This dataset contains estimates of the resident population and estimates of the components of population change as at 30 June for the years 2001-2020. The data is aggregated to the 2020 Australian Statistical Geography Standard (ASGS) Local Government Areas (LGA). This data is sourced from the Australian Bureau of Statistics (Catalogue Number: 3218.0). For more information please visit the Regional population methodology. Notes: The population estimates in this issue are final for 2001 to 2016, revised for 2017 to 2019, and preliminary for 2020. Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimated resident population is based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. AURIN has ingested this dataset in its GeoPackage format.

This dataset contains statistics for deaths and mortality in Australia. It includes all deaths that occurred and were registered in Australia, including deaths of persons whose place of usual residence was overseas. Deaths of Australian residents that occurred outside Australia may be registered by individual Registrars, but are not included in Australian Bureau of Statistics (ABS) death statistics. Standardised death rates in this dataset differ from those in the ABS.Stat datasets and commentary. Standardised death rates in this dataset are averaged using data for the three years ending in the reference year. They are calculated for each calendar year and then averaged. Standardised death rates in the ABS.Stat datasets and commentary are based on death registration data for the reference year only. Null values represent data not available for publication This dataset uses deaths and estimated resident population (ERP) for Statistical Area 3 (SA3) of Australia for 30 June 2012 to 2020, according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). ERP is final for 2012 to 2016, revised for 2017 to 2019 and preliminary for 2020, based on the 2016 Census of Population and Housing. Data has been sourced from the September 2021 release. For more information including which ERP was used in this dataset please visit the Australian Bureau of Statistics (ABS) Explanatory Notes. AURIN has spatially enabled the original data from the ABS with the 2016 SA3 boundaries.

This dataset presents the preliminary estimates of the resident population by age and sex as at 30 June 2020. The data is aggregated to the 2020 edition of the Local Government Areas (LGA). This data is ABS data available from the Australian Bureau of Statistics. For more information please refer to the Methodology. Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period.

Australia Average Number of Dependent Children in Household: Multiple Family data was reported at 1.200 Person in 2020. This records a decrease from the previous number of 1.400 Person for 2018. Australia Average Number of Dependent Children in Household: Multiple Family data is updated yearly, averaging 1.300 Person from Jun 2004 (Median) to 2020, with 9 observations. The data reached an all-time high of 1.500 Person in 2016 and a record low of 1.200 Person in 2020. Australia Average Number of Dependent Children in Household: Multiple Family data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H039: Survey of Income and Housing: Average Number of Dependent Children in Household: by Family Composition.

This dataset presents the preliminary estimates of the resident population by age and sex as at 30 June 2020. The data is aggregated to Statistical Areas Level 2 (SA2), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Methodology . Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period.

2020 ASGS Non ABS Structures in GeoPackage format.

This dataset contains estimates of the resident population and estimates of the components of population change as at 30 June for the years 2001-2020. The data is aggregated to the 2016 Australian Statistical Geography Standard (ASGS) Statistical Areas Level 2 (SA2). This data is sourced from the Australian Bureau of Statistics (Catalogue Number: 3218.0). For more information please visit the Regional population methodology. Notes: The population estimates in this issue are final for 2001 to 2016, revised for 2017 to 2019, and preliminary for 2020. Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimated resident population is based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. AURIN has ingested this dataset in its GeoPackage format.

This dataset presents data on the labour force categorised by age and sex, available from the Australian Bureau of Statistics (ABS) Labour Force statistics. This dataset is based on Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS), and covers data for individual months between October 1998 and June 2020. Labour Force statistics are compiled from the Labour Force Survey which is conducted each month throughout Australia as part of the ABS household survey program. The Labour Force Survey provides monthly information about the labour market activity of Australia's resident civilian population aged 15 years and over. The Labour Force Survey is designed to primarily provide estimates of employment and unemployment for the whole of Australia and, secondarily, for each state and territory. This data is ABS data (catalogue number: 6291.0.55.001) used with permission from the Australian Bureau of Statistics. For more information please visit the Australian Bureau of Statistics.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Monthly

Sampling procedure

Sample size:

The number of social media users in Australia was forecast to continuously increase between 2024 and 2029 by in total 2.1 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 26.68 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of social media users in countries like Fiji and New Zealand.

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

This dataset contains statistics about births and fertility rates for Australia, states and territories, and sub-state regions. It includes all births that occurred and were registered in Australia, including births to mothers whose place of usual residence was overseas. Estimated resident populations (ERPs) are used as denominators to calculate fertility rates and are based on the results of the 2016 Census. This dataset uses the ABS Statistical Area Level 3 (SA3) boundaries of the Australian Statistical Geography Standard (ASGS) 2016. For more information such as the scope, coverage and exclusions used in this dataset please visit the Australian Bureau of Statistics (ABS) methodology documentation. AURIN has spatially enabled the original data from the ABS with the 2016 SA3 boundaries.

This dataset presents information from Sugarcane, experimental regional estimates using new data sources and methods, 2020-21 by Statistical Areas Level 2 (SA2s), 2016, from the Australian Statistical Geography Standard (ASGS) Edition 2.

The Australian Bureau of Statistics (ABS) partnered with industry and other stakeholders to co-design methods to produce agriculture statistics using new data sources. Traditionally, agriculture area and production statistics are produced by the ABS through an annual agricultural survey or a five yearly agricultural census which collects information directly from farm businesses. These experimental sugarcane statistics demonstrate that these non-survey data sources can produce accurate and more timely statistics at a level of regional detail normally only available from an agricultural census.

These experimental sugarcane statistics for production, area of harvest and business counts were produced by combining Levy Payers Register administrative data with satellite derived crop mapping. More information on the method and approach can be found here and feedback to further refine it is welcomed via email: agriculture.statistics@abs.gov.au.

Data points of ‘less than 5’ in the original data have been converted to blank cells as part of the web service production process due to the need for web service data to be integers only, whilst continuing to suppress any values less than 5. Similarly, ‘-‘ entries in the original data which symbolised a null result (i.e. no production detected) have also been converted to a blank cell. The original data containing ‘less than 5’ and ‘-‘ annotations can be accessed here.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Sugarcane, experimental regional estimates using new data sources and methods Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 2 Further information: Sugarcane, experimental regional estimates using new data sources and methods methodology Source: Australian Bureau of Statistics (ABS)

Australia GDP: 2020-21p data was reported at 2,155,680.000 AUD mn in 2022. This records an increase from the previous number of 2,080,419.000 AUD mn for 2021. Australia GDP: 2020-21p data is updated yearly, averaging 860,935.000 AUD mn from Jun 1960 (Median) to 2022, with 63 observations. The data reached an all-time high of 2,155,680.000 AUD mn in 2022 and a record low of 279,781.000 AUD mn in 1960. Australia GDP: 2020-21p data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A167: SNA08: Gross Domestic Product and Gross Domestic Product per Capita: by State.

This dataset presents the percentage change in weekly payroll jobs between the week ending 04 January 2020 to the week ending 3 October 2020 relative to the week ending 14 March 2020 (the week Australia recorded its 100th confirmed COVID-19 case). The data is aggregated to Statistical Area Level 4 (SA4) from the 2016 Australian Statistical Geography Standard (ASGS).

These weekly estimates are derived from Single Touch Payroll (STP) data, which is provided to the Australian Taxation Office (ATO) by businesses with STP-enabled payroll or accounting software each time the business runs its payroll. STP data includes both business and job level tax information and superannuation information. The data are combined with other administrative data from the Australian taxation system to determine additional classification attributes, such as the age and sex of employees.

This data is sourced from the Australian Bureau of Statistics.

Note:

• The weekly change estimates for the most recent weeks of data contain a higher degree of reporting variability and imputation. The ABS recommends that users exercise caution when focusing on change in the most recent weeks, as these estimates are subject to a greater levels of revision in subsequent releases.

For more information please visit the Data Methodology.

This release presents experimental estimates of weekly payroll jobs and wages for the purpose of assessing the economic impact of COVID-19 on employees and the labour market.

AURIN has restructured and spatially enabled the original dataset.

Australia GDP per Capita: 2020-21p data was reported at 83,678.000 AUD in 2022. This records an increase from the previous number of 81,159.000 AUD for 2021. Australia GDP per Capita: 2020-21p data is updated yearly, averaging 49,950.000 AUD from Jun 1960 (Median) to 2022, with 63 observations. The data reached an all-time high of 83,678.000 AUD in 2022 and a record low of 27,290.000 AUD in 1962. Australia GDP per Capita: 2020-21p data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A167: SNA08: Gross Domestic Product and Gross Domestic Product per Capita: by State.

Australia GDP per Capita: Chain Volume: 2020-21p: Victoria data was reported at 76,357.000 AUD in 2022. This records an increase from the previous number of 72,265.000 AUD for 2021. Australia GDP per Capita: Chain Volume: 2020-21p: Victoria data is updated yearly, averaging 67,630.000 AUD from Jun 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 76,357.000 AUD in 2022 and a record low of 45,363.000 AUD in 1992. Australia GDP per Capita: Chain Volume: 2020-21p: Victoria data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A167: SNA08: Gross Domestic Product and Gross Domestic Product per Capita: by State.

This dataset, released February 2020, contains the total Population projections for years 2020, 2025 and 2030, by 5-year age groups: 0-14, 15-24, 25-44, 45-64, 65+, 70+, 75+, 85+ years. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: These data are based on customised projections prepared for the Australian Government Department of Health by the Australian Bureau of Statistics and originally published by the Australian Institute of Health and Welfare. PHA data were compiled by PHIDU based on these customised projections for 2020, 2025, and 2030.. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset presents information from National Land Cover Account, Time series of land cover stock positions, 1988 to 2020 by Statistical Areas Level 2 (SA2s), 2016, from the Australian Statistical Geography Standard (ASGS) Edition 2.

This is part of the National Land Cover Account that provides Experimental statistics on detailed land cover stock positions and changes in land cover from 1988 to 2020.

The National Land Cover Account was produced by the Australian Bureau of Statistics (ABS) and released in alignment with the Commonwealth government's Common national approach to environmental-economic accounting in Australia. Experimental estimates published in the Land Cover Account were developed in collaboration with the Department of Agriculture, Water and the Environment (now Department of Climate Change, Energy, the Environment and Water) and Geoscience Australia (GA), including GA's internal research area: Digital Earth Australia (DEA).

Further information on the National Land Cover Account methodology can be found here.

Data considerations The input data was not collected or validated at SA2 level. Classes with small estimates are less reliable and should be used with caution. Any discrepancies between totals and sums of components in this publication are due to rounding.

Zeros can be real zeros (or rounded to zero), or data that is not available.

SA2s that cover Christmas Island, Cocos (Keeling) Islands, Lord Howe Island and Norfolk Island are not in scope of the account and these SA2s have not been included in the data.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: National Land Cover Account Geographic boundary information: Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 Further information: National Land Cover Account methodology Source: Australian Bureau of Statistics (ABS)

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.