The question

A friend of mine had a parcel stolen after it was delivered. My friend reported it to the delivery company and waited. The parcel never came back, there was no news of any progress, and nobody was ever named.

It is an ordinary story, and that is why it matters. Crime news tends to ask how much crime there is and where it is “dangerous”. These charts ask a quieter question that affects far more people: after a theft like this is recorded, does anyone ever find out who did it?

In the year ending June 2026, Victoria Police recorded 465,100 criminal incidents. 55.1% of them are classed as unsolved: no offender has been identified. Ten years earlier the figure was 47.9%. This report asks what is behind that shift, and where in Victoria it lands.


1. For the first time in a decade, most recorded crime goes unsolved

How to read: each bar is a financial year (July–June). Orange = no offender identified; blue = someone was charged; grey = offender identified but not charged (e.g. caution, diversion). Hover for counts; click a legend item to hide it. Source: Crime Statistics Agency (2026), Criminal incidents by LGA, year ending June 2026.

The share of incidents ending in a charge fell from 39.1% to 30.3%, and the unsolved share crossed 50% in 2025.


2. The growth is concentrated in one kind of crime: theft

How to read: bars to the right grew between 2017 and 2026; bars to the left shrank. Colour shows the broader offence division. Offence types with fewer than 2,000 incidents in both years are excluded. Click legend entries to isolate a division. Source: Crime Statistics Agency (2026), Criminal incidents by LGA, year ending June 2026.

Theft alone added 46,226 incidents (32%), more than every other growing offence type. Theft is also the hardest kind of crime to solve, because there is usually no one to identify.


3. Busier crime areas do not have more unsolved crime

How to read: each bubble is a Local Government Area in 2026; size = number of incidents; colour = police region. Click a region in the legend to hide it; drag to zoom; double-click to reset. LGAs with fewer than 200 incidents excluded. Source: Crime Statistics Agency (2026), Criminal incidents by LGA, year ending June 2026.

The bubbles form a cloud, not a line (r = -0.10). Inner-city Melbourne has a very high crime rate and Boroondara a low one, yet both sit far above the state line.


4. What predicts it is what kind of crime a place has

How to read: same LGAs as above, but the horizontal axis now shows how much of each area’s recorded crime is property-related (theft, burglary, property damage, fraud). Hover any bubble to compare it with Chart 3. Source: Crime Statistics Agency (2026), Criminal incidents by LGA, year ending June 2026.

With the same LGAs on a different axis, the cloud becomes a line (r = 0.97). The places with the most unsolved crime, Maribyrnong, Stonnington, Boroondara, have mostly property crime. At the other end, regional LGAs such as Southern Grampians, Warrnambool, Horsham record a much smaller share of property crime. More of their incidents are assaults and breaches of orders, where the offender is usually known.


5. Find your own area

How to use: type an LGA name in the box (or click any grey line) to highlight it against the Victorian average (black dashed). Select several to compare; double-click the chart to clear. Source: Crime Statistics Agency (2026), Criminal incidents by LGA, year ending June 2026.

The unsolved share rose in 65 of the 76 LGAs shown here, so this is a statewide shift rather than a problem in a few hotspots. Most “unsolved” crime is not violence in the places people tend to fear. It is theft from homes, cars and shops, often in comfortable suburbs, where there is usually no witness and no suspect to name. If more than half of recorded crime now ends with no one identified, then crime counts alone tell us little about whether people get an outcome when they report.


Data and method notes

  • Data: Crime Statistics Agency, Criminal incidents by LGA, years ending June 2017–2026 (Tables 01, 02 and 05), downloaded directly from the CSA in R.
  • Unsolved follows the CSA definition: no alleged offender has been identified. “No charges laid” means an offender was identified but dealt with another way (e.g. caution or diversion).
  • Charge status is recorded at the time the data are extracted. Recent years may have more cases still open, so part of the recent rise may reflect investigations that are still under way.
  • Charts 3–5 exclude LGAs with fewer than 200 incidents, plus Unincorporated Vic and Justice Institutions.
  • All charts were built in R with plotly and crosstalk.

Acknowledgements

I acknowledge the use of Claude (Anthropic, 2026) to assist with reviewing spelling and grammar, improving the clarity of my own writing, and explaining the technical use of R, R Markdown, HTML report generation, and publishing reports on RPubs. The tool was used for language refinement and technical guidance only. I reviewed the suggestions and take responsibility for the accuracy, originality, and final content of my submission. All analysis, visualisations, interpretations, and conclusions presented in this assessment are my own work and comply with the assessment’s guidelines on generative AI use.

Crime data © State of Victoria (Crime Statistics Agency), licensed under CC BY 4.0.

References

Anthropic. (2026). Claude (Opus 5.5 version) [Large language model]. https://claude.ai

Crime Statistics Agency. (2026, September 24). Criminal incident by LGA – Year ending June 2026 [Data set]. DataVic. https://discover.data.vic.gov.au/dataset/criminal-incident/resource/c5750abf-4440-4e86-8bf0-11abd04741a9

Crime Statistics Agency. (n.d.). Glossary and data dictionary. https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary

R Core Team. (2025). R: A language and environment for statistical computing (Version 4.5.2) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://plotly-r.com

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), Article 1686. https://doi.org/10.21105/joss.01686