As part of my postgraduate research, I am undertaking a study titled “Motor Vehicle Theft Analysis Using Machine Learning.” This research aims to analyse patterns, trends, and contributing factors associated with vehicle theft incidents and to develop predictive models to support data-driven insights for crime prevention. To support this research, I respectfully request access to motor vehicle theft data in New Zealand covering the period from 2020 to 2025 or atleast for latest 3 years. I am particularly interested in aggregated or anonymised datasets that may include variables such as incident counts, geographical distribution, time and date of occurrence, vehicle types, and any other relevant attributes, in accordance with applicable privacy and confidentiality regulations. The requested dataset will consist of the following key variables: Incident Information: incident ID, offence type, date reported, date occurred, and time of occurrence Location Data: region, district, suburb or area, and geographic coordinates (latitude and longitude) Vehicle Attributes: vehicle make, vehicle model, vehicle type (e.g., SUV, sedan), model year, and optional features such as colour and fuel type Theft Context (if available): method of theft (e.g., forced entry), presence of security features (alarm, immobiliser), and whether the vehicle was locked Outcome Variables: vehicle recovery status (yes/no), recovery time, and case status (open/closed)

Opening this data would solve the problem of limited availability of high-quality, location-specific motor vehicle theft data, thereby enabling data-driven analysis, improving the accuracy of machine learning-based predictions, and supporting more effective and evidence-based crime prevention strategies.

Opening this data would solve this problem by enabling access to detailed, location-specific and vehicle-level data, which facilitates data-driven analysis, enhances the accuracy of machine learning models, and supports the development of effective, evidence-based motor vehicle theft prevention strategies.