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).

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

Here are a few use cases for this project:

1. Law Enforcement/Security: The model will be beneficial for law enforcement agencies to track and identify stolen vehicles, suspicious vehicles, or vehicles involved in crimes based on plate numbers. It can also be used for parking violations or to monitor traffic congestions in real-time.

2. Parking Management: This model can be used in parking lots or garages to automatically read plate numbers and record the entry and exit of vehicles. It may also streamline the payment process by connecting the plate number with the owners' payment details.

3. Toll Collection: The model can be integrated into toll booth systems to automatically identify the vehicle type and plate number, facilitating automatic digital payment and reducing manual labor and waiting times.

4. Traffic Flow Management: Government or related transportation authorities could use this model to monitor road traffic, identify traffic patterns, and make data-driven decisions on road expansion or traffic light timings.

5. Shipping and Logistics: The model could be used in warehouses or shipping yards to identify, track, and manage trucks entering and exiting the premises, ensuring efficient logistical movement.

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 IOC, how often the IOC met, the relationship of the IOC and the executive committee, how RATT was unique, why RATT was successful, how RATT could be improved, how RATT was funded, and ways in which auto theft could be reduced. Variables in Parts 4 and 5 include the goals of RATT, sources of information about vehicle thefts, strategies used to solve auto theft cases, location of most vehicle thefts, how motor vehicle thefts were impacted by RATT, impediments to the RATT program, suggestions for improving the program, ways in which auto theft could be reduced, and methods to educate citizens about auto theft. In addition, Part 5 also has variables about the type of officers' training, usefulness of maps and other data, descriptions of auto theft rings in terms of the age, race, and gender of its members, and types of cars stolen by rings.

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.

Here are a few use cases for this project:

1. Vehicle Identification in an Inventory System: In a car dealership or large corporate fleet, the OCR_VINPLACA_YOLO_V2 model could be used to automatically identify the VIN/Vehicle Plate numbers of cars in inventory. This can streamline the process of checking in and out cars, aiding in inventory management.

2. Automatic Traffic Fines: Government bodies or traffic authorities may use this model to implement an automatic fine system. Cameras set up on roads run the CV model to capture and interpret the license plate numbers of vehicles breaking traffic laws. They can then automatically send fines to those vehicle owners.

3. Anti-Theft Applications: Surveillance systems can use this model to monitor and detect stolen vehicles. By recognizing the license plate or VIN, the system can alert the authorities when a reported stolen car is detected.

4. Airport or Hotel Car Services: At airports or hotels where valet services need to manage large numbers of vehicles, this model could be used to automatically identify and track cars via their license plates, improving accuracy in assignment and retrieval processes.

5. Drive-thru systems: In drive-thru services like fast food or car wash, this model could be used to identify regular customers and recall their preferences based on license plate, creating a unique customer experience.

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 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. 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 Tuesday through Sunday. 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 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.

Here are a few use cases for this project:

1. Vehicle Theft Prevention and Recovery: The Vin detection model can be used by law enforcement agencies to identify and track stolen vehicles by recognizing the VIN numbers in photos or videos taken from cameras, such as traffic or security cameras, when searching for specific missing cars.

2. Vehicle Insurance Claim Processing: Insurance companies can utilize the Vin detection model to automatically verify and validate vehicle ownership during insurance claims by extracting the VIN number from images submitted by customers. This can significantly reduce fraudulent claims and speed up the claim processing.

3. Vehicle Maintenance and Service Tracking: Automotive service centers can integrate the Vin detection model into their customer management systems, enabling them to maintain accurate service records based on the identified VIN numbers. This allows for a streamlined system that ensures each vehicle receives the correct services and parts.

4. Used Car Marketplace: Online platforms for buying and selling used vehicles can use the Vin detection model to identify VIN numbers of listed vehicles, enabling potential buyers to access important information like the vehicle's history report through services like Carfax or AutoCheck. This can help increase buyer confidence and reduce the prevalence of scams in the used car market.

5. Vehicle Registration and Licensing: Government departments responsible for registering and licensing vehicles can automate the process of verifying vehicle identity by using the Vin detection model on photos submitted during registration or renewal. This not only increases efficiency and accuracy but also reduces the risk of human error in copying down the VIN number manually.

This dataset comes from the Annual Community Survey question related to residents’ feeling of safety and their perceptions about their likelihood of becoming a victim of violent or property crimes. The fear of crime refers to the fear of being a victim of crime as opposed to the actual probability of being a victim of crime. The Annual Community Survey question that relates to this dataset is: “Please indicate how often you worry about each of the following: a) Getting mugged; b) Having your home burglarized when you are not there; c) Being attacked or threatened with a weapon; d) Having your car stolen or broken into; e) Being a victim of identity theft?” Respondents are asked to rate how often they worry about being a victim on a scale of 5 to 1, where 5 means “Frequently” and 1 means “Never” (without "don't know" as an option).This page provides details about the Worry About Being a Victim performance measure. Click on the Showcases tab for any available stories or dashboards related to this data.The performance measure dashboard is available at 1.10 Worry About Being a VictimAdditional InformationSource: Community Attitude SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: CSVPreparation Method: Data received from vendor and entered in CSVPublish Frequency: AnnualPublish Method: ManualData Dictionary

Analysis of ‘1.10 Worry About Being a Victim (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6910da88-3615-44d9-8ee0-9bb9c1add57e on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset comes from the Annual Community Survey question related to residents’ feeling of safety and their perceptions about their likelihood of becoming a victim of violent or property crimes. The fear of crime refers to the fear of being a victim of crime as opposed to the actual probability of being a victim of crime. The Annual Community Survey question that relates to this dataset is: “Please indicate how often you worry about each of the following: a) Getting mugged; b) Having your home burglarized when you are not there; c) Being attacked or threatened with a weapon; d) Having your car stolen or broken into; e) Being a victim of identity theft?” Respondents are asked to rate how often they worry about being a victim on a scale of 5 to 1, where 5 means “Frequently” and 1 means “Never” (without "don't know" as an option).

This page provides details about the Worry About Being a Victim performance measure. Click on the Showcases tab for any available stories or dashboards related to this data.

The performance measure dashboard is available at 1.10 Worry About Being a Victim

Additional Information

Source: Community Attitude Survey

Contact: Wydale Holmes

Contact E-Mail: Wydale_Holmes@tempe.gov

Data Source Type: CSV

Preparation Method: Data received from vendor and entered in CSV

Publish Frequency: Annual

Publish Method: Manual

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Here are a few use cases for this project:

1. Traffic Surveillance: The "LPR note" model can be used in real-time traffic monitoring systems for recognizing license plate classes and categorizing vehicles during peak traffic situations, even distinguishing between cars, motorbikes and more specific classes like taxis ('bbb').

2. Law Enforcement: With the ability to discern between different plate classes, this model could assist law enforcement agencies in vehicle tracking, identifying stolen vehicles, or monitoring vehicular traffic in restricted areas.

3. Parking Management: The model can be employed in automating parking lot systems by identifying vehicle types (e.g., allocated areas for motorcycles and cars) and could also aid in recognizing special categories like taxi ('bbb') to facilitate specific parking provisions.

4. Transportation Services: Ride-hailing services may use this model to enhance their vehicle tracking and dispatch systems, particularly identifying and tracking their taxis ('bbb') in real-time.

5. Toll Collection Systems: The "LPR note" model can be deployed in automated toll collection systems to categorize vehicles and charge tolls accordingly i.e., different rates for cars, motorcycles, or specially designated vehicles like taxis.

The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. There are 19 semantic classes which are compatible with the ones of Cityscapes dataset.

English

The "Taiwan License Plate Character Recognition Research" project focuses on identifying characters primarily based on Taiwan license plate fonts, coupled with license plate detection technology. Through our simple yet practical code, users can assemble a full license plate number according to the X-coordinate of the characters. The aim of this project is to optimize the license plate recognition process, enabling a faster, more accurate capture of license plate numbers.

Generate by GPT4 Here are a few use cases for this project:

1. Automated Parking System: Utilize the "High-precision Taiwan license plate number recognition" model to read and recognize license plates in parking lots, allowing for streamlined and automated entry/exit management and billing.

2. Traffic Surveillance and Enforcement: Integrate the model into traffic monitoring systems to identify traffic violators, such as speeding or running red lights, by capturing and recognizing their license plates, and assist law enforcement in issuing fines or citations.

3. Stolen Vehicle Detection: Leverage the model within police and security systems to identify stolen or flagged vehicles by matching their license plates in real-time with a database of reported stolen or wanted vehicles.

4. Intelligent Transportation System: Incorporate the model into smart city infrastructure for monitoring and predicting traffic flow, analyzing road conditions, and managing traffic signals, based on real-time vehicle count and license-plate identification.

5. Access Control and Security: Implement the model in gated communities, corporate campuses, or sensitive facilities to provide automated access control to authorized vehicles, enhancing security and convenience for residents, employees, and visitors.

Additional Explanation: The images in this project come from multiple different authors' projects. Prior to the creation of this dataset, we performed the following steps on the images:

1. Detected the presence of license plates.
2. Centered and cropped the images around the license plates to reduce training time and maintain the proportion of license plate fonts.
3. Manually inspected the clarity of the license plate letters.
4. Completed preliminary annotations.
5. Conducted semi-automatic annotations using a smaller initial model.
6. Reviewed each image individually.

However, due to the complexity of annotating license plate fonts and the high requirements for image quality, we have only annotated over 700 images with our limited manpower. Notably, each image requires at least seven letters to be marked.

If you have other questions or want to discuss this data set, you can contact: https://t.me/jtx257

注意事項

• 缺點：檢測信心較低。由於我沒有更多時間幫助這個項目，如果您願意幫助我們進行標記，請聯絡我(https://t.me/jtx257)，我們將樂於提供其他技術協助。
• 優勢：圖像精細裁剪。

台灣車牌字元識別研究

英文

High-precision Taiwan license plate number recognition專案主要聚焦於識別基於台灣車牌字體的字元，結合車牌檢測技術。通過我們簡潔實用的程式碼，用戶可以根據字元的X坐標組合出完整的車牌號碼。此項目旨在優化車牌識別過程，使其更快速、準確地捕捉車牌號碼。

由GPT4生成 以下是此項目的幾個**應用案例**：

1. 自動停車系統：利用“台灣車牌字元識別研究”模型，在停車場讀取和識別車牌，從而實現出入口管理和計費的自動化。

2. 交通監控與執法：將模型整合到交通監控系統中，識別違反交通規則的行為，如超速或闖紅燈，通過捕捉並識別其車牌，協助執法部門開出罰單或傳票。

3. 被盜車輛檢測：在警方和安全系統中利用該模型，通過與報告中被盜或通緝車輛的數據庫即時匹配其車牌，識別被盜或被標記的車輛。

4. 智能交通系統：將模型納入智慧城市基礎設施，基於實時車輛計數和車牌識別，用於監測和預測交通流量，分析道路條件，並管理交通信號。

5. 出入控制與安全：在封閉社區、企業園區或敏感設施中實施該模型，為授權車輛提供自動出入控制，提升居民、員工和訪客的安全性和便利性。

額外說明: 該專案的圖片來自多個不同作者的專案。在製作這個資料集之前，我們已經對照片進行了以下幾個步驟:

1. 檢測車牌存在。
2. 以車牌為中心點，切割圖片，旨在降低訓練時間，並保留車牌字型的比例。
3. 進行人眼檢查，確認車牌字母是否清晰。
4. 進行初步的標註。
5. 利用較小的初始模型進行半自動標註。
6. 對每一張照片進行逐一檢查。

然而，由於車牌字型的標註難度較大，並且對照片品質要求嚴格，我們在有限的人力下，僅標註了700多張照片。值得注意的是，每張照片至少有7個字母需要進行標記。

如果對此資料集有其他疑問或想討論的，可聯繫: https://t.me/jtx257

Status Update

2023-11-28

• Although this project is named for its high precision, which is our aspiration, we currently lack the resources (time, manpower) to improve the accuracy of this project.
• If you require temporary high-precision recognition capabilities, please consider the first version training of this project, "robo-train-v1," which may offer a better.