In 2023, New Mexico had the highest burglary rate in the United States. That year, they had 517.9 occurrences per 100,000 residents. Washington followed with 481 incidents per 100,000 residents. What is burglary? Burglary in the United States is considered a felony or misdemeanor. It includes trespassing and theft, and going inside a building or car with the intent to commit any crime. Even if the crime is not necessarily theft, it is still illegal. Some states consider burglary committed during the day as housebreaking, not burglary. The Bureau of Justice Statistics defines it as unlawful or forcible entry into a building. There are four types of burglary in total: completed burglary, forcible entry, unlawful entry, and attempted forcible entry. Burglary in the United States Burglary affects all 50 states in the United States, as burglary was the third most common type of property crime in the United States in 2023. California had the highest number of reported burglaries in that same year, whereas New Hampshire had the lowest number. However, the overall reported burglary rate in the United States has decreased significantly since 1990.

In 2023, the federal state of California had the most reported burglaries in the United States, with a total of 135,369 reported cases. Texas, North Carolina, Washington, and Florida rounded out the top five states with the most burglaries in that year.

The District of Columbia had the highest robbery rate in the United States in 2023, with 614.2 robberies per 100,000 inhabitants. The lowest robbery rate in the country was found in Idaho, with 9.5 robberies per 100,000 inhabitants. Crime in the District of Columbia The violent crime rate in the District of Columbia was found to be the highest in the United States, with there being a few reasons for this: Firstly, the population of the District of Columbia is quite low (causing a higher rate of crime), and secondly, issues such as the crack epidemic of the 1990s exacerbated the prevalence of crime in the District. As rising rents and gentrification force more people out of the District, crime is moving into neighboring Maryland and Virginia suburbs, as poorer residents seek more affordable living conditions. Crime in the United States Overall, violent crime in the United States and the District of Columbia today is far below the violent crime rate of the 1990s. While some may feel that crime is on the rise, due in part to media sensationalism in fact, the opposite is true, and the United States is becoming safer over time.

In 2023, the nationwide burglary rate in the United States was 250.7 cases per 100,000 of the population. This is a slight decrease from the previous year, when the burglary rate stood at 272.7 cases per 100,000 of the population.

The average for 2017 based on 79 countries was 105 robberies per 100,000 people. The highest value was in Costa Rica: 1587 robberies per 100,000 people and the lowest value was in Oman: 1 robberies per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

In 2023, an estimated 839,563 reported burglary cases occurred across the United States, a slight decrease from the previous year. The number of reported burglaries has been decreasing since 1990, when there were 3.07 million reported burglaries nationwide.

This dataset contains monthly records of theft-related crimes reported across the 32 states of Mexico from January 2015 through June 2025. Sourced from the official open data portal of the Executive Secretariat of the National Public Security System (SESNSP) (gob.mx/sesnsp), the data categorizes theft by type and by whether the crime involved violence.Dataset Fields:PERIOD: Reporting period in YYYY-MM-DD format.STATE_ID: Numeric identifier for each Mexican state.STATE: Name of the Mexican state.CRIME: Category of theft (e.g., "Bank robbery", "Motor vehicle theft").MODALITY: Indicates whether the crime was committed "With violence" or "Without violence".TOTAL_CASES: Number of reported incidents for the specified category, time, and location.Supplementary Materials:This dataset is part of a broader project that includes:A Python script that generates normalized bar charts to visualize the proportion of each theft type by modality. The script can be configured by state and year.A requirements.txt file listing Python dependencies for easy environment setup.A sample figure showing the distribution of thefts across Mexico for the year 2024, illustrating modality-based crime patterns.Potential Applications:Ideal for researchers, data analysts, and policy professionals, this dataset supports the study of crime trends, regional disparities in theft modalities, and the evaluation of public security policies.

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 2020, Hot Springs, Arkansas had the highest burglary rate in the United States, with 1,202.9 cases of burglary per 100,000 of its inhabitants. Lake Charles, Louisiana had the second highest burglary rate, at 1,065.7 cases per 100,000 inhabitants.

The research team collected data on homicide, robbery, and assault offending from 1984-2006 for youth 13 to 24 years of age in 91 of the 100 largest cities in the United States (based on the 1980 Census) from various existing data sources. Data on youth homicide perpetration were acquired from the Supplementary Homicide Reports (SHR) and data on nonlethal youth violence (robbery and assault) were obtained from the Uniform Crime Reports (UCR). Annual homicide, robbery, and assault arrest rates per 100,000 age-specific populations (i.e., 13 to 17 and 18 to 24 year olds) were calculated by year for each city in the study. Data on city characteristics were derived from several sources including the County and City Data Books, SHR, and the Vital Statistics Multiple Cause of Death File. The research team constructed a dataset representing lethal and nonlethal offending at the city level for 91 cities over the 23-year period from 1984 to 2006, resulting in 2,093 city year observations.

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 was a secondary analysis of data from the National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) for the period 1998-2007. The analysis calculates two separate measures of the incidents of violence that occurred during burglaries. The study addressed the following research questions: Is burglary a violent crime? Are different levels of violence associated with residential versus nonresidential burglaries? How frequently is a household member present during a residential burglary? How frequently does violence occur in the commission of a burglary? What forms does burglary-related violence take? Are there differences in rates of violence between attempted and completed burglaries? What constitutes the crime of burglary in current statutory law? How do the federal government and the various states define burglary (grades and elements)? Does statutory law comport with empirical observations of what the typical characteristics of acts of burglary are? The SPSS code distributed here alters an existing dataset drawn from pre-existing studies. In order to use this code users must first create the original data file drawn from National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) data from the period of 1998-2007. All data used for this study are publicly available through ICPSR. See the variable description section for a comprehensive list of, and direct links to, all datasets used to create this original dataset.

The files in this collection contain counts of arrests and offenses for UCR index crimes: murder, rape, robbery, assault, burglary, larceny, auto theft, and arson. County populations are also reported.

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

This study was designed to explain variations in crime rates and to examine the deterrent effects of sanctions on crime. The study concentrated on bank robberies, but it also examined burglaries and other kinds of robberies. In examining these effects the study condidered three sets of factors: (1) Economic considerations-- the cost/benefit factors that individuals consider in deciding whether or not to perform a crime, (2) Degree of anomie--the amount of alienation and isolation individuals feel toward society and the effect of these feelings on the individuals' performing a crime, and (3) Opportunity--the effect of exposure, attractiveness, and degree of guardianship on an object being taken. These factors were explored by gathering information on such topics as: crime clearance rates, arrests, and sentences, bank attributes, and socioeconomic and demographic information.

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. The study integrated neighborhood-level data on robbery and burglary gathered from local police agencies across the United States, foreclosure data from RealtyTrac (a real estate information company), and a wide variety of social, economic, and demographic control variables from multiple sources. Using census tracts to approximate neighborhoods, the study regressed 2009 neighborhood robbery and burglary rates on foreclosure rates measured for 2007-2008 (a period during which foreclosure spiked dramatically in the nation), while accounting for 2007 robbery and burglary rates and other control variables that captured differences in social, economic, and demographic context across American neighborhoods and cities for this period. The analysis was based on more than 7,200 census tracts in over 60 large cities spread across 29 states. Core research questions were addressed with a series of multivariate multilevel and single-level regression models that accounted for the skewed nature of neighborhood crime patterns and the well-documented spatial dependence of crime. The study contains one data file with 8,198 cases and 99 variables.

These data were prepared in conjunction with a project using Bureau of Labor Statistics data (not provided with this collection) and the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data to examine the relationship between unemployment and violent crime. Three separate time-series data files were created as part of this project: a national time series (Part 1), a state time series (Part 2), and a time series of data for 12 selected cities: Baltimore, Buffalo, Chicago, Columbus, Detroit, Houston, Los Angeles, Newark, New York City, Paterson (New Jersey), and Philadelphia (Part 3). Each data file was constructed to include 82 monthly time series: 26 series containing the number of Part I (crime index) offenses known to police (excluding arson) by weapon used, 26 series of the number of offenses cleared by arrest or other exceptional means by weapon used in the offense, 26 series of the number of offenses cleared by arrest or other exceptional means for persons under 18 years of age by weapon used in the offense, a population estimate series, and three date indicator series. For the national and state data, agencies from the 50 states and Washington, DC, were included in the aggregated data file if they reported at least one month of information during the year. In addition, agencies that did not report their own data (and thus had no monthly observations on crime or arrests) were included to make the aggregated population estimate as close to Census estimates as possible. For the city time series, law enforcement agencies with jurisdiction over the 12 central cities were identified and the monthly data were extracted from each UCR annual file for each of the 12 agencies. The national time-series file contains 82 time series, the state file contains 4,083 time series, and the city file contains 963 time series, each with 228 monthly observations per time series. The unit of analysis is the month of observation. Monthly crime and clearance totals are provided for homicide, negligent manslaughter, total rape, forcible rape, attempted forcible rape, total robbery, firearm robbery, knife/cutting instrument robbery, other dangerous weapon robbery, strong-arm robbery, total assault, firearm assault, knife/cutting instrument assault, other dangerous weapon assault, simple nonaggravated assault, assaults with hands/fists/feet, total burglary, burglary with forcible entry, unlawful entry-no force, attempted forcible entry, larceny-theft, motor vehicle theft, auto theft, truck and bus theft, other vehicle theft, and grand total of all actual offenses.

Two major changes to the Uniform Crime Reports (UCR) county-level files were implemented beginning with the 1994 data. A new imputation algorithm to adjust for incomplete reporting by individual law enforcement jurisdictions was adopted. Within each county, data from agencies reporting 3 to 11 months of information were weighted to yield 12-month equivalents. Data for agencies reporting less than 3 months of data were replaced with data estimated by rates calculated from agencies reporting 12 months of data located in the agency's geographic stratum within its state. Secondly, a new Coverage Indicator was created to provide users with a diagnostic measure of aggregated data quality in a particular county. Data from agencies reporting only statewide figures were allocated to the counties in the state in proportion to each county's share of the state population.In the arrest files (Parts 1-3 and 5-7), data were estimated for agencies reporting 0 months based on the procedures mentioned above. However, due to the structure of the data received from the FBI, estimations could not be produced for agencies reporting 0 months in the crimes reported files (Parts 4 and 8). Offense data for agencies reporting 1 or 2 months are estimated using the above procedures. Users are encouraged to refer to the codebook for more information.No arrest data were provided for Washington, DC, and Florida. Limited arrest data were available for Illinois and Kentucky. Limited offense data were available for Illinois, Kentucky, Mississippi, Missouri, Montana, and South Dakota.UCR program staff at the Federal Bureau of Investigation (FBI) were consulted in developing the new adjustment procedures. However, these UCR county-level files are not official FBI UCR releases and are being provided for research purposes only. Users with questions regarding these UCR county-level data files can contact the National Archive of Criminal Justice Data at ICPSR.Users should note that there are no records in the data for the borough of Denali, Alaska (FIPS code 02068) in any of the collections of the Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest and Offense Data from 1990 to 2003. The borough of Denali, Alaska (FIPS code 02068) was created from part of the Yukon-Koyukuk Census Area (FIPS code 02290) an unpopulated part of the Southeast Fairbanks Census Area (FIPS code 02240) effective December 7, 1990. Since no agency records for either arrests or crimes reported from Denali were present in any of the original FBI files, no data for the borough of Denali, Alaska appear in any the ICPSR collections for these years. This data collection contains county-level counts of arrests and offenses for Part I offenses (murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, and arson) and counts of arrests for Part II offenses (forgery, fraud, embezzlement, vandalism, weapons violations, sex offenses, drug and alcohol abuse violations, gambling, vagrancy, curfew violations, and runaways). Datasets: DS0: Study-Level Files DS1: Arrests, All Ages DS2: Arrests, Adult DS3: Arrests, Juveniles DS4: Crimes Reported DS5: Allocated Statewide Data for Arrests, All Ages DS6: Allocated Statewide Data for Arrests, Adults DS7: Allocated Statewide Data for Arrests, Juveniles DS8: Allocated Statewide Data for Crimes Reported County law enforcement agencies in the United States.

National or state offense totals are based on data from all reporting agencies and estimates for unreported areas. Rates are the number of reported offenses per 100,000 population
Sources: FBI, Uniform Crime Reports, prepared by the National Archive of Criminal Justice Data
Date of download: Sep 18 2013

This table provides the types of weapons used in aggravated assault and robbery offenses. The data are based on the aggregated data from agencies within each state for which weapon information was reported to the FBI. The table includes the number of agencies that submitted data by state and the population covered by those agencies. The dataset also includes a breakdown of the types of firearms used in murders (i.e., handguns, rifles, shotguns, or firearms).

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.