There were almost 444,000 shoplifting offences recorded by the police in England and Wales in 2023/24, the highest in this provided time period. Between 2002/03 and 2012/13, shoplifting offences in England and Wales fluctuated between around 280,000 and 320,000 annual offences. From 2013/14 onwards, shoplifting offences began to increase, and reached a pre-2024 peak of around 382, 607 in 2017/18, before beginning to decline again in subsequent years. The 2020/21, and 2021/20 reporting years are not directly comparable to the other reporting years due to COVID-19 lockdowns that occurred at that time. Areas with the highest shoplifting rate As of 2023/24, the shoplifting rate for England and Wales as a whole was 7.4 shoplifting offences per 1,000 population. Looking at individual police force areas, the area covered by Nottinghamshire Police Force, in the English East Midlands, had the highest shoplifting rate of 13.2, followed by Cleveland, in North East England, at 13.1. By contrast, the Dyfed-Powys Police Force Area, in Wales had the lowest shoplifting rate of just 3.9 offences per 1,000 population. Although Cleveland also had the highest overall crime rate in England and Wales, and Dyfed-Powys had one of the lowest, Nottinghamshire's crime rate was slightly below the overall average, despite having such a high shoplifting rate. Comparisons with other theft offences Shoplifting is one of the major sub-categories of theft in the jurisdiction of England and Wales. Although shoplifting offences have increased significantly recently, the number of theft offences overall has generally been falling. In 2002/03 for example, there were approximately 3.41 million incidences of theft, compared with just 1.78 million in 2023/24. This trend has been driven by declines in other types of theft, such as burglary, which fell from over 890,000 offences in 2002/03, to around 266,500 in 2023/24.

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.

In 2023, the nationwide larceny-theft rate in the United States was 1,347.2 cases per 100,000 of the population. This is a decrease from the previous year, when the larceny-theft rate stood at 1,416.6 cases per 100,000 of the population.

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 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.

Sweden crime rate per 100K population for 2021 was 1.08, a 9.72% decline from 2020.

<ul style='margin-top:20px;'>

<li>Sweden crime rate per 100K population for 2020 was <strong>1.20</strong>, a <strong>10.63% increase</strong> from 2019.</li>
<li>Sweden crime rate per 100K population for 2019 was <strong>1.08</strong>, a <strong>1.71% increase</strong> from 2018.</li>
<li>Sweden crime rate per 100K population for 2018 was <strong>1.06</strong>, a <strong>5.4% decline</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

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 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.

The number of shoplifting cases registered by the German police has fluctuated in the last decade, though the figures displayed in this graph show a decrease since 2015. In 2023, around 426,096 cases of shoplifting were recorded by police in Germany, a rather large increase compared to the previous year. Mind the shop Shoplifting is damaging to any business. There are measures that can be taken in order to minimize shoplifting, such as video surveillance, tagging items, as well as hiring security personnel that are visibly present to customers in some shops. The reality is, however, that among theft crimes, shoplifting is the most common, so even with security measures implemented, it is difficult for stores to completely avoid being targeted. Falling crime rate Recent statistics show that the crime rate in Germany (estimated per 100,000 people) was falling considerably. However, since the pandemic the rate of crime in Germany has risen. Police have also recorded more crime offences since 2021.

Colombia crime rate per 100K population for 2021 was 27.48, a 13.37% increase from 2020.

<ul style='margin-top:20px;'>

<li>Colombia crime rate per 100K population for 2020 was <strong>24.24</strong>, a <strong>5.87% decline</strong> from 2019.</li>
<li>Colombia crime rate per 100K population for 2019 was <strong>25.75</strong>, a <strong>1.82% decline</strong> from 2018.</li>
<li>Colombia crime rate per 100K population for 2018 was <strong>26.23</strong>, a <strong>3.13% increase</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.

Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. Law enforcement agencies contribute reports either directly or through their state reporting programs. Each year, summary data are reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Offenses Known and Clearances by Arrest data files include monthly data on the number of Crime Index offenses reported and the number of offenses cleared by arrest or other means. The counts include all reports of Index crimes (excluding arson) received from victims, officers who discovered infractions, or other sources.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

World crime rate per 100K population for 2020 was 5.61, a 0.74% increase from 2019.

<ul style='margin-top:20px;'>

<li>World crime rate per 100K population for 2019 was <strong>5.56</strong>, a <strong>3.65% decline</strong> from 2018.</li>
<li>World crime rate per 100K population for 2018 was <strong>5.77</strong>, a <strong>2.24% decline</strong> from 2017.</li>
<li>World crime rate per 100K population for 2017 was <strong>5.91</strong>, a <strong>0.69% decline</strong> from 2016.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

Annual data from the Crime Survey for England and Wales (CSEW). Data include when and where incidents happened, information about offenders, the victim's perception of the incident, and who they reported the incident to.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), police services in Ontario, 1998 to 2023.

These data are a product of the National Crime Surveys Redesign Project. The purpose of the data collection was to create several different data files from existing public-use National Crime Surveys files. For each crime, information is gathered on the victim's housing unit and household and the incident itself. A personal history and interview are also included. Several data files contain National Crime Survey and Uniform Crime Report data on the following index crimes: robbery, larceny-theft, burglary, motor vehicle theft, rape, and aggravated assault.

The National Crime Survey (NCS), a study of personal and household victimization, measures victimization for six selected crimes, including attempts. The NCS was designed to achieve three primary objectives: to develop detailed information about the victims and consequences of crime, to estimate the number and types of crimes not reported to police, and to provide uniform measures of selected types of crime. The surveys cover the following types of crimes, including attempts: rape, robbery, assault, burglary, larceny, and auto or motor vehicle theft. Crimes such as murder, kidnapping, shoplifting, and gambling are not covered. Questions designed to obtain data on the characteristics and circumstances of the victimization were asked in each incident report. Items such as time and place of occurrence, injuries suffered, medical expenses incurred, number, age, race, and sex of offender(s), relationship of offender(s) to victim (stranger, casual acquaintance, relative, etc.), and other detailed data relevant to a complete description of the incident were included. Legal and technical terms, such as assault and larceny, were avoided during the interviews. Incidents were later classified in more technical terms based upon the presence or absence of certain elements. In addition, data were collected in the study to obtain information on the victims' education, migration, labor force status, occupation, and income. Full data for each year are contained in Parts 101-110. Incident-level extract files (Parts 1-10, 41) are available to provide users with files that are easy to manipulate. The incident-level datasets contain each incident record that appears in the full sample file, the victim's person record, and the victim's household information. These data include person and household information for incidents only. Subsetted person-level files also are available as Parts 50-79. All of the variables for victims are repeated for a maximum of four incidents per victim. There is one person-level subset file for each interview quarter of the complete national sample from 1973 through the second interview quarter in 1980.

This table contains data on the rate of violent crime (crimes per 1,000 population) for California, its regions, counties, cities and towns. Crime and population data are from the Federal Bureau of Investigations, Uniform Crime Reports. Rates above the city/town level include data from city, university and college, county, state, tribal, and federal law enforcement agencies. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Ten percent of all deaths in young California adults aged 15-44 years are related to assault and homicide. In 2010, California law enforcement agencies reported 1,809 murders, 8,331 rapes, and over 95,000 aggravated assaults. African Americans in California are 11 times more likely to die of assault and homicide than Whites. More information about the data table and a data dictionary can be found in the About/Attachments section.