Theft has become an increasing concern across Victoria in recent years, but the rise has not been evenly distributed. This interactive briefing tracks theft patterns from 2016 to 2025, revealing where theft rates have changed most, which communities remain persistent hotspots, and the offence types driving Victoria’s recent increase in theft.
This dashboard uses publicly available datasets from the Crime Statistics Agency (CSA) Victoria. The analysis focuses on recorded theft offences across Victorian Local Government Areas (LGAs) between 2016 and 2025.
The dataset was filtered to theft-related offences and aggregated by year and LGA to calculate offence counts and theft rates per 100,000 population. These measures were used to examine long-term trends, identify hotspot communities and compare changes across Victoria.
Geographic boundary data for Victorian LGAs was obtained from Australian Bureau of Statistics (ABS) boundary data and accessed through the ozmaps R package. This boundary information was used to create the interactive hotspot map.
Key Insights:
The median theft rate declined between 2019 and 2022 before increasing sharply from 2023 onwards.
The middle 50% of LGAs also experienced higher theft rates after 2022, indicating that the increase was widespread rather than isolated.
By 2025, the median theft rate reached its highest level in the decade suggesting theft has become a growing concern across Victoria.
Key Insights:
Melbourne, Maribyrnong and Port Phillip recorded the largest increases in theft rates between 2019 and 2025.
Several LGAs recorded decreases over the same period, showing that theft did not rise evenly across Victoria.
Comparing changes in rates helps identify emerging hotspots, rather than only focusing on areas that already had high theft levels.
Key Insights
Melbourne consistently recorded the highest theft rate throughout the decade remaining well above other LGAs.
Inner-metropolitan LGAs including Yarra, Stonnington, Port Phillip and Maribyrnong also experienced persistently high theft rates.
Most hotspot LGAs showed a decline around 2020–2021 followed by a strong increase after 2022, indicating a broad recovery in theft activity across urban Victoria.
Key Insights
Motor vehicle-related theft and other theft offences accounted for the largest share of theft offences throughout the decade.
Retail theft increased substantially after 2022, contributing to the sharp rise in overall theft recorded in 2024 and 2025.
The recent increase in theft is driven by several offence categories rather than a single type of theft.
Key Insights
Theft rates are unevenly distributed across Victoria, with several LGAs falling into the High and Very High categories.
Inner-metropolitan areas show clear hotspot patterns, although some regional LGAs also record elevated theft rates.
The map supports the previous charts by showing that theft is concentrated in specific locations rather than evenly spread across the state.
Theft rates across Victoria increased sharply after 2022, reaching the highest levels observed during the decade. However, the increase was not evenly distributed, with Melbourne,Yarra,Stonnington, Port Phillip and Maribyrnong remaining the state’s most persistent theft hotspots.
The rise in theft was driven by multiple offence categories, particularly motor vehicle-related theft and retail theft. Overall, the findings show that theft is concentrated in a relatively small number of communities and has become a growing concern across Victoria in recent years.
Crime Statistics Agency Victoria. (2025). Recorded Offences by Local Government Area. https://www.crimestatistics.vic.gov.au/crime-statistics/latest-victorian-crime-data/download-data
Australian Bureau of Statistics. (2021). Local Government Area boundaries. https://www.abs.gov.au
Coombes,M. (2024). ozmaps: Australian maps for visualisation and analysis in R. https://cran.r-project.org/package=ozmaps
GeeksforGeeks. (2025). Plot function in R. https://www.geeksforgeeks.org/r-language/plot-function-in-r/
Google. (2026). Gemini [Large language model] https://gemini.google.com/
I used Gemini to help me understand data. This tool supported me in drafting an outline of the key requirements and suggesting a possible flow. I applied my logic to develop the overall work and I did not copy any content from Gemini’s result.
AI Google Gemini. (2026, June 08). Data Exploration [Generative AI chat]. https://www.google.com/gemini