Recorded offences by council area and offence, Victoria
liveOffences recorded by Victoria Police for each police service area and local government area, by offence division, subdivision and subgroup, for the ten years to the end of the latest quarter. This is table 2 of the Crime Statistics Agency's workbook of recorded offences by local government area.
Also called: Victorian crime statistics by LGA, Melbourne crime statistics by council, Crime Statistics Agency recorded offences data tables, CSA recorded offences by local government area.
Part of Victorian recorded offences, 2 tables the publisher releases together: Recorded offences by suburb and offence, Victoria.
- What period does each year cover?
- Each year is the twelve months ending in the month named in year_ending. A year of 2026 with a year_ending of June covers July 2025 to June 2026. Every row of one release uses the same month.
- Where do the rates come from?
- The CSA publishes a rate per 100,000 people for each police service area and each council area beside the count. Both rates are copied as published. This site does not calculate them.
- How often is it updated?
- The CSA releases new tables every quarter, in March, June, September and December. Each release covers ten years and can revise earlier years, so every release is kept here as its own version.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 10 fields, count and filter across all 52,809 rows and diff its versions. Every answer names the version and carries CSA's attribution. No key.
Then ask it: What does Recorded offences by council area and offence, Victoria hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
https://publicdata.au/mcpStreamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?For an assistant that reads the web and has no connector.
The dashboard above, live. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=offence_division&metric=sum.offence_count&year=eq.2026
Offences by offence division where year is 2026: 357,246 B Property and deception offences, 100,463 E Justice procedures offences, 95,322 A Crimes against the person, 34,583 C Drug offences.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/vic-recorded-offences-by-lga/latest/ redirects to the newest version.
https://publicdata.au/d/vic-recorded-offences-by-lga/latest/data.csvIn Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/vic-recorded-offences-by-lga/latest/data.csvIn Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("vic-recorded-offences-by-lga")
pd_attribution(df)install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("vic-recorded-offences-by-lga")
df.attrs["publicdata"]["attribution"]pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/vic-recorded-offences-by-lga/v/2026-09-24/data.duckdb' AS vic_recorded_offences_by_lga (READ_ONLY);
SELECT year, count(*) FROM vic_recorded_offences_by_lga.records GROUP BY 1 ORDER BY 2 DESC;The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/vic-recorded-offences-by-lga/aggregate?group=offence_division&metric=sum.offence_count&year=eq.2026");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| year | integer | Year | The year the twelve months end in. |
| year_ending | string | Year ending | The month the twelve months end in. |
| police_service_area | string | Police Service Area | |
| lga | string | Local Government Area | Local government area as the CSA names it. |
| offence_division | string | Offence Division | |
| offence_subdivision | string | Offence Subdivision | |
| offence_subgroup | string | Offence Subgroup | |
| offence_count | integer | Offence Count | Offences recorded by Victoria Police in the twelve months. |
| psa_rate_per_100000 | number | PSA Rate per 100,000 population | The publisher's rate per 100,000 people in the police service area. |
| lga_rate_per_100000 | number | LGA Rate per 100,000 population | The publisher's rate per 100,000 people in the council area. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is year, police_service_area, lga, offence_subgroup.
A sample of 10 rows
The ten largest offence counts for one council area and offence subgroup in the newest year, largest first. A blank cell is shown as null.
| year | year_ending | police_service_area | lga | offence_division | offence_subdivision | offence_subgroup | offence_count | psa_rate_per_100000 | lga_rate_per_100000 |
|---|---|---|---|---|---|---|---|---|---|
| 2026 | June | Melbourne | Melbourne | B Property and deception offences | B40 Theft | B49 Other theft | 5236 | 2678.12477641 | 2678.12477641 |
| 2026 | June | Melbourne | Melbourne | B Property and deception offences | B40 Theft | B42 Steal from a motor vehicle | 5191 | 2655.10804323 | 2655.10804323 |
| 2026 | June | Casey | Casey | B Property and deception offences | B40 Theft | B42 Steal from a motor vehicle | 4744 | 1100.58726446 | 1100.58726446 |
| 2026 | June | Casey | Casey | E Justice procedures offences | E20 Breaches of orders | E21 Breach family violence order | 4037 | 936.566354688 | 936.566354688 |
| 2026 | June | Melbourne | Melbourne | B Property and deception offences | B40 Theft | B43 Steal from a retail store | 3550 | 1815.76450654 | 1815.76450654 |
| 2026 | June | Wyndham | Wyndham | B Property and deception offences | B40 Theft | B49 Other theft | 3455 | 946.510421102 | 946.510421102 |
| 2026 | June | Geelong | Greater Geelong | E Justice procedures offences | E20 Breaches of orders | E21 Breach family violence order | 3318 | 1088.90602888 | 1102.29045708 |
| 2026 | June | Melbourne | Melbourne | B Property and deception offences | B50 Deception | B53 Obtain benefit by deception | 3237 | 1655.67034019 | 1655.67034019 |
| 2026 | June | Hume | Hume | B Property and deception offences | B40 Theft | B42 Steal from a motor vehicle | 3120 | 1075.83930416 | 1075.83930416 |
| 2026 | June | Greater Dandenong | Greater Dandenong | B Property and deception offences | B40 Theft | B42 Steal from a motor vehicle | 3105 | 1843.92851187 | 1843.92851187 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/vic-recorded-offences-by-lga/latest/data.json
{
"year": 2026,
"year_ending": "June",
"police_service_area": "Ballarat",
"lga": "Ballarat",
"offence_division": "A Crimes against the person",
"offence_subdivision": "A20 Assault and related offences",
"offence_subgroup": "A211 FV Serious assault",
"offence_count": 317,
"psa_rate_per_100000": 237.883404852,
"lga_rate_per_100000": 252.927439679
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. The publisher's row order when absent. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.
- 2026-09-24 52,809 rows10 fieldsxlsx54b62bc2dd0f
versions.json · changes.json · history.tar.zst (732 KB, every version's Parquet and manifest)
Questions
How do I download Recorded offences by council area and offence as a CSV file?
Open https://publicdata.au/d/vic-recorded-offences-by-lga/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/vic-recorded-offences-by-lga/v/2026-09-24/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Recorded offences by council area and offence in Excel?
Yes. https://publicdata.au/d/vic-recorded-offences-by-lga/v/2026-09-24/data.xlsx is a workbook with the 52,809 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/vic-recorded-offences-by-lga/v/2026-09-24/data.csv.gz is the CSV at about a tenth of the size.
What years does Recorded offences by council area and offence cover?
The current version covers 2016 to 2025. Each release from CSA becomes a new dated version here, and earlier versions stay online.
How often is Recorded offences by council area and offence updated?
CSA releases it quarterly. This site checks the portal every week and adds a dated version when the file changes.
Can I use Recorded offences by council area and offence commercially?
Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Recorded offences by council area and offence?
No. The publisher is Crime Statistics Agency, and its page is https://discover.data.vic.gov.au/dataset/data-tables-recorded-offences. This site republishes the publisher's file without changing its content. The original sits beside every version as source.xlsx with its SHA-256, so the two can be compared.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.xlsx so the change can be checked.
The publisher releases Victorian recorded offences as 2 files at once, so all 2 tables here share a version date.