A Guide to the ABS's Boundary Datasets

This guide explores the get_boundary_data and prepare_boundaries function. These functions simplify retrieving boundary datasets from the Australian Bureau of Statistics (ABS) including correspondence tables and allocation files with specific boundary types like Commonwealth Electoral Divisions (CED), State Electoral Divisions (SED), and Postal Areas (POA). These datasets enable users to align election results across changing boundaries and integrate demographic data, supporting research into voting trends and voter demographics. Below, we detail the function’s parameters, available datasets, practical usage examples, and the context of ABS boundary data.

Introduction to ABS Boundary Datasets

The ABS provides boundary datasets as part of the Australian Statistical Geography Standard (ASGS), a framework for collecting and disseminating statistical data across geographic areas (ABS ASGS). These datasets are essential for electoral analysis, as electoral boundaries, such as CEDs, change periodically due to population shifts and redistributions. The get_boundary_data function streamlines access to these datasets, allowing users to retrieve correspondence tables for converting data between Census years, allocation tables for mapping smaller areas to larger ones, and specific boundary definitions for electoral and postal areas. This guide explains how to use these datasets effectively, their importance in electoral studies, and how to handle nuances like boundary changes over time.

Why Boundary Datasets Matter

Electoral boundaries evolve, making it challenging to compare election results across different years. For example, a division’s boundaries in 2013 may differ significantly from those in 2025. Correspondence tables help adjust historical election results to current boundaries, enabling consistent trend analysis. Allocation tables map smaller areas, like Statistical Area Level 1 (SA1) or Mesh Blocks (MB), to larger electoral divisions, facilitating data aggregation or mapping. Additionally, datasets like POA allocations allow for converting election data to postal codes, useful for campaign targeting. By integrating these datasets with Australian Electoral Commission (AEC) election data, researchers can analyse voting patterns alongside demographic factors like income or education from the Census.

The `get_boundary_data` Function

The get_boundary_data function retrieves ABS boundary datasets, leveraging internal metadata to map parameters to specific files. It downloads data from ABS servers, typically in Excel or CSV format, and returns a data frame. Users can specify the dataset by reference date (ref_date), geographic level (level), and data type (type), with options to retrieve raw or processed data. This function is particularly useful for aligning AEC election data, often provided at the SA1 level, with electoral boundaries that may be defined using MBs or other units.

Parameters	Description	Default	Valid Options
`ref_date`	Year of the boundary dataset	None	2011, 2013, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2024 (varies by `level`)
`level`	Geographic or boundary level	“CED”	“SA1”, “MB”, “CED”, “SED”, “POA”
`type`	Type of dataset	“allocation”	“correspondence”, “allocation”

Dataset Types

Correspondence Tables: Convert data between different boundary sets or Census years (e.g., 2011 SA1 to 2016 SA1), providing proportional allocations based on population or area overlap.
Allocation Tables: Assign smaller geographic units (e.g., SA1, MB) to larger ones (e.g., CED, SED), indicating which larger area each smaller unit belongs to.

Key Considerations

Boundary Changes: CEDs and SEDs switched from SA1-based to MB-based allocations in 2021, requiring MB to SA1 allocation tables for consistency with AEC data.
Supplementary Data: For accurate 2022 and 2025 election analyses, incorporate AEC redistribution data for Victoria, Western Australia, and the Northern Territory.
Data Availability: Not all years are available for all boundary types; verify availability below.

Understanding Correspondence and Allocation Tables

Correspondence Tables

Correspondence tables facilitate data conversion between different geographic boundaries or between the same boundary type across Census years. They provide ratios indicating how much of one area overlaps with another. For electoral analysis, these tables are critical for adjusting historical election results to current boundaries. For example, to compare 2013 election results with 2016 boundaries, you need the 2011 SA1 correspondence table to convert 2006 Collection Districts (CD) to 2011 SA1s, aligning with 2013 CEDs.

Use Case: Convert 2016 election results from 2011 SA1s to 2021 SA1s to match 2024 CED boundaries for trend analysis.

Allocation Tables

Allocation tables assign smaller geographic units to larger ones, such as mapping SA1s to CEDs or MBs to SEDs. These tables are essential for aggregating election data or determining which electoral division a specific area belongs to. For instance, an SA1 to CED allocation table lists which CED each SA1 is part of, based on its geographic centroid or majority overlap.

Use Case: Identify all SA1s within a specific CED to analyse demographic characteristics using Census data.

Available Datasets and Examples

The following sections detail the datasets available through get_boundary_data, including their coverage, key columns, and practical usage usage examples. Each section includes sample data to illustrate the dataset’s structure.

Main Structure and Greater Capital City Statistical Areas

Correspondence Tables

Correspondence tables convert data between Census years, enabling alignment of election results with changing statistical areas. They are available for SA1 and MB levels, covering transitions like 2006 CD to 2011 SA1, 2011 SA1 to 2016 SA1, and 2016 SA1 to 2021 SA1.

Usage: Adjust historical election results to current boundaries or align Census data with electoral divisions.

2013 Election results: the AEC provides count data per 2006 CD, meaning the 2011 SA1 correspondence table is needed to convert from 2006 CDs to 2011 SA1s and thus to be in line with the 2011 Census data and the ABS’s 2013 CEDs.
2016 Election results: the AEC provides count data per 2011 SA1, meaning the 2016 SA1 correspondence table is needed to convert from 2011 SA1s to 2016 SA1s and thus to be in line with the 2016 Census data and the ABS’s 2016 CEDs.
2019 and 2022 Election results: the AEC provides count data per 2016 SA1, meaning the 2021 SA1 correspondence table is needed to convert from 2016 SA1s to 2021 SA1s and thus to be in line with the most recent Census and to be able to convert election results into the ABS’s 2024 CEDs (current boundaries used for the 2025 election).

Example Usage (2006 CD -> 2011 SA1):

df <- get_boundary_data(
  ref_date = 2011,
  level = "SA1",
  type = "correspondence"
)

Sample Data (2006 CD -> 2011 SA1):

	CD_CODE_2006	SA1_7DIGITCODE_2011	SA1_MAINCODE_2011	RATIO_FROM_TO
2	1010101	1117908	10902117908	0.5789474

Example Usage (2011 -> 2016):

df <- get_boundary_data(
  ref_date = 2016,
  level = "SA1", # OR "MB"
  type = "correspondence"
)

Sample Data (2011 SA1 -> 2016 SA1):

	SA1_MAINCODE_2011	SA1_7DIGITCODE_2011	SA1_MAINCODE_2016	SA1_7DIGITCODE_2016	RATIO_FROM_TO
2	10101100101	1100101	10105153965	1153965	1

Sample Data (2011 MB -> 2016 MB):

	MB_CODE_2011	MB_CODE_2016	RATIO_FROM_TO
2	80000010000	80000010000	1

Example Usage (2016 -> 2021):

df <- get_boundary_data(
  ref_date = 2021,
  level = "SA1", # OR "MB"
  type = "correspondence"
)

Sample Data (2016 SA1 -> 2021 SA1):

SA1_MAINCODE_2016	SA1_CODE_2021	RATIO_FROM_TO	INDIV_TO_REGION_QLTY_INDICATOR	OVERALL_QUALITY_INDICATOR	BMOS_NULL_FLAG
10102100701	10102100701	1	Good	Good	0

Sample Data (2016 MB -> 2021 MB):

MB_CODE_2016	MB_CODE_2021	RATIO_FROM_TO	INDIV_TO_REGION_QLTY_INDICATOR	OVERALL_QUALITY_INDICATOR	BMOS_NULL_FLAG
10000009499	10000009499	1	Good	Good	0

Allocation

Allocation tables map smaller geographic units (SA1, MB) to larger ones, such as SA1 to STATE or MB to SA1, facilitating data aggregation and boundary identification.

Example Usage:

df <- get_boundary_data(
  ref_date = 2021, # OR 2011, 2016
  level = "MB", # OR "SA1"
  type = "allocation" # = default
)

Sample Data (2021 MB):

MB_CODE_2021	MB_CATEGORY_2021	CHANGE_FLAG_2021	CHANGE_LABEL_2021	SA1_CODE_2021	SA2_CODE_2021	SA2_NAME_2021	SA3_CODE_2021	SA3_NAME_2021	SA4_CODE_2021	SA4_NAME_2021	GCCSA_CODE_2021	GCCSA_NAME_2021	STATE_CODE_2021	STATE_NAME_2021	AUS_CODE_2021	AUS_NAME_2021	AREA_ALBERS_SQKM	ASGS_LOCI_URI_2021
10000010000	Residential	0	No change	10901117207	109011172	Albury - East	10901	Albury	109	Murray	1RNSW	Rest of NSW	1	New South Wales	AUS	Australia	0.0209	http://linked.data.gov.au/dataset/asgsed3/MB/10000010000

Sample Data (2021 SA1):

SA1_CODE_2021	CHANGE_FLAG_2021	CHANGE_LABEL_2021	SA2_CODE_2021	SA2_NAME_2021	SA3_CODE_2021	SA3_NAME_2021	SA4_CODE_2021	SA4_NAME_2021	GCCSA_CODE_2021	GCCSA_NAME_2021	STATE_CODE_2021	STATE_NAME_2021	AUS_CODE_2021	AUS_NAME_2021	AREA_ALBERS_SQKM	ASGS_LOCI_URI_2021
10102100701	0	No change	101021007	Braidwood	10102	Queanbeyan	101	Capital Region	1RNSW	Rest of NSW	1	New South Wales	AUS	Australia	362.8727	http://linked.data.gov.au/dataset/asgsed3/SA1/10102100701

Non ABS Structures

Non-ABS Structures include CED, SED, and POA, which are approximations of official boundaries used for statistical purposes.

CED

CEDs are ABS approximations of AEC federal electoral boundaries, used for the House of Representatives. Prior to 2021, CEDs were constructed from SA1s; since 2021, they use MBs, requiring MB to SA1 allocation tables for consistency with AEC data.

CED Release Notes:

Year	Release Date	Details
2011	July 2011	Aligned with 2011 Census boundaries.
2013	July 2012	Included SA redistribution (13 March 2012). Used for 2013 Federal Election (7 September 2013).
2016	July 2016	Aligned with 2016 Census boundaries. Used for 2016 Federal Election (2 July 2016).
2017	31 October 2017	Included redistributions in NSW (25 February 2016), WA (19 January 2016), ACT (28 January 2016).
2018	20 August 2018	Included redistributions in Qld (27 March 2018), Vic (13 July 2018), ACT (13 July 2018), SA (20 July 2018), NT (27 March 2018), Tas (14 November 2017). Used for 2019 Federal Election (18 May 2019).
2021	20 July 2021	Aligned with 2021 Census boundaries, switched to MB-based allocations. Used for 2022 Federal Election (21 May 2022). Excludes Vic (26 July 2021) and WA (2 August 2021) redistributions.
2024	December 2024	Included redistributions in NSW (27 November 2024), Vic (26 July 2021, 27 November 2024), WA (2 August 2021, 27 November 2024). Used for 2025 Federal Election (3 May 2025). Excludes NT redistribution (26 March 2025)

Example Usage:

df <- get_boundary_data(
  ref_date = 2024, # OR 2011, 2013, 2016, 2017, 2018, 2021
  level = "CED" # = default
)

Sample Data (2024):

MB_CODE_2021	CED_CODE_2024	CED_NAME_2024	STATE_CODE_2021	STATE_NAME_2021	AUS_CODE_2021	AUS_NAME_2021	AREA_ALBERS_SQKM	ASGS_LOCI_URI_2021
10396560000	101	Banks	1	New South Wales	AUS	Australia	0.0257	https://linked.data.gov.au/dataset/asgsed3/MB/10396560000

Sample Data (2018):

SA1_MAINCODE_2016	CED_CODE_2018	CED_NAME_2018	STATE_CODE_2016	STATE_NAME_2016	AREA_ALBERS_SQKM
11904137920	101	Banks	1	New South Wales	0.0452

Supplementary Data: For accurate 2022 and 2025 election analyses, incorporate AEC redistribution data:

2022 Election: Use Vic (Vic Redistribution) and WA (WA Redistribution) SA1 to CED mappings to update the 2021 CED product.
2025 Election: Use NT (NT Redistribution) SA1 to CED mappings to update the 2024 CED product until the 2025 CEDs are released (22 July 2025).

To retrieve these datasets, use the get_file in the scgUtils package:

url <- "https://www.aec.gov.au/redistributions/2021/vic/final-report/files/vic-by-SA2-and-SA1.xlsx"
Vic <- scgUtils::get_file(url, source = "web")

SA1 code (2016 SA1s)	New electoral division from 26 July 2021	Old electoral division as at 15 July 2020	SA2 Name (2016 SA2s)	Actual enrolment 15/7/2020	Projected enrolment 26/1/2025
2100101	Ballarat	Ballarat	Alfredton	306	390

url <- "https://www.aec.gov.au/redistributions/2021/wa/final-report/files/wa-by-SA2-and-SA1.xlsx"
WA <- scgUtils::get_file(url, source = "web")

SA1 code (2016 SA1s)	New electoral division from 2 August 2021	Old electoral division as at 15 July 2020	SA2 Name (2016 SA2s)	Actual enrolment 15/7/2020	Projected enrolment 2/2/2025
5118501	Brand	Brand	Baldivis	1	1

url <- "https://www.aec.gov.au/redistributions/2024/nt/final-report/files/Northern-Territory-electoral-divisions-SA1-and-SA2.xlsx"
NT <- scgUtils::get_file(url, source = "web")

New Electoral Division from 4 March 2025	Old electoral division as at Thursday 22 February 2024	Statistical Area Level 2 (SA2) Name (2021 SA2s)	Statistical Area Level 1 (SA1) Code (7-digit) (2021 SA1s)	Actual enrolments Thursday 22 February 2024	Projected enrolment Monday 4 September 2028
LINGIARI	LINGIARI	Berrimah	7101203	159	226

SED

SEDs are ABS approximations of state and territory legislative boundaries, used for state elections. Prior to 2021, SEDs were allocated from SA1s; since 2021, they use MBs.

Usage: Analyse federal election results with state election results.

Example Usage:

df <- get_boundary_data(
  ref_date = 2024, # OR 2011, 2016, 2017, 2018, 2019, 2020, 2021, 2022
  level = "SED"
)

Sample Data (2024):

MB_CODE_2021	SED_CODE_2024	SED_NAME_2024	STATE_CODE_2021	STATE_NAME_2021	AUS_CODE_2021	AUS_NAME_2021	AREA_ALBERS_SQKM	ASGS_LOCI_URI_2021
10000260000	10001	Albury	1	New South Wales	AUS	Australia	0.014	https://linked.data.gov.au/dataset/asgsed3/MB/10000260000

Sample Data (2019):

SA1_MAINCODE_2016	SED_CODE_2019	SED_NAME_2019	STATE_CODE_2016	STATE_NAME_2016	AREA_ALBERS_SQKM
10102100701	10031	Goulburn	1	New South Wales	362.8727

POA

POAs are ABS approximations of Australia Post postcodes, useful for converting election data to postal codes for campaign targeting or social media analysis.

Usage: Map election results to postcodes for targeted analysis.

Note: Prior to 2016, POAs were allocated from 2011 SA1s; since 2016, they use MBs, requiring MB to SA1 allocation tables for consistency.

Example Usage:

df <- get_boundary_data(
  ref_date = 2021, # OR 2011, 2016
  level = "POA"
)

Sample Data (2021):

MB_CODE_2021	POA_CODE_2021	POA_NAME_2021	AUS_CODE_2021	AUS_NAME_2021	AREA_ALBERS_SQKM	ASGS_LOCI_URI_2021
70034860000	0800	0800	AUS	Australia	0.0434	http://linked.data.gov.au/dataset/asgsed3/MB/70034860000

Sample Data (2011):

SA1_MAINCODE_2011	POA_CODE_2011	POA_NAME_2011	AREA_ALBERS_SQKM
70101100206	800	0800	0.4481936

The `prepare_boundaries` Function

While get_boundary_data retrieves individual ABS boundary files, the prepare_boundaries function automates the complex process of building correspondence tables between election events and target boundaries. It handles:

Chaining multiple correspondence files across ASGS editions
Merging with allocation tables for CED, POA, or SA1 targets
Applying redistribution adjustments for recent elections (Vic/WA 2021, NT 2024)

This function is essential for comparing election results across different boundary configurations or linking election data to Census demographics.

Parameters	Description	Default	Valid Options
`event`	Source election whose results you want to re-map	“2025 Federal Election”	“2025 Federal Election”, “2023 Referendum”, “2022 Federal Election”, “2019 Federal Election”, “2016 Federal Election”, “2013 Federal Election”
`compare_to`	Target boundaries to map to	“2025 Federal Election”	Elections, Census SA1s, or Postcodes (see below)
`process`	Remove geographic units with invalid ratios	TRUE	TRUE, FALSE

Source Event Base Geographies

Each election event uses a specific ABS geographic base for its SA1/CD-level results:

Event	Base Geography	Notes
2013 Federal Election	2006 Census Collector Districts (CD)	Pre-SA1 geography
2016 Federal Election	2011 SA1	ASGS Edition 1
2019 Federal Election	2016 SA1	ASGS Edition 2
2022 Federal Election	2016 SA1	ASGS Edition 2
2023 Referendum	2016 SA1	ASGS Edition 2
2025 Federal Election	2021 SA1	ASGS Edition 3

Valid Target Options

Target Type	Options	Description
Federal Elections	“2013 Federal Election”, “2016 Federal Election”, “2019 Federal Election”, “2022 Federal Election”, “2025 Federal Election”	Maps to CED boundaries used for that election
Referendum	“2023 Referendum”	Maps to CED boundaries used for the Voice referendum
Census SA1	“2011 Census”, “2016 Census”, “2021 Census”	Maps to SA1 boundaries for that Census year
Postcodes	“2011 Postcodes”, “2016 Postcodes”, “2021 Postcodes”	Maps to Postal Areas (POA) for that year

Important: You cannot map an event to boundaries from an earlier ASGS edition. For example, you cannot map the 2025 Federal Election (SA1 2021) to 2016 Census boundaries.

Use Case 1: Comparing Elections Over Time

The most common use case is comparing election results across different elections, which requires mapping historical results to current boundaries (or vice versa).

Mapping to Current (2025) Boundaries

To analyse how voting patterns have changed over time using consistent 2025 electoral boundaries:

df <- prepare_boundaries(
  event = "2022 Federal Election", # OR "2019 Federal Election", "2016 Federal Election", "2013 Federal Election", "2023 Referendum"
  compare_to = "2025 Federal Election"
)

SA1_CODE_2021	SA1_MAINCODE_2016	RATIO_16SA1_21SA1	SA1_7DIGITCODE_2016	CED_NAME_2024	SA1_7DIGITCODE_2021
10102100701	10102100701	1	1100701	Eden-Monaro	1100701
10102100702	10102100702	1	1100702	Eden-Monaro	1100702
10102100703	10102100703	1	1100703	Eden-Monaro	1100703

Use cases:

Compare election results from any previous election using the same 2025 electoral boundaries
Track voting trends across multiple elections (e.g., 2019, 2022, 2025) on consistent boundaries
Analyse relationship between Voice referendum voting and 2025 election results
Long-term trend analysis from 2013 to 2025 (chains through CD 2006 → SA1 2011 → SA1 2016 → SA1 2021)

Mapping to Historical Boundaries (2022 and earlier)

These mappings are useful for historical research, redistribution impact studies, or aligning with data published using older boundary definitions.

df <- prepare_boundaries(
  event = "2016 Federal Election", # OR "2013 Federal Election"
  compare_to = "2022 Federal Election" # OR "2023 Referendum", "2019 Federal Election", "2016 Federal Election", "2013 Federal Election"
)

SA1_MAINCODE_2016	SA1_7DIGITCODE_2016	SA1_MAINCODE_2011	SA1_7DIGITCODE_2011	RATIO_11SA1_16SA1	CED_NAME_2018
10102100701	1100701	10102100701	1100701	1.0000000	Hume
10102100702	1100702	10102100702	1100702	1.0000000	Eden-Monaro
10102100702	1100702	10102100710	1100710	0.0062026	Eden-Monaro

Use cases:

Historical research: Analyse elections in their original boundary context for academic studies
Census alignment: Link to Census data published with period-specific CED approximations
Redistribution impact analysis: Compare how votes would count under old vs new boundaries
Media archives: Recreate historical election maps or validate old publications
State election comparisons: Align federal results with state electoral boundaries from the same era

Notes:

The 2022 election and 2023 referendum used identical boundaries (2021 CEDs with Vic/WA adjustments)
See the Valid Combinations Reference table below for all supported event/boundary combinations

Use Case 2: Linking to Census Demographics

Map election results to Census SA1 boundaries to analyse voting patterns alongside demographic data like income, education, or age profiles.

Mapping to Current (2021) Census

df <- prepare_boundaries(
  event = "2022 Federal Election", # OR "2025 Federal Election", "2019 Federal Election", "2016 Federal Election", "2013 Federal Election"
  compare_to = "2021 Census"
)

SA1_MAINCODE_2016	SA1_CODE_2021	RATIO_16SA1_21SA1	SA1_7DIGITCODE_2016
10102100701	10102100701	1	1100701
10102100702	10102100702	1	1100702
10102100703	10102100703	1	1100703

Use cases:

Link any election results to current Census demographics (income, education, age profiles, etc.)
2025 election results already use 2021 SA1s (direct mapping)
Earlier elections require correspondence chains (e.g., 2013 chains through CD 2006 → SA1 2011 → SA1 2016 → SA1 2021)

Mapping to Historical Census (2016/2011)

df <- prepare_boundaries(
  event = "2019 Federal Election", # OR "2022 Federal Election", "2016 Federal Election", "2013 Federal Election"
  compare_to = "2016 Census" # OR "2011 Census"
)

SA1_MAINCODE_2016	SA1_7DIGITCODE_2016
10102100701	1100701
10102100702	1100702
10102100703	1100703

Use cases:

Period-matched analysis: Link elections to the Census closest in time (e.g., 2019 election with 2016 Census demographics)
Longitudinal studies: Track demographic shifts by comparing elections against their contemporaneous Census
Historical research: Reproduce analyses as they would have been done at the time
Direct mappings: 2019/2022 elections already use 2016 SA1s; 2016 election uses 2011 SA1s

Note: Cannot map 2025 election to 2016/2011 Census (earlier ASGS editions). See Valid Combinations Reference table below.

Use Case 3: Mapping to Postcodes

Map election results to Postal Areas (POA) for campaign targeting, social media analysis, or linking to postcode-based datasets.

Mapping to Current (2021) Postcodes

df <- prepare_boundaries(
  event = "2022 Federal Election", # OR "2025 Federal Election", "2019 Federal Election", "2016 Federal Election", "2013 Federal Election"
  compare_to = "2021 Postcodes"
)

SA1_CODE_2021	SA1_MAINCODE_2016	RATIO_16SA1_21SA1	SA1_7DIGITCODE_2016	POA_NAME_2021
10102100701	10102100701	1	1100701	2580
10102100702	10102100702	1	1100702	2622
10102100703	10102100703	1	1100703	2622

Use cases:

Campaign targeting: Convert election results to postcodes for direct mail or advertising campaigns
Social media analysis: Link voting patterns to postcode-based social media demographics
Commercial data integration: Join election results with postcode-based datasets (e.g., consumer behaviour, property data)
Service planning: Analyse voting patterns by postcode for government or NGO service delivery planning

Mapping to Historical Postcodes (2016/2011)

df <- prepare_boundaries(
  event = "2016 Federal Election", # OR "2019 Federal Election", "2013 Federal Election"
  compare_to = "2016 Postcodes" # OR "2011 Postcodes"
)

SA1_MAINCODE_2016	SA1_MAINCODE_2011	SA1_7DIGITCODE_2011	SA1_7DIGITCODE_2016	RATIO_11SA1_16SA1	POA_NAME_2016
10102100701	10102100701	1100701	1100701	1	2580
10102100701	10102100701	1100701	1100701	1	2622
10102100702	10102100702	1100702	1100702	1	2622

Use cases:

Historical commercial data: Link elections to postcode-based datasets from that period
Postcode boundary changes: Some postcodes have been split, merged, or renamed over time
Archival research: Reproduce analyses using postcode definitions as they existed at the time

Note: Cannot map 2025 election to 2016/2011 Postcodes (earlier ASGS editions). See Valid Combinations Reference table below.

Valid Combinations Reference

The following table summarises all valid event and compare_to combinations. An ✓ indicates a valid combination; an ✗ indicates the combination is not supported (typically because it would require mapping to an earlier ASGS edition).

Event ↓ / Compare to →	2013 Election	2016 Election	2019 Election	2022 Election	2023 Referendum	2025 Election	2011 Census	2016 Census	2021 Census	2011 POA	2016 POA	2021 POA
2013 Federal Election	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
2016 Federal Election	✗	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
2019 Federal Election	✗	✗	✓	✓	✓	✓	✗	✓	✓	✗	✓	✓
2022 Federal Election	✗	✗	✓	✓	✓	✓	✗	✓	✓	✗	✓	✓
2023 Referendum	✗	✗	✓	✓	✓	✓	✗	✓	✓	✗	✓	✓
2025 Federal Election	✗	✗	✗	✗	✗	✓	✗	✗	✓	✗	✗	✓

Introduction to ABS Boundary Datasets

Why Boundary Datasets Matter

The get_boundary_data Function

Dataset Types

Key Considerations

Understanding Correspondence and Allocation Tables

Correspondence Tables

Allocation Tables

Available Datasets and Examples

Main Structure and Greater Capital City Statistical Areas

Correspondence Tables

Example Usage (2006 CD -> 2011 SA1):

Sample Data (2006 CD -> 2011 SA1):

Example Usage (2011 -> 2016):

Sample Data (2011 SA1 -> 2016 SA1):

Sample Data (2011 MB -> 2016 MB):

Example Usage (2016 -> 2021):

Sample Data (2016 SA1 -> 2021 SA1):

Sample Data (2016 MB -> 2021 MB):

Allocation

Example Usage:

Sample Data (2021 MB):

Sample Data (2021 SA1):

Non ABS Structures

CED

Example Usage:

Sample Data (2024):

Sample Data (2018):

SED

Example Usage:

Sample Data (2024):

Sample Data (2019):

POA

Example Usage:

Sample Data (2021):

Sample Data (2011):

The prepare_boundaries Function

Source Event Base Geographies

Valid Target Options

Use Case 1: Comparing Elections Over Time

Mapping to Current (2025) Boundaries

Mapping to Historical Boundaries (2022 and earlier)

Use Case 2: Linking to Census Demographics

Mapping to Current (2021) Census

Mapping to Historical Census (2016/2011)

Use Case 3: Mapping to Postcodes

Mapping to Current (2021) Postcodes

Mapping to Historical Postcodes (2016/2011)

Valid Combinations Reference

The `get_boundary_data` Function

The `prepare_boundaries` Function