Estimated resident population by Local Government Area, 2001 to 2025, Australia
liveThe ABS Data Explorer table ABS_ANNUAL_ERP_LGA2025, estimated resident population by Local Government Area (LGA 2025), age and sex, 2001 to 2025, read for Local Government Areas, both sexes and all ages only. Each row is one year and council area, with the estimated number of residents at 30 June and the ABS observation status. The ABS codes are kept beside the names it gives them.
Also called: Estimated resident population by LGA, Council area population Australia, Population by Local Government Area ABS, Regional population by LGA.
- What is in each row?
- The ABS's estimate of all the people usually resident in one council area at 30 June of one year from 2001, both sexes and all ages together.
- Is there a breakdown by age and sex?
- The ABS table also gives each council's population by sex and five-year age group. This dataset reads only the totals, which are the figure most uses need. The breakdown is 57 times larger.
- Which council boundaries are used?
- The Local Government Areas of the ABS's 2025 edition, the same edition as the council boundaries dataset here. The ABS has re-estimated every year from 2001 on those boundaries, so a council's figures for earlier years describe the area it covers in 2025. A table on another edition's boundaries is a separate dataset, because the ABS changes some codes between editions.
- Are the figures final?
- The ABS revises recent years when later census and migration data arrive. Each yearly release replaces the figures for the years it revises, and this site keeps every release as a dated version.
- What do the codes beginning with the state digit and ending in 99 mean?
- The ABS gives each state rows for its unincorporated area, for people with no usual address, and for people offshore or on ships. They are kept as the ABS publishes them and are not council areas.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 6 fields, count and filter across all 13,700 rows and diff its versions. Every answer names the version and carries ABS's attribution. No key.
Then ask it: What does Estimated resident population by Local Government Area, 2001 to 2025, Australia hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcp One 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/mcp Streamable 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=lga_name&metric=sum.population&year=eq.2025
Estimated resident population of each council area at 30 June of the newest year: 1,375,301 Brisbane, 691,230 Gold Coast, 532,445 Moreton Bay, 484,630 Unincorporated ACT.
Download it as Excel, CSV, JSON and 5 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 keeps its content.
One workbook with a records sheet of 13,700 typed rows, a fields sheet and a publicdata sheet with the provenance. Dates are real Excel dates. Opens in Excel, Numbers, LibreOffice and Sheets.
UTF-8 without a byte-order mark, RFC 4180 quoting, one header row using the normalised field names. Opens in Excel, Numbers and Sheets.
One object with a publicdata header (publisher, licence, attribution, version, source hash), a fields list and a records array of 13,700 objects with typed values. Blank cells are null.
Columnar and typed. The smallest download and the fastest to query. Reads directly in DuckDB, pandas, Polars, Arrow and Spark. The provenance header is in the file metadata.
One database with a records table, a fields table and a publicdata metadata table. Open it with any SQLite client.
One DuckDB database with a records table of typed columns, a fields table and a publicdata metadata table. It attaches read-only over HTTPS, so DuckDB, Python and R can query it without downloading it.
The first line is the provenance header, then one JSON object per line. For streaming and for tools that read line by line.
The same CSV, gzipped, at about a tenth of the size. curl, pandas, DuckDB and R read it directly.
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/au-population-by-lga-2025/latest/ redirects to the newest version.
https://publicdata.au/d/au-population-by-lga-2025/latest/data.csv In 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/au-population-by-lga-2025/latest/data.csv In 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("au-population-by-lga-2025")
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("au-population-by-lga-2025")
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/au-population-by-lga-2025/v/2026-10-20/data.duckdb' AS au_population_by_lga_2025 (READ_ONLY);
SELECT lga_code, count(*) FROM au_population_by_lga_2025.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/au-population-by-lga-2025/aggregate?group=lga_name&metric=sum.population&year=eq.2025");
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 |
|---|---|---|---|
| lga_code | string | LGA_2025 | |
| lga_name | string | Region | |
| year | integer | TIME_PERIOD | The year whose 30 June the estimate is for. |
| population | integer | OBS_VALUE | |
| obs_status | string | OBS_STATUS | |
| obs_status_meaning | string | Observation Status |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is lga_code, year.
A sample of 10 rows
The ten council areas with the most residents at 30 June of the newest year, most first. A blank cell is shown as null.
| lga_code | lga_name | year | population | obs_status | obs_status_meaning |
|---|---|---|---|---|---|
| 31000 | Brisbane | 2025 | 1375301 | null | null |
| 33430 | Gold Coast | 2025 | 691230 | null | null |
| 35010 | Moreton Bay | 2025 | 532445 | null | null |
| 89399 | Unincorporated ACT | 2025 | 484630 | null | null |
| 10750 | Blacktown | 2025 | 449385 | null | null |
| 21610 | Casey | 2025 | 414929 | null | null |
| 34590 | Logan | 2025 | 403515 | null | null |
| 11570 | Canterbury-Bankstown | 2025 | 389687 | null | null |
| 36720 | Sunshine Coast | 2025 | 381957 | null | null |
| 11650 | Central Coast (NSW) | 2025 | 357816 | null | null |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/au-population-by-lga-2025/latest/data.json
{
"lga_code": "52100",
"lga_name": "Corrigin",
"year": 2001,
"population": 1302,
"obs_status": null,
"obs_status_meaning": null
}
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 that like and ilike ignore case in the ASCII letters A to Z. 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, ignoring case in the ASCII letters A to Z. |
| ilike.*text* | The same as like. Both ignore case in ASCII letters only, so É and é differ. |
| 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. When absent, rows come in the order of the file they are read from. For the newest versions that is the version's own data.parquet, which keeps the publisher's order unless the dataset sorted it. Any other version is read from the Parquet file the answer names in file, and when that file is sorted the answer names its sort in order, which ties follow too. |
| 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 version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer from that version alone. Every version with a table can be queried, and /api/v1/datasets/<slug>/versions lists them. A query on a version the database does not hold is read from its Parquet file, and one that would read too much of it is refused with DuckDB SQL that answers it from the file. 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. Its row tools and the query API answer every version through one query path, so a tool's answer and the query URL it cites give the same rows. A query that would read too much of a file is refused with DuckDB SQL that answers it from the file. It also has search and fetch, the two tools ChatGPT's deep research and company knowledge call by name, which run search_datasets and read a dataset's record in the shapes those features expect. 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 dataset with a table 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.
- 2026-10-20 13,700 rows6 fieldsutf-8-sig3fdcb89ad146
The publisher's server states no change date, so this version is dated by the fetch.
versions.json · changes.json · history.tar.zst (66 KB, every version's Parquet and manifest)
Questions
How do I download Estimated resident population by Local Government Area, 2001 to 2025 as a CSV file?
Open https://publicdata.au/d/au-population-by-lga-2025/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/au-population-by-lga-2025/v/2026-10-20/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON. A dated URL never changes, so use it when the file must stay the same.
Can I open Estimated resident population by Local Government Area, 2001 to 2025 in Excel?
Yes. https://publicdata.au/d/au-population-by-lga-2025/v/2026-10-20/data.xlsx is a workbook with the 13,700 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/au-population-by-lga-2025/v/2026-10-20/data.csv.gz is the CSV at about a tenth of the size.
What years does Estimated resident population by Local Government Area, 2001 to 2025 cover?
The current version covers 2001 to 2025. Each release from ABS becomes a new dated version here, and earlier versions stay online.
How often is Estimated resident population by Local Government Area, 2001 to 2025 updated?
ABS releases it yearly. This site checks the portal every week and adds a dated version when the file changes.
Can I use Estimated resident population by Local Government Area, 2001 to 2025 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 Estimated resident population by Local Government Area, 2001 to 2025?
No. The publisher is Australian Bureau of Statistics, and its page is https://www.abs.gov.au/website-privacy-copyright-and-disclaimer. National Digital converted the publisher's file to other formats. It renamed and typed the columns and kept only those the schema lists. National Digital made these changes, and the publisher is not responsible for them. The original sits beside every version as source.csv 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.csv so the change can be checked.
The licence requires the publisher's attribution. We ask that you also name publicdata.au and link to the version you used.
Australian Bureau of Statistics, ERP by LGA (2025), Age and Sex, 2001 to 2025 (ABS_ANNUAL_ERP_LGA2025), ABS Data Explorer, sourced 19 October 2026, https://dataexplorer.abs.gov.au/vis?df[ds]=ABS_ABS_TOPICS&df[id]=ABS_ANNUAL_ERP_LGA2025&df[ag]=ABS&df[vs]=1.0.0, licensed under CC BY 4.0. Serialised and versioned by National Digital at publicdata.au, https://publicdata.au/d/au-population-by-lga-2025/v/2026-10-20/
The attribution alone is inside every file. Harvard is the author-date form a report or a brief asks for. Paste the HTML on a web page so the links carry.
Australian Bureau of Statistics states the licence, CC BY 4.0, on its own page, which was read and recorded here on 18 October 2026.
Every fetch reads the licence again and stops when it has changed. The fetch that made version 2026-10-20 read it from www.abs.gov.au on 19 October 2026.
- Hugging Faceload_dataset("National-Digital/au-population-by-lga-2025")
- Kagglewith a starter notebook
- ZenodoDOI 10.5281/zenodo.23486167
Each copy carries the same attribution and links back to this page.
- datapackage.jsonFrictionless data package pointing at the latest version
- schema.jsonTable Schema for every field
- schema.xlsxthe same field list as an Excel data dictionary
- versions.jsonevery version with date, rows, fields and hash
- changes.jsondiff for every consecutive pair
- manifest.jsonwhere the bytes came from and when
- Query APIfiltered rows and counts without a download
- openapi.jsonOpenAPI 3.1 for this dataset's query paths
- explore/charts and dashboards in the browser, shareable and embeddable
- index.mdthis page as Markdown
This page carries a schema.org Dataset record and is listed in /.well-known/ard.json and /catalog.json.
Australian Bureau of Statistics publishes this table on data.api.abs.gov.au. Questions about the content go to the publisher. Questions about the files here go to corrections.
Fetched 19 October 2026. Source SHA-256 3fdcb89ad1468cbf…
This page is built from register/au-population-by-lga-2025.yaml on GitHub. Anyone can propose a fix to it, such as a clearer description or a field the publisher renamed, as a pull request.
Report a file that differs from the publisher's · How to contribute