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.

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).

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.

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.

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.

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 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 data set is no longer compiled by the Ministry of the Solicitor General.

Property crimes are typically non-violent in nature and include:

• breaking and entering
• motor vehicle theft
• theft over $5,000 (non-motor vehicle)
• theft under $5,000 (non-motor vehicle)
• mischief

The data can be accessed from "https://www150.statcan.gc.ca/n1/en/type/data?text=property+crime">Statistics Canada.

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.

Vehicle Thefts by State and Make/Model

Identifying Highest Risk Models and Locations

By Joe Boutros [source]

About this dataset

This dataset compiled by the National Insurance Crime Bureau (NICB) reveals the top ten most stolen cars in each state and across the country during the year 2015. Additionally, this dataset provides information on the top 25 stolen model year cars in 2015. It offers insights into theft trends through valuable data points such as vehicle, make and model, and thefts per state. This dataset can help you gain access to important data which will enable you to make informed decisions about car purchase, insurance rates or areas where extra precaution may be necessary when it comes to protecting your automobile investment

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How to use the dataset

This dataset provides an in-depth look at the vehicles that are most often stolen in the United States during 2015. It can be used to understand which models and makes of cars are most frequently targeted by thieves on a state-by-state basis.

The two files included in this dataset provide information about both the general trends for all U.S. states and more specific insights about the top 25 stolen 2015 model year cars.

The first file, 2015_State_Top10Report_wTotalThefts.csv, includes detailed information regarding each of the top ten most stolen vehicles per state. Each row contains data such as state and rank, make/model, thefts by number and year of thefts as well as vehicle model year (allowing users to track trends over time). It is important to note that rank specifies which car is most frequently stolen overall within a given state for all years combined (and not just within 2015), whereas thefts only indicate how many times a specific vehicle was reported stolen within that chosen state for the given year (as stated above). Additionally, # of Thefts reflects how many times each model has been reported stolen nationally regardless of individual location or date - it gives an indication to how widespread a particular car's theft rate is across America. All in all this file provides valuable information regarding individual type and make/model combinations across various states in American while being able to pinpoint exact numbers with regards to frequency of theft over time intervals This can help inform users on whether certain makes/models have risen or decreased in desirability among thieves thus impacting their decision making when purchasing or insuring their own cars..

The second file Top25-2015-Models-2015-thefts-for-release.csv offers detailed insight into exactly 25 car models which were reported as being amongst those with highest localised incidence factor when concerning theft reports coming out of various US states throughout 2015 – consequently it may also be called „most popular” list amongst criminal circles when referring specifically NATIONALLY occurring incidents within one given calendar year . The average user can utilize this data along similar means as before - reviewing „popularity” title conferred upon them either locally through investigation into variations between popular target choices between different areas but also compare those against national rates too . It has one slight difference however from previous entry – model years covered revert exclusively towards brand new releases , i,.e maybe user would want

Research Ideas

• Targeted Crime Prevention Programs: Governments, law enforcement, and insurers can use this dataset to target areas with the highest rate of car thefts and create crime prevention programs tailored to those locations.
• Vehicle Security Analysis & Upgrades: Auto manufacturers can use this data to analyze which models are being stolen the most in certain states and adjust security systems accordingly.
• Insurance Loss Modeling & Rates: Insurance providers can use this dataset to refine their loss models by creating actuarial tables based on model type and region, which would help them better define risk profiles for customers in different locations across the country

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...

There were ******* vehicle theft offences in England and Wales in 2024/25, compared with ******* in the previous reporting year. Despite recent increases in this type of offence in the late 2010s, there were still far fewer vehicle thefts than there were in 2002/03, when there were more than ***********. This was followed by a steep decline, which saw vehicle thefts reduced to just under ******* in 2014/15. Declining theft offences bucks overall trend The sharp fall in vehicle theft offences between the early 2000s and mid 2010s was also reflected in overall crime figures for England and Wales. Unlike overall crime which generally increased from the late 2010s, vehicle thefts have remained relatively stable. This, along with the decline in burglaries has helped to keep overall theft offences relatively low compared to other types of crime, such as violent and sexual offences, which have both risen substantially. Changing nature of property crime Not every type of theft has declined, however. Shoplifting offences in England and Wales reached a recent peak of just over ******* offences in 2024/25. At the same time, theft from the person offences, such as via pockpocketing or snatching property, reached a peak in this reporting year at around ******* offences. The ongoing cost of living crisis has meant that everyday consumer goods are typically more valuable than even a few years previous, increasing the perceived gains for the desperate or opportunistic.

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police.\r
\r
The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public.\r
\r
The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose.\r
\r
Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r
\r
Motor vehicle thefts recorded in Victoria - where it occurs, when it occurs and who commits these offences.\r
\r
Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/\r
\r
Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

This dataset includes all Theft from Motor Vehicle occurrences by reported date and related offences since 2014. The Theft from Motor Vehicle offences include Theft from Motor Vehicle Under and Theft from Motor Vehicle Over.Theft from Motor Vehicle DashboardDownload DocumentationThis data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various offences used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

This dataset provides comprehensive data on violent and property crimes in Maryland counties from 1975 to the present. It includes various crime metrics such as murder, rape, robbery, aggravated assault, burglary, larceny theft, motor vehicle theft, and more.

Columns Information

JURISDICTION

• Description: County name in Maryland.
• Data Type: String

YEAR

• Description: Year of the data.
• Data Type: Integer

POPULATION

• Description: Population of the county.
• Data Type: Integer

MURDER

• Description: Number of murders.
• Data Type: Integer

RAPE

• Description: Number of rapes.
• Data Type: Integer

ROBBERY

• Description: Number of robberies.
• Data Type: Integer

AGG. ASSAULT

• Description: Number of aggravated assaults.
• Data Type: Integer

B & E

• Description: Number of burglaries (breaking and entering).
• Data Type: Integer

LARCENY THEFT

• Description: Number of larceny thefts.
• Data Type: Integer

M/V THEFT

• Description: Number of motor vehicle thefts.
• Data Type: Integer

GRAND TOTAL

• Description: Total number of crimes.
• Data Type: Integer

PERCENT CHANGE

• Description: Percent change in total crimes from the previous year.
• Data Type: Float

VIOLENT CRIME TOTAL

• Description: Total number of violent crimes.
• Data Type: Integer

VIOLENT CRIME PERCENT

• Description: Percent of violent crimes out of total crimes.
• Data Type: Float

VIOLENT CRIME PERCENT CHANGE

• Description: Percent change in violent crimes from the previous year.
• Data Type: Float

PROPERTY CRIME TOTALS

• Description: Total number of property crimes.
• Data Type: Integer

PROPERTY CRIME PERCENT

• Description: Percent of property crimes out of total crimes.
• Data Type: Float

PROPERTY CRIME PERCENT CHANGE

• Description: Percent change in property crimes from the previous year.
• Data Type: Float

OVERALL CRIME RATE PER 100,000 PEOPLE

• Description: Crime rate per 100,000 people.
• Data Type: Float

OVERALL PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in crime rate per 100,000 people from the previous year.
• Data Type: Float

VIOLENT CRIME RATE PER 100,000 PEOPLE

• Description: Violent crime rate per 100,000 people.
• Data Type: Float

VIOLENT CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in violent crime rate per 100,000 people from the previous year.
• Data Type: Float

PROPERTY CRIME RATE PER 100,000 PEOPLE

• Description: Property crime rate per 100,000 people.
• Data Type: Float

PROPERTY CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in property crime rate per 100,000 people from the previous year.
• Data Type: Float

MURDER PER 100,000 PEOPLE

• Description: Murder rate per 100,000 people.
• Data Type: Float

RAPE PER 100,000 PEOPLE

• Description: Rape rate per 100,000 people.
• Data Type: Float

ROBBERY PER 100,000 PEOPLE

• Description: Robbery rate per 100,000 people.
• Data Type: Float

AGG. ASSAULT PER 100,000 PEOPLE

• Description: Aggravated assault rate per 100,000 people.
• Data Type: Float

B & E PER 100,000 PEOPLE

• Description: Burglary (breaking and entering) rate per 100,000 people.
• Data Type: Float

LARCENY THEFT PER 100,000 PEOPLE

• Description: Larceny theft rate per 100,000 people.
• Data Type: Float

M/V THEFT PER 100,000 PEOPLE

• Description: Motor vehicle theft rate per 100,000 people.
• Data Type: Float

MURDER RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in murder rate per 100,000 people from the previous year.
• Data Type: Float

RAPE RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in rape rate per 100,000 people from the previous year.
• Data Type: Float

ROBBERY RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in robbery rate per 100,000 people from the previous year.
• Data Type: Float

AGG. ASSAULT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in aggravated assault rate per 100,000 people from the previous year.
• Data Type: Float

B & E RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in burglary (breaking and entering) rate per 100,000 people from the previous year.
• Data Type: Float

LARCENY THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in larceny theft rate per 100,000 people from the previous year.
• Data Type: Float

M/V THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in motor vehicle t...

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.

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This dataset includes all auto theft occurrences by reported date and related offences since 2014.Auto Theft DashboardDownload DocumentationThis data is provided at the offence and/or vehicle level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset.The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data.** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Stolen Vehicle Recovery Market Outlook

According to our latest research, the global stolen vehicle recovery market size in 2024 stands at USD 8.1 billion, driven by the rising adoption of advanced tracking technologies and increased vehicle theft rates worldwide. The market is expected to expand at a robust CAGR of 13.7% during the forecast period, reaching an estimated USD 25.1 billion by 2033. This growth is primarily propelled by technological advancements in GPS and cellular-based recovery solutions, alongside heightened consumer and regulatory emphasis on vehicle security.

One of the primary growth factors fueling the stolen vehicle recovery market is the rapid technological evolution in vehicle tracking and monitoring systems. The integration of GPS-based and cellular-based solutions has significantly enhanced real-time location tracking, enabling faster and more efficient recovery of stolen vehicles. Automakers are increasingly embedding these technologies during manufacturing, responding to consumer demand for enhanced safety features. Additionally, the proliferation of connected vehicles and the Internet of Things (IoT) ecosystem has made it easier for both individuals and fleet operators to monitor and secure their assets, further boosting market growth. The continuous innovation in telematics and the use of artificial intelligence for predictive analytics are also expected to propel the adoption of stolen vehicle recovery solutions across various segments.

Another key driver is the rising incidence of vehicle thefts globally, which has compelled insurance companies and regulatory authorities to mandate or incentivize the installation of recovery systems. Law enforcement agencies are collaborating with private technology providers to streamline recovery operations, leveraging sophisticated tracking devices and centralized databases. This trend is especially pronounced in urban areas, where high vehicle density and organized crime rings exacerbate the risk of theft. As insurance premiums are often reduced for vehicles equipped with certified recovery systems, there is a growing incentive for both individual and commercial end-users to invest in these technologies. Furthermore, public awareness campaigns and government regulations aimed at reducing vehicle-related crimes are accelerating market penetration, particularly in emerging economies with rising vehicle ownership rates.

The expansion of the commercial vehicle sector, coupled with the increasing digitization of fleet management, is also a major contributor to the growth of the stolen vehicle recovery market. Logistics companies, ride-sharing services, and public transportation agencies are investing heavily in tracking and recovery services to minimize losses and ensure operational continuity. The integration of recovery solutions with fleet management software allows for comprehensive monitoring, not only deterring theft but also improving asset utilization and compliance with safety standards. This trend is expected to gain further momentum as businesses prioritize risk mitigation and operational efficiency in an increasingly competitive environment. The market is also witnessing the emergence of value-added services, such as recovery assistance and remote immobilization, which are enhancing the overall value proposition for end-users.

Regionally, North America currently leads the stolen vehicle recovery market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high adoption of advanced telematics, stringent insurance requirements, and robust law enforcement infrastructure in these regions underpin their market dominance. However, Asia Pacific is anticipated to exhibit the fastest growth over the forecast period, driven by rising vehicle ownership, urbanization, and increasing investments in smart city initiatives. Latin America and the Middle East & Africa are also emerging as significant markets, fueled by growing concerns over vehicle theft and the proliferation of affordable tracking technologies. The interplay of regional regulatory frameworks, economic development, and consumer preferences will continue to shape the competitive landscape of the global market.

Technology Analysis

The technology segment of the stolen vehicle recovery market is characterized by rapid advancements and the proliferation of diverse tracking solutio

This is a dataset on reported swedish crime statistics from 1950 to 2023 taken from https://bra.se/bra-in-english/home/crime-and-statistics/crime-statistics.html

The dataset includes - Total number of crimes - Life and Death crimes - Murder, Manslaughter and lethal assault - Assault/Aggravated Assult - Sexual Offences - Rape, Aggravated Rape - Theft, Robbery, Stealing - Burglary not involving firearm - Burglary in flats, Private Houses - Vehicle theft - Theft out of a motor vehicle - Theft from shops, Department stores etc. - Robbery, Aggravated robbery - Fraud and other acts of dishonesty - Criminal Damage - Narcotics - Driving under the influence

Things to consider:

1 Statistic figures on reported completed murder and manslaughter are higher than the actual number of murder and manslaughter.

2 Year 1950-1984 also including "sexual violation".

3 Year 1965-1967 also including burglary involving fire arms, ammunition and explosives.

4 Including crimes against the Narcotics Regulation year 1950-1983. From 1 July 1983 crimes against the Narcotics Regulation is covered by the Narcotics Drugs (Penal) Act section 5a.

5 Since 1 July 1999 also including driving under the influence of illicit drugs.

6 Four cases of embezzlement reported in 1996 covered appx 24'500 offences.

7 Statistics on crimes against creditors (Penal code ch. 11) and tax crimes are incomplete year 1998 and 1999 due incomplete reporting to Brå.

8 Data for year 1999 and 2000 may have been affected by a system change in year 1999/2000.

9 Excluding data where crime is uncategorized. These data were removed from the statistics from year 2003.