In 2023, around 42,508 burglaries in the United States took place in commercial or office buildings. A further 23,358 burglaries took place in restaurants in that year, and a further 45 burglaries took place on military bases.

This data collection is one of three quantitative databases comprising the Commercial Theft Studies component of the Study of the Causes of Crime for Gain, which focuses on patterns of commercial theft and characteristics of commercial thieves. This data collection contains information on commercial burglary incidents in Xenon, New Jersey. The data collection includes incident characteristics, theft item, value of stolen property, and demographic information about the suspect(s), such as police contacts, number of arrests, sex, race, and age.

Livorno and Milan were the leading provinces for burglary rate in commercial establishments in Italy in 2020. According to data, the Central Italian city of Livorno ranked first for non-residential burglaries, with *** cases per 100,000 inhabitants.

GapMaps offers advanced and reliable Crime Risk Places Data sourced from Applied Geographic Solutions (AGS), a trusted provider of premium demographic insights with over 20 years of experience. Leveraged by thousands of businesses, AGS use advanced statistical methodologies and a rolling seven-year database of FBI and local agency statistics to provide a highly accurate view of the relative risk of specific crime types for any geographic area empowering organizations to make informed decisions in areas such as insurance, urban planning, and real estate.

The AGS Crime Risk dataset includes: - Standardised indexes for a range of serious crimes against both persons and property such as murder, rape, robbery, assault, burglary, theft, and motor vehicle theft - Aggregate measures of crime risk, including crimes against persons, crimes against property, and overall crime risk, offering a comprehensive overview of an area’s safety. - 5-Year Projections: Added in 2020, these projections enhance the dataset by forecasting future crime risks, providing valuable insights for long-term planning. - High-Resolution Data: Crime risk indexes are available at the block group level, allowing insurers to identify variations in crime risk across specific land uses such as motor vehicle theft from parking structures.

Use cases: 1. Insurance underwriting and risk mitigation. 2. Evaluating the security measures needed to protect employees and customers at retail facilities. 3. The study of the effects of neighborhood crime on wellness and health care outcomes.

Methodology: Crime is tracked for multiple years using both FBI aggregate crime reports and for many parts of the country at the individual incident level. A complex set of statistical models are used to estimate and forecast risk of each individual crime type by using land use data in conjunction with demographic and business characteristics.

For more information visit www.appliedgeographic.com

In 2024, there were ** commercial crimes committed in Singapore for every 100,000 individuals. This was a slight increase compared to the previous year.

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 major objective of this study was to examine how physical characteristics of commercial centers and demographic characteristics of residential areas contribute to crime and how these characteristics affect reactions to crime in mixed commercial-residential settings. Information on physical characteristics includes type of business, store hours, arrangement of buildings, and defensive modifications in the area. Demographic variables cover racial composition, average household size and income, and percent change of occupancy. The crime data describe six types of crime: robbery, burglary, assault, rape, personal theft, and shoplifting.

This report tracks the incident-based crime rate per 100,000 people. Data is sourced from the Incident-based Uniform Crime Reporting Survey and Statistics Canada.

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), Canada, provinces, territories, Census Metropolitan Areas and Canadian Forces Military Police, 1998 to 2024.

Context

Crime data analysis is essential for understanding patterns of criminal activity, identifying risk factors, and informing public safety policies. This dataset provides a detailed look at reported offenses in Indiana for the year 2023, offering valuable insights into demographic trends, geographic crime distribution, and seasonal variations. By analyzing this dataset, researchers, policymakers, and data enthusiasts can uncover key factors influencing crime rates and develop data-driven strategies for prevention and intervention.

Content

This dataset compiles crime records from Indiana in 2023, structured to facilitate in-depth analysis across various dimensions. It includes:

• Demographics – Age, race, and gender details of individuals involved in reported offenses, enabling a deeper understanding of crime patterns among different population groups.
• Offense Details – Categorized crime types, including theft, violent crimes, drug-related offenses, and property crimes, to reveal crime distribution across Indiana.
• Temporal Patterns – A breakdown of crimes by month to identify seasonal crime trends and fluctuations throughout the year.
• Geographic Distribution – County-level crime data that highlight high-crime areas and differences in crime rates between urban and rural regions.
• Arrest Rates – Information on arrests linked to various offenses, providing insights into law enforcement actions and policy effectiveness.

Inspiration

This dataset presents several opportunities for exploration and analysis:

• Which demographics are most affected by specific types of crime?
• How do crime rates vary across different counties, and what factors contribute to regional differences?
• Are there seasonal patterns in criminal activity that could inform law enforcement strategies?
• What are the relationships between arrest rates and specific types of offenses?

Potential Applications

This dataset is well-suited for various analytical and research purposes, including:

• Demographic Analysis – Examining which age, race, and gender groups are most affected by certain offenses.
• Geographic Insights – Analyzing county-level crime rates to understand how population density and urbanization impact crime trends.
• Temporal Analysis – Identifying seasonal crime patterns to assist in resource allocation and crime prevention strategies.
• Policy Recommendations – Using data insights to propose interventions aimed at reducing crime and improving community safety.
• Machine Learning Applications – Developing predictive models for crime forecasting and classification.

Ideal For

• Beginners and intermediate analysts looking to apply data cleaning, visualization, and storytelling skills.
• Machine learning enthusiasts interested in crime prediction models.
• Policymakers, law enforcement agencies, and public safety organizations seeking data-driven insights for decision-making.

Dataset Origin

This dataset was curated from publicly available Indiana crime records and compiled for educational and analytical purposes. All personally identifiable information has been anonymized to ensure privacy.

Licensing and Restrictions

This dataset is open for non-commercial projects. Attribution to the original source is appreciated when sharing findings or insights.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

This data collection is the 2023 iteration of the Commercial Victimisation Survey (CVS), a survey of the extent of crime and crime related issues experienced by business premises in England and Wales. It provides additional detail on the extent of crime, to be used alongside the other main sources of information on crime. These are the Crime Survey for England and Wales (CSEW) (formerly the British Crime Survey), which covers crimes against private individuals and households, and the Police Recorded Crime statistics, which cover crimes reported to the police.

The CVS was previously run as a standalone survey in 1994 and again in 2002. The CVS was then run as an annual publication from 2012 onwards. A break occurred from 2019 to 2021 where CVS underwent a re-design following a consultation. A standalone CVS was run in 2021, covering only the Wholesale and Retail sector, to provide insights on the effects of the COVID-19 pandemic. From 2022 onwards, the coverage of the survey has included all commercial business premises.

The 2023 CVS aims to estimate the extent and nature of crime affecting all commercial business premises in England and Wales. The data includes the prevalence and frequency of crime affecting business premises in England and Wales: as well as impacts on premises, crime prevention measures taken up by premises, experiences of the police, and attitudes to the police.

These Commercial Victimization Surveys were collected as part of the National Crime Surveys. They document burglary and robbery incidents for all types of commercial establishments, as well as political, cultural, and religious organizations. Business characteristics gathered include form of ownership and operation, size and type of business, and security measures. Information regarding the reported incidents includes time and place, weapon involvement, offender and entry characteristics, injuries and deaths, and type and value of stolen property. Data were collected by calendar quarter for four quarters in 1973-1976 and for two quarters in 1977, while the actual incidents reported in the files reflect those occurring six months prior to the interview date.

The National Crime Surveys, of which these Commercial Victimization Surveys are a part, were conducted to obtain current and reliable measures of serious crime in the United States. The Commercial Victimization Surveys are restricted to coverage of burglary and robbery incidents. They include all types of commercial establishments as well as political, cultural, and religious organizations. The survey includes a series of questions about the business, e.g., type and size, form of ownership, insurance, security, and break-in and robbery characteristics. Time and place, weapon, injury, entry evidence, offender characteristics, and stolen property data were collected for each of the incidents. Data on both victimized and nonvictimized establishments in 26 different cities were collected during 1972, 1973, and 1974. In the 1975 survey, data from the 13 cities surveyed during 1972 and 1973 were collected again.

Statistics on extent of crime against businesses in the wholesale & retail, accommodation & food, arts, entertainment & recreation, and agriculture, forestry & fishing sectors taken from the Commercial Victimisation Survey. Source agency: Home Office Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Crime against business premises

According to a late 2023 survey on retail theft, approximately *** in five small business retailers experience theft a few times a week. Nearly ** percent of surveyed small business owners said retail theft occurs within their store(s) every day.

This dataset reflects reported incidents of crime 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. Any use of the information for commercial purposes is strictly prohibited. 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.