In 2023, the federal state of California recorded the most motor vehicle thefts in the United States, with a total of 199,592 reported cases of motor vehicle theft. This was followed by Texas with 125,045 cases. Washington, Illinois, and Colorado rounded out the top five states for motor vehicle theft in that year.

In 2023, the nationwide rate of motor vehicle theft in the United States was 318.7 reported cases per 100,000 population. While this is an increase from the previous year, it is a significant decrease from the rate in 1990, which stood at 657.8 motor vehicle thefts per 100,000 of the population.

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.

When incidents happened, where it took place, the victim’s perception of the incident, and what items were stolen or damaged. Annual data from the Crime Survey for England and Wales (CSEW).

Stolen Vehicles Data (stolen_vehicles.csv)

This dataset contains details about various stolen vehicles. Each record represents a specific vehicle with the following attributes:

• vehicle_id: A unique identifier for each vehicle.
• vehicle_type: The type of vehicle, such as Trailer or Boat Trailer.
• make_id: A numerical identifier linking to the manufacturer's details in the other dataset.
• model_year: The manufacturing year of the vehicle.
• vehicle_desc: A description of the vehicle, possibly including model names and other specifics.
• color: The color of the vehicle.
• date_stolen: The date when the vehicle was reported stolen.
• ocation_id: A numerical identifier representing the location from which the vehicle was stolen.

Make Details Data (make_details.csv)

This dataset provides information about the makers of the vehicles. Each record in this dataset corresponds to a maker with the following fields:

• make_id: A numerical identifier for the maker. This correlates with the make_id in the stolen vehicles dataset.
• make_name: The name of the maker, such as Aakron Xpress, ADLY, etc.
• make_type: The classification of the maker, typically noted here as 'Standard'.

These datasets are presumably linked by the make_id field, allowing for an integrated view that matches vehicles to their manufacturers, enhancing understanding of the data regarding stolen vehicles and their origins.

In 2023, an estimated 1,067,522 reported motor vehicle theft cases occurred in the United States. This is an increase from the previous year, when there were an estimated 948,119 cases of motor vehicle theft nationwide.

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.

Car Theft State-by-State (With Makes and Models)

The Most Stolen Makes and Models by Number of Thefts

By Joe Boutros [source]

About this dataset

This dataset aims to explore the car theft climate in the US. It contains information on the top ten most stolen cars (make, model and year) by State, as well as the top 25 stolen model year cars and their corresponding number of thefts. This data was gathered by The National Insurance Crime Bureau which reports this data annually to provide an insight into car theft prevention strategies. Insight included is not only which vehicle might be at higher risk of being stolen in a given state but also what kind of models/makes can be found among many states or nationwide with highest frequency of being stolen. Analyzing this dataset could help answer questions like “What are the most frequently targeted makes/models?” or “Which states have seen an increase or decrease in car thefts?” - perhaps providing invaluable insight for consumers on how to best protect their vehicles from potential theft

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How to use the dataset

This dataset contains information about the car thefts that occured in the United States, organized by state and make/model of the vehicle stolen. You can use this dataset to uncover trends of car theft and investigate how different states differ when it comes to car theft patterns. Below are a few ways you can use this data:

• Explore Car Thefts by State: Use the columns State and # of Thefts to compare total thefts across all states, or specific states if you wish.

• Compare Stolen Vs Non-Stolen Cars by Make / Model: Use the columns Make/Model and Thefts to look at which make/model cars were most frequently stolen in 2015, as well as compare them with models that were not stolen at all.

• Uncover Hot Wheels for Each State: Look at columns Rank and Model Year for each state to determine which specific hot wheels vehicles experienced the most thefts during that year from each state.

  • Dive Deeper into Model Years : Utilize columns Veh model Yr and Thefts_Year to explore correlations between vehicle model years & total number of thefts per year for particular models or overall data set trends (e..g identifying increases in theft frequency)

Research Ideas

• Identifying consumer trends on automotive theft to create targeted educational materials to help drivers stay safe and protect against theft.
• Creating geographical heat maps of car theft that could be consulted by drivers when they are considering purchasing a new vehicle or relocating.
• Providing data-driven recommendations on the best types of vehicles and precautions that drivers should take if they live in an area with higher rates of car theft

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2015_State_Top10Report_wTotalThefts.csv | Column name | Description | |:---------------|:--------------------------------------------------------| | State | The state in which the car theft occurred. (String) | | Rank | The rank of the car theft in the state. (Integer) | | Make/Model | The make and model of the car that was stolen. (String) | | Model Year | The year of the car that was stolen. (Integer) | | Thefts | The number of thefts of the car in the state. (Integer) |

File: Top25-2015-Models-2015-thefts-for-release.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------| | Theft_Year | The year in which the theft occurred. (Integer) | | Veh Model Yr | The model year of the vehicle that was stolen. (Integer) | | **...

This dataset contains theft of motor vehicle occurrences from 2018 - 2024.For privacy reasons, the locations of the occurrences have been geomasked to the closest intersection. The crime statistics published are accurate on the day that they were produced. Due to ongoing police investigations and internal data quality control efforts, this information is subject to change, including addition, deletion and reclassification of any and all data. Date created: June 20th, 2023 Date updated: February 11th, 2024Update frequency: Annually Accuracy: The Ottawa Police provides this information in good faith but provides no warranty, nor accepts any liability arising from any incorrect, incomplete or misleading information or its improper use. Attributes: 1. Vehicle Year2. Vehicle Make3. Vehicle Model4. Vehicle Style5. Vehicle Colour6. Vehicle Value7. Weekday8. Recovered9. Neighbourhood10. Ward11. Councillor12. Sector13. Division14. Reported Date15. Occurred Date16. Year17. Intersection18. Division19. Census Tract20. Time of Day21. Councillor22. Reported Hour23. Occurred Hour

This statistic shows Canada's reported motor vehicle theft rate from 2000 to 2023. There were about 286.46 motor vehicle thefts per 100,000 residents in Canada in 2021. Motor Vehicle TheftMotor vehicle theft, a subset of property crime, is the theft or attempted theft of any self-propelled land vehicles such as cars, trucks, motorcycles and ATVs. Motor vehicles are typically stolen for resale and parts after being stripped down, or for joy rides, short term thefts for the sole purpose of entertainment. Motor vehicle theft has been on the decline for some years. It follows the downward trend of all property crime in Canada, which is down 40 percent since 2000. It is thought one of the primary reasons for the decline in motor vehicle thefts is better anti-theft technology in newer cars, such as engine immobilizers, which make it very difficult to steal without the ignition key. In fact, all of the vehicles on Insurance Bureau of Canada’s list of top ten most stolen automobiles predate legislation that went into effect in 2007 requiring new cars sold in Canada to be equipped with an engine immobilizer.

This trends and best practices evaluation geared toward motor vehicle theft prevention with a particular focus on the Watch Your Car (WYC) program was conducted between October 2002 and March 2004. On-site and telephone interviews were conducted with administrators from 11 of 13 WYC member states. Surveys were mailed to the administrators of auto theft prevention programs in 36 non-WYC states and the 10 cities with the highest motor vehicle theft rates. Completed surveys were returned from 16 non-WYC states and five of the high auto theft rate cities. Part 1, the survey for Watch Your Car (WYC) program members, includes questions about how respondents learned about the WYC program, their WYC related program activities, the outcomes of their program, ways in which they might have done things differently if given the opportunity, and summary questions that asked WYC program administrators for their opinions about various aspects of the overall WYC program. The survey for the nonmember states, Part 2, and cities, Part 3, collected information about motor vehicle theft prevention within the respondent's state or city and asked questions about the respondent's knowledge of, and opinions about, the Watch Your Car program.

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.

In 2020, Vermont was the state with the least number of motor vehicle thefts in the United States, with only 264 motor vehicle thefts. Maine had the second fewest motor vehicle thefts in that year, at 862.

The Criminal Justice Research Division of the San Diego Association of Governments (SANDAG) received funds from the National Institute of Justice to assist the Regional Auto Theft Task (RATT) force and evaluate the effectiveness of the program. The project involved the development of a computer system to enhance the crime analysis and mapping capabilities of RATT. Following the implementation of the new technology, the effectiveness of task force efforts was evaluated. The primary goal of the research project was to examine the effectiveness of RATT in reducing auto thefts relative to the traditional law enforcement response. In addition, the use of enhanced crime analysis information for targeting RATT investigations was assessed. This project addressed the following research questions: (1) What were the characteristics of vehicle theft rings in San Diego and how were the stolen vehicles and/or parts used, transported, and distributed? (2) What types of vehicles were targeted by vehicle theft rings and what was the modus operandi of suspects? (3) What was the extent of violence involved in motor vehicle theft incidents? (4) What was the relationship between the locations of vehicle thefts and recoveries? (5) How did investigators identify motor vehicle thefts that warranted investigation by the task force? (6) Were the characteristics of motor vehicle theft cases investigated through RATT different than other cases reported throughout the county? (7) What investigative techniques were effective in apprehending and prosecuting suspects involved in major vehicle theft operations? (8) What was the impact of enhanced crime analysis information on targeting decisions? and (9) How could public education be used to reduce the risk of motor vehicle theft? For Part 1 (Auto Theft Tracking Data), data were collected from administrative records to track auto theft cases in San Diego County. The data were used to identify targets of enforcement efforts (e.g., auto theft rings, career auto thieves), techniques or strategies used, the length of investigations, involvement of outside agencies, property recovered, condition of recoveries, and consequences to offenders that resulted from the activities of the investigations. Data were compiled for all 194 cases investigated by RATT in fiscal year 1993 to 1994 (the experimental group) and compared to a random sample of 823 cases investigated through the traditional law enforcement response during the same time period (the comparison group). The research staff also conducted interviews with task force management (Parts 2 and 3, Investigative Operations Committee Initial Interview Data and Investigative Operations Committee Follow-Up Interview Data) and other task force members (Parts 4 and 5, Staff Initial Interview Data and Staff Follow-Up Interview Data) at two time periods to address the following issues: (1) task force goals, (2) targets, (3) methods of identifying targets, (4) differences between RATT strategies and the traditional law enforcement response to auto theft, (5) strategies employed, (6) geographic concentrations of auto theft, (7) factors that enhance or impede investigations, (8) opinions regarding effective approaches, (9) coordination among agencies, (10) suggestions for improving task force operations, (11) characteristics of auto theft rings, (12) training received, (13) resources and information needed, (14) measures of success, and (15) suggestions for public education efforts. Variables in Part 1 include the total number of vehicles and suspects involved in an incident, whether informants were used to solve the case, whether the stolen vehicle was used to buy parts, drugs, or weapons, whether there was a search warrant or an arrest warrant, whether officers used surveillance equipment, addresses of theft and recovery locations, date of theft and recovery, make and model of the stolen car, condition of vehicle when recovered, property recovered, whether an arrest was made, the arresting agency, date of arrest, arrest charges, number and type of charges filed, disposition, conviction charges, number of convictions, and sentence. Demographic variables include the age, sex, and race of the suspect, if known. Variables in Parts 2 and 3 include the goals of RATT, how the program evolved, the role of the I

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.

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 RDAnalysis@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 are updated daily. 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

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 PSITAdministration@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 are updated daily. 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

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2019). For additional details, please see the attached data dictionary in the ‘About’ section.

The data is sourced from the NIBRS Group A Offense Crimes dataset and covers the period from January 1, 2020, to the end of the most recent complete month. The displayed number represents the total motor vehicle thefts within the specified timeframe and sector.