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).

As of July 19, 2015, the PD District boundaries have been updated through a redistricting process. These new boundaries are not reflected in the dataset yet so you cannot compare data from July 19, 2015 onward to official reports from PD with the Police District column. We are working on an update to the dataset to reflect the updated boundaries starting with data entered July 19 onward.

Incidents derived from SFPD Crime Incident Reporting system Updated daily, showing data from 1/1/2003 up until two weeks ago from current date. Please note: San Francisco police have implemented a new system for tracking crime. The dataset included here is still coming from the old system, which is in the process of being retired (a multi-year process). Data included here is no longer the official SFPD data. We will migrate to the new system for DataSF in the upcoming months.

Provide vehicle theft, license plate theft (including taxis) API and Web services data query.

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.

The Oakland Police Department provides crime data to the public through the City of Oakland’s Crime Watch web site. This site presents the data in a geographic format, which allows users of the information to produce maps and/or reports.

The file that you are about to electronically download, copy, or otherwise retrieve by other means is a tabular representation of the same data without maps or reporting capabilities. Be advised that the exact address of each crime has been substituted with the block address to protect the privacy of the victim.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

Here are a few use cases for this project:

1. Traffic Management: Since the model can recognize license plates, it can be used by traffic authorities for congestion control, toll collection, and license plate-based routing.

2. Vehicle Tracking in Parking Lots: The model can be utilized in parking lots to track the entry and exit of vehicles, thereby automating invoice generation process based on time spent.

3. Security Surveillance: In malls or residential communities, the model can be used to notify security if unregistered or unauthorized cars enter the premises.

4. Stolen Vehicle Detection: The license plate detection model can assist in identifying and tracking stolen vehicles when integrated with national or regional vehicle databases.

5. Law Enforcement: Police can use the model to identify vehicles involved in road traffic violations and crimes, leading to the possible identification and capture of suspects.

The UNIFORM CRIME REPORTING PROGRAM DATA: PROPERTY STOLEN AND RECOVERED, UNITED STATES, 2019 file (also known as the Supplement to Return A) is collected at the agency level and includes detailed monthly data on the nature of crime and the value and type of property stolen and recovered incident to each crime. The Return A Supplement requires that a value be established for property stolen and recovered in each Crime Index category except aggravated assault. It is designed to record the value of property stolen and recovered in the following eleven classifications: Currency/notes, Jewelry and Precious Metals, Clothing and Furs, Locally Stolen Motor Vehicles, Office Equipment, Televisions/Radios, Firearms, Household Goods, Consumable Goods, Livestock, and Miscellaneous. The determination of the value of property stolen is an obligation of the investigating officer, and such information is essential to assure the completeness of a law enforcement investigative report on stolen property. The data were originally assembled by the Federal Bureau of Investigation (FBI) from reports submitted by agencies participating in the UCR. The ICPSR file was processed from Return A Supplement files provided by the FBI.

This dataset displays location for vehicles that have been towed and impounded by the City of Chicago within the last 90 days. Illegally parked vehicles, abandoned vehicles and vehicles used for illegal activities may be towed by the Chicago Police Department, the Department of Streets and Sanitation, the Department of Revenue, Aviation and the office of the City Clerk. After a tow request is issued, an inventory number is assigned by the Department of Streets and Sanitation and a truck is dispatched to tow the requested vehicle to a City auto pound. Disclaimer: This dataset includes vehicles towed or relocated by the City of Chicago; it does not include vehicles towed by a private towing company.

Background Information: Auto Pound Locations (http://j.mp/kG5sgF). Tow Process Overview (http://j.mp/lfBOEP). Common Towing Questions (http://j.mp/imFYlp). Parking and Standing Violations (http://j.mp/ifW8Uj). Related Applications: Find Your Vehicle (http://j.mp/lWn0S7).

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.

There were ******* motor vehicle thefts in England and Wales in 2024/25, compared with ******* in the previous reporting year. Despite recent increases in this type of offence, there were still far fewer vehicle thefts than there were in 2002/03, when there were almost *******. This was followed by a steep ten-year decline, which saw vehicle thefts reduced to just ****** in 2013/14. Links with overall crime The sharp fall in motor vehicle thefts seen between 2002/03 and the mid-2010s, followed by a sudden increase recently tracks a pattern that can be observed in the overall crime figures for the United Kingdom In total, there were approximately **** million crime offences in 2023/24, an increase of over *** million offences when compared with 2013/14. Although this was a higher number of crimes than in the early 2000s, due to population increase, the crime rate for 2023/24 was ****, lower than in 2003/04, when the crime rate was ***** crimes per 1,000 people. Staff and funding cuts to blame? The recent uptick in overall crime has been sudden and severe enough to catch the attention of the British media. It has not gone unnoticed that this rise occurred following cuts to funding for the police which was then followed by a decline in officer numbers These cuts have since been reversed, and funding for the police has again started to increase, although in other areas of the justice system, such as legal aid, funding has remained at reduced levels, when compared with spending before the mid-2010s.

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 dataset contains incident reports recorded by the Norfolk Police Department that occurred over the last five years. Incidents can be searched by type, location, date and time of occurrence. This dataset is updated daily.

description:

This metadata contains information on crime definitions and location obfuscation techniques to protect citizen identification data. Officers responding to incidents have also been redacted for privacy.

• Currency, Notes, Etc.
• Jewelry and Precious Metals
• Clothing and Furs
• Locally Stolen Motor Vehicles
• Office Equipment
• Televisions, Radios, Stereos, Etc.
• Firearms
• Household goods
• Consumable goods
• Livestock

• Single Occupancy Residential (houses, townhouses, duplexes, etc.)
• Other Residential (apartments, tenements, flats, hotels, motels, dormitories, etc.)
• Storage (barns, garages, warehouses, etc.)
• Industrial/Manufacturing
• Other Commercial (stores, restaurants, offices, etc.)
• Community/Public (churches, jails, schools, colleges, hospitals, etc.)
• All Other Structures (out buildings, monuments, buildings under construction, etc.)
• Motor Vehicles (automobiles, trucks, buses, motorcycles, etc.)
• Other Mobile Property (trailers, recreational vehicles, airplanes, boats, etc.)
• Other (crops, timber, fences, signs, etc.)

2. Protecting the identification of citizens and officers:

The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent. It is more common to have masking applied to data that is represented outside of a corporate production system. In other words where data is needed for the purpose of application development, building program extensions and conducting various test cycles. It is common practice in enterprise computing to take data from the production systems to fill the data component, required for these non-production environments.

How we obfuscate data through Donut Masking:

Donut Masking. This technique is similar to random displacement within a circle, but a smaller internal circle is utilized within which displacement is not allowed. In effect, this sets a minimum and maximum level for the displacement. Masked locations are placed anywhere within the allowable area. A slightly different approach to donut masking is the use of a random direction and two random radii: one for maximum and one for minimum displacement. These two techniques only differ slightly in the probability of how close masked locations are placed to the original locations. Both approaches enforce a minimum amount of displacement.

Update daily.

This metadata contains information on crime definitions and location obfuscation techniques to protect citizen identification data. Officers responding to incidents have also been redacted for privacy.

• Currency, Notes, Etc.
• Jewelry and Precious Metals
• Clothing and Furs
• Locally Stolen Motor Vehicles
• Office Equipment
• Televisions, Radios, Stereos, Etc.
• Firearms
• Household goods
• Consumable goods
• Livestock

• Single Occupancy Residential (houses, townhouses, duplexes, etc.)
• Other Residential (apartments, tenements, flats, hotels, motels, dormitories, etc.)
• Storage (barns, garages, warehouses, etc.)
• Industrial/Manufacturing
• Other Commercial (stores, restaurants, offices, etc.)
• Community/Public (churches, jails, schools, colleges, hospitals, etc.)
• All Other Structures (out buildings, monuments, buildings under construction, etc.)
• Motor Vehicles (automobiles, trucks, buses, motorcycles, etc.)
• Other Mobile Property (trailers, recreational vehicles, airplanes, boats, etc.)
• Other (crops, timber, fences, signs, etc.)

2. Protecting the identification of citizens and officers:

The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain usable for the purposes of undertaking valid test cycles. It must also look real and appear consistent. It is more common to have masking applied to data that is represented outside of a corporate production system. In other words where data is needed for the purpose of application development, building program extensions and conducting various test cycles. It is common practice in enterprise computing to take data from the production systems to fill the data component, required for these non-production environments.

How we obfuscate data through Donut Masking:

Donut Masking. This

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police.

The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public.

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.

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.

Supplementary information in relation to offences, including points of entry for burglaries and motor vehicle thefts, value of stolen goods and deception offences.

Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/

Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

Note: All incident locations are mapped to nearby intersections to ensure anonymity. The locations of incidents reported before April 24, 2024 are currently mapped to a slightly different set of intersections than those reported on or after April 24. This may result in slight reporting irregularities for analyses that span this date. We will harmonize all historical data and resolve this as soon as possible. A. SUMMARY Read the detailed overview of this dataset. This dataset includes incident reports that have been filed as of January 1, 2018. These reports are filed by officers or self-reported by members of the public using SFPD’s online reporting system. The reports are categorized into the following groups based on how the report was received and the type of incident: Initial Reports: the first report filed for an incident Coplogic Reports: incident reports filed by members of the public using SFPD’s online reporting system Vehicle Reports: any incident reports related to stolen and/or recovered vehicles Disclaimer: The San Francisco Police Department does not guarantee the accuracy, completeness, timeliness or correct sequencing of the information as the data is subject to change as modifications and updates are completed. B. HOW THE DATASET IS CREATED Data is added to open data once incident reports have been reviewed and approved by a supervising Sergeant or Lieutenant. Incident reports may be removed from the dataset if in compliance with court orders to seal records or for administrative purposes such as active internal affair investigations and/or criminal investigations. Read more about how incident reports are created and approved in the detailed overview of this dataset. C. UPDATE PROCESS Updated automatically daily by 10:00 Pacific D. HOW TO USE THIS DATASET Read more about how to appropriately use identifiers, interpret different kinds of records, and limitations of analysis related to active privacy controls. E. RELATED DATASETS Reference: Police Department Incident Code Crosswalk

The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides as well as Part II crimes of drugs, alcohol offenses, disorderly conduct, embezzlement, family offenses, forgery, fraud, simple assault, stolen property, and vandalism. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

Canadian Police Information Centre - where the public can search for property or motor vehicles that have been reported stolen.

License Plate Reader Market Outlook

In 2023, the global license plate reader market size was estimated at around USD 3.8 billion, with a projected CAGR of 10.5% from 2024 to 2032. By 2032, this market is forecasted to reach approximately USD 9.6 billion, driven by increasing demand for efficient traffic management, enhanced law enforcement capabilities, and the integration of advanced technologies such as AI and machine learning. The rising need for security and surveillance across various sectors, coupled with government initiatives to enhance road safety, is a significant growth factor propelling the market forward.

One of the primary growth factors for the license plate reader market is the escalating need for enhanced traffic management systems. With increasing urbanization and the subsequent rise in the number of vehicles on the road, managing traffic efficiently has become a critical concern for metropolitan areas. License plate readers (LPR) play a pivotal role in this by providing real-time data that helps in minimizing congestion, monitoring traffic flow, and identifying traffic rule violations. Governments and municipal bodies are investing heavily in intelligent transportation systems (ITS) that incorporate LPR technology, which in turn is driving market growth.

Another significant driver is the growing emphasis on law enforcement and public safety. License plate readers are extensively used by police and law enforcement agencies to track stolen vehicles, identify unregistered vehicles, and even apprehend criminals. The integration of LPR with existing surveillance systems has enhanced the capabilities of law enforcement agencies, allowing for quicker response times and more efficient crime prevention. The heightened focus on public safety, especially in regions with high crime rates or terrorism threats, is fostering the adoption of LPR systems.

Moreover, the advent of smart cities is fueling the demand for LPR technology. Smart cities rely heavily on digital infrastructure and data analytics to improve the quality of urban services, and LPR systems are an integral component of this ecosystem. They assist in a variety of applications, from parking management to electronic toll collection, enhancing operational efficiency and user experience. The push towards creating more sustainable and efficient urban environments is a significant growth catalyst for the LPR market.

Automatic License Plate Recognition (ALPR) Systems have become an essential tool in modern traffic management and law enforcement. These systems utilize advanced imaging technology and software algorithms to automatically capture and analyze vehicle license plates in real-time. The integration of ALPR systems with existing surveillance infrastructure allows for seamless data collection and analysis, providing valuable insights into traffic patterns and vehicle movements. This technology not only aids in reducing traffic congestion but also enhances public safety by enabling the quick identification of stolen or unregistered vehicles. As urban areas continue to grow, the demand for efficient and automated solutions like ALPR systems is expected to rise, further driving the growth of the license plate reader market.

Regionally, North America currently dominates the license plate reader market, primarily due to the high adoption rate of advanced surveillance technologies and significant government funding for public safety initiatives. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rapid urbanization, increasing vehicle sales, and substantial investments in smart city projects by countries like China and India.

Component Analysis

The license plate reader market is segmented by component into hardware, software, and services. The hardware segment includes cameras, sensors, and other physical devices required for capturing and reading license plates. The demand for high-resolution cameras with advanced imaging capabilities is particularly high, as they are crucial for accurate plate recognition even in low-light conditions. Technological advancements in camera hardware, such as the development of 4K and night-vision cameras, are significantly contributing to the growth of this segment.

Software is another critical component, encompassing the algorithms and analytics tools used to process and interpret the data captured by the hard