Two crime counts, one city

A reported offence and a recorded criminal incident sound like the same thing. In Victoria’s statistics, they are different counting units. In Melbourne, the gap depends sharply on what kind of offence is being counted.

The question: when does one measure produce a much larger tally than the other—and does that pattern hold across offence types and neighbouring local government areas?

This is a focused exploration of the gap between two official measures, using the shared year ending March 2026 period. It does not claim that either count is the “true” number of crimes. The story is about how statistical units shape the picture the public sees.

What the data says: the largest Melbourne gap is in Betting and gaming offences. The recorded-offence count is 98, compared with 2 criminal incidents. The ratio is a comparison of published counts, not a claim about how many offences happened inside each real-world incident.

1. The totals pull apart differently by offence type

The same offence categories do not produce the same distance between the two measures. This paired view puts the counts on one scale; hover for exact values. Select a legend item to focus on one measure.

2. The biggest gaps are not the biggest counts

A ratio above 1 means the recorded-offence count is larger than the incident count. Categories with fewer than 50 incidents are excluded here so small denominators do not dominate the ranking. This ranking highlights differences in statistical construction, not danger or severity.

3. Is the gap stable over time?

A single year can be an anomaly. This chart follows selected offence groups across the shared reporting years, including theft, deception, and family-violence-order breaches where the workbooks use matching labels. Hover and zoom to inspect the annual movement.

4. Melbourne is part of a wider pattern

The gap is not unique to the central LGA. This heatmap compares offence-count ratios for the highest-volume shared categories across greater Melbourne. Darker cells show larger ratios; hover gives the underlying totals. Blank cells mean the category did not meet the minimum-count rule in that LGA.

5. High volume does not predict the size of the gap

Each point is an LGA–offence-group pair. The horizontal axis shows incident count, the vertical axis the count ratio, and colour shows recorded-offence volume. If volume alone explained the gap, the points would form a clear slope. Hover to identify each place and category; zoom to inspect clusters.

How to read this story

The charts suggest that the gap is category-specific and can persist across years and LGAs. That makes the unit of counting part of the story: a headline total depends on what the data system counts as an offence and what it groups into a criminal incident. These charts do not identify why the measures differ, and they should not be read as a conversion from incidents to offences.

Counting note

The Crime Statistics Agency publishes separate statistical series for recorded offences and criminal incidents. Their definitions and counting rules differ; the incident series uses a principal-offence approach for offence classification. As a result, a ratio between the two published counts is descriptive only. It is not a measure of unreported crime, distinct victims, offending intensity, or community safety. The two files are aligned to the same rolling year ending March 2026 for this report. The workbook structure can change; if the parser stops, inspect the “Table 02” sheet and update the header detection before interpreting results.

Data and method

  • Geography: Melbourne local government area for Charts 1–3; greater Melbourne LGAs for Charts 4–5.
  • Period: year ending March, using only years and offence-subgroup labels present in both official workbooks.
  • Measures: published recorded-offence counts and criminal-incident counts; ratio = recorded-offence count divided by criminal-incident count.
  • Thresholds: Charts 2 and 3 suppress low counts for stability; Charts 4 and 5 require at least 30 incidents per LGA-category cell.
  • Reproducibility: the R Markdown downloads both source workbooks directly from the Crime Statistics Agency. It reshapes the year and count fields at knit time; no observations are typed into the code.
  • Interactivity: hover tooltips, legend toggles, and zoom/pan are provided by Plotly.

References (APA 7)

Crime Statistics Agency. (2026). Data tables: Recorded offences by local government area, year ending March 2026 [Data set]. State Government of Victoria. https://files.crimestatistics.vic.gov.au/2026-06/Data_Tables_LGA_Recorded_Offences_Year_Ending_March_2026.xlsx

Crime Statistics Agency. (2026). Data tables: Criminal incidents by local government area, year ending March 2026 [Data set]. State Government of Victoria. https://files.crimestatistics.vic.gov.au/2026-06/Data_Tables_LGA_Criminal_Incidents_Year_Ending_March_2026.xlsx

Crime Statistics Agency. (n.d.). About the data: Classifications and counting rules. State Government of Victoria. https://www.crimestatistics.vic.gov.au/about-the-data/classifications

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