a suspect is determined on the last Friday of September of the year under review. This data comes from the Basic Registration Persons (BRP) or, if the person is not registered in the BRP, from the registration system of the police. The table shows the number of suspects of crime in absolute and relative figures. The relative figure is calculated per 10000 persons of the selected population. Individuals who do not appear in the Basic Registration Persons (BRP) often lack personal data. These individuals are counted in the absolute figures, but not in the relatives.

Because the number of suspects per type of crime is presented per year, the number of suspects per type of crime is increased to more than the total number of unique suspects. A person who has been registered more than once within a reporting year is counted only once in the total number of suspects. In addition, for each offence he is suspected of, he is counted once in the main group of offences. Example: a suspect of 10 burglaries and 2 violent crimes is counted once in Total number of suspects, 1 in Power Offences and 1 in Violence Offences.

Data available from: 2010

Status of the figures: The figures up to 2020 are final. The figures for 2021 and 2022 are provisional. Preliminary figures give an underestimation of the final number of suspects. The preliminary number of suspects in the most recent year is a few percent lower than the final number.

Changes as of 16 February 2024: None, this is a new table.

When new figures will be available: Figures on registered suspects 2023 will be available in March 2024.

Crime Survey for England and Wales (CSEW) estimates, by each combination of offence group, age, sex, and important demographic characteristics.