The statistic shows the total population of Australia from 1980 to 2023, with projections up until 2030. In 2023, Australia had a total population of about 26.95 million people. Population of Australia Australia is among the ten largest countries in the world, in terms of area size, although its total population is low in relation to this. Much of Australia’s interior remains uninhabited, as the majority of Australians live in coastal metropolises and cities. Most of the population is of European descent (predominantly British), although there is a growing share of the population with Asian heritage; only a small percentage belongs to the indigenous Aboriginal population. Australia's year-on-year population growth is fairly high compared to most other economically and demographically advanced nations, due to comparatively high rates of natural increase and immigration. Living standards Standard of living is fairly high in Australia, which can be seen when looking at the Human Development Index, which ranks countries by their level of human development and living standards, such as their unemployment rate, literacy rate, or life expectancy at birth. Life expectancy of Australia’s population is quite high in international comparison, for example, Australia is also among the leading countries when it comes to this key factor. Economically speaking, Australia is also among the leading nations, with a steadily rising employment rate, an increasing gross domestic product (GDP) with a steady growth rate, and a relatively stable share in the global GDP.

Humans have been living on the continent of Australia (name derived from "Terra Australis"; Latin for "the southern land") for approximately 65,000 years, however population growth was relatively slow until the nineteenth century. Europeans had made some contact with Australia as early as 1606, however there was no significant attempt at settlement until the late eighteenth century. By 1800, the population of Australia was approximately 350,000 people, and the majority of these were Indigenous Australians. As colonization progressed the number of ethnic Europeans increased while the Australian Aboriginal population was decimated through conflict, smallpox and other diseases, with some communities being exterminated completely, such as Aboriginal Tasmanians. Mass migration from Britain and China After the loss of its American colonies in the 1780s, the British Empire looked to other parts of the globe to expand its sphere of influence. In Australia, the first colonies were established in Sydney, Tasmania and Western Australia. Many of these were penal colonies which became home to approximately 164,000 British and Irish convicts who were transported to Australia between 1788 and 1868. As the decades progressed, expansion into the interior intensified, and the entire country was claimed by Britain in 1826. Inland colonization led to further conflict between European settlers and indigenous Australians, which cost the lives of thousands of natives. Inward expansion also saw the discovery of many natural resources, and most notably led to the gold rushes of the 1850s, which attracted substantial numbers of Chinese migrants to Australia. This mass migration from non-European countries eventually led to some restrictive policies being introduced, culminating with the White Australia Policy of 1901, which cemented ethnic-European dominance in Australian politics and society. These policies were not retracted until the second half of the 1900s. Independent Australia Australia changed its status to a British dominion in 1901, and eventually became independent in 1931. Despite this, Australia has remained a part of the British Commonwealth, and Australian forces (ANZAC) fought with the British and their Allies in both World Wars, and were instrumental in campaigns such as Gallipoli in WWI, and the South West Pacific Theater in WWII. The aftermath of both wars had a significant impact on the Australian population, with approximately 90 thousand deaths in both world wars combined, as well as 15 thousand deaths as a result of the Spanish flu pandemic following WWI, although Australia experienced a significant baby boom following the Second World War. In the past fifty years, Australia has promoted immigration from all over the world, and now has one of the strongest economies and highest living standards in the world, with a population that has grown to over 25 million people in 2020.

This dataset presents information from G07 – Indigenous status by age by sex in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Local Government Areas (LGA), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

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: G07 – Indigenous status by age by sex Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

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SA4 based data for Age By Indigenous Status By Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in single year age brackets up to 24 years of age and 5 year age brackets up to 65 years and over. I03 is broken up into three sections (I03a to I03c), this section covers ‘ Age years 0 Aboriginal and or Torres Strait Islander Males’ - ‘ Age years 14 Indigenous status not stated Females’. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

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LGA based data for Age By Indigenous Status By Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in single year age brackets up to 24 years of age …Show full descriptionLGA based data for Age By Indigenous Status By Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in single year age brackets up to 24 years of age and 5 year age brackets up to 65 years and over. I03 is broken up into three sections (I03a and I03c), this section covers ‘Age years 40-44 years Non-Indigenous Females’ - ‘Total Total Persons’. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA2 based data for Age By Indigenous Status By Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in single year age brackets up to 24 years of age …Show full descriptionSA2 based data for Age By Indigenous Status By Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in single year age brackets up to 24 years of age and 5 year age brackets up to 65 years and over. I03 is broken up into three sections (I03a and I03c), this section covers ‘Age years 14 Indigenous status not stated Persons’ - ‘Age years 40-44 years Non-Indigenous Males’. The data is by SA2 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Wildfires in forests globally have become more frequent and intense due to changes in climate and human management. Shrub layer fuels allow fire to spread vertically to forest canopy, creating high-intensity fires. Our research provides a deep-time perspective on shrub fuel loads in fire-prone southeastern Australia. Comparing 2,833 records for vegetation cover, past climate, biomass burning, and human population size across different phases of human occupation, we demonstrate that Indigenous population expansion and cultural fire use resulted in a 50% reduction in shrub cover, from approximately 30% from the early-mid Holocene (12-6 ka) to 15% during the late-mid Holocene (6-1 ka). Following British colonization, shrub cover has increased to the highest ever recorded (mean of 35% land cover), increasing the risk of high-intensity fires. Methods Vegetation reconstruction We applied REVEALS (32) to quantify past vegetation using 31 pollen records in southeast Australia (Table S1), covering the Holocene (n=29) and Last Interglacial periods (n=2). The REVEALS model was run to convert raw pollen data (counts) into estimates of land cover (%) by correcting for biases in pollen production (i.e. different plant species produce different amounts of pollen) and pollen dispersal (i.e. dispersal patterns differ in response to pollen grain properties) (32). Pollen productivity estimates (PPEs) required for REVEALS for the 19 most abundant pollen taxa were derived from Mariani et al. These 19 taxa cover a large proportion of the vegetation and pollen counts, for example, across >275 vegetation quadrats for the state of Victoria (5), a median of 81% were target taxa (± 24%). Based on the modern (moss) pollen counts, a median of 96% of moss samples were made up by target taxa (±22%). For fossil samples across the region, these 19 target taxa constitute approx. 75% of pollen counts (median value, ±14%). The missing % are usually within the Proteaceae and Fabaceae families (shrub layer), which means our reconstruction of shrub cover is on the conservative side and reconstructed values might be slightly higher. REVEALS was executed using the R package disqover version 0.9.09 accessible through (https://github.com/MartinTheuerkauf/disqover.), using the Lagrangian stochastic model (LSM) for pollen dispersal parameterized as previous studies in Europe and Australia (5, 58–61). The median across all REVEALS results combining each site per 200 year time bins were compiled using MS Excel and the R code provided. The results of the Holocene estimates were compared with post-colonial vegetation estimates previously published (n= 51; Mariani et al.). Statistical analyses were undertaken to assess significance of shrub cover % change amongst the throughout the reconstruction period. A pairwise t-test was conducted on square-root transformed percentage data for shrub cover (Table S1). Square-root transformation was required prior to the t-test, as the dataset did not have a normal distribution and parametric tests assume normality. The square-root transformation provided a normal distribution (Figure S11a,b) and the autocorrelation of the shrubs time series is presented in Figure S11c. To further support the results from the t-test, we undertook a Kruskal-Wallis test (62) with Wilcoxon pairwise comparisons (63). This test is non-parametric, hence the raw data (non-normal) were supplied and results are presented in Figure S11d. Human population A total of 2,358 radiocarbon ages of archaeological evidence of past human occupation across southeast Australia were used in this study. Initially 6,522 radiocarbon ages were extracted from the SahulArch database, accessible through OCTOPUS v.2 (https://octopusdata.org/); Codilean et al. (936 and subject to screening for region of interest, Holocene age range, and appropriate associations with archaeological deposit (if context was indicated as ‘sterile’ or ‘non-occupation’ by original study, these were excluded). The resulting 2,368 radiocarbon ages were then calibrated using SHCal20, and the summed probability density (SPD) of calibrated ages was calculated using the thinning approach in the rcarbon package using version 1.5.1 to infer past human population changes. We acknowledge that the number of radiocarbon dates and associated uncertainties can influence summed probability estimates, especially for estimates aimed at detecting short-term variations and rates of change. Our study is focused on long-term multi-millennia-scale changes and does not consider rates of change. Bayesian bounded population growth models were further used to assess the fit of the SPD of radiocarbon ages using the nimbleCarbon package version 0.2.5 in R. The models were fitted through Markov Chain Monte-Carlo and ranked using the Watanabe–Akaike information criterion (WAIC). The fitted population growth models include exponential, double exponential, and exponential logistic models (Fig. S4; see Crema and Shoda, for method details). Among the fitted models for the SPD, the exponential logistic model ranked as the top model with the lowest WAIC, while the double exponential model ranked the lowest with the highest WAIC (Fig. S4). Biomass burning Sedimentary charcoal records from 108 lakes and wetlands across southeastern Australia were collated from the Global Charcoal Database and Neotoma (Table S2). Charcoal concentrations were converted to charcoal accumulation rates using the available chronological information. As elsewhere in the world, researchers in Australia have used various methods to quantify charcoal, necessitating data transformations to extract regional-scale palaeofire trends. Because these transformations tend to mask inter-site variability, we grouped records by analysis method prior to min-max rescaling of all records within each group and square-root transformation. As woody charcoal is preferentially preserved and accumulated in the sedimentary records, as opposed to grass charcoal (38), we can interpret our charcoal influx trends as woody biomass burned. The method is fully described in Mariani et al. and Rowe et al. Palaeoclimate Five Holocene terrestrial palaeomoisture records (see Table S3) were compiled in this investigation comprising three precipitation/evaporation (P/E) proxy reconstruction types. Lake level reconstructions where lower lake levels represent diminished lake recharge into closed lakes through precipitation. Palaeosalinity records where higher salinities indicate periods of increasing lake desiccation and reduced regional recharge (drier conditions). A mean annual rainfall reconstruction for Swallow Lagoon, Stradbroke Island, directly reconstructing precipitation. Whilst the palaeoclimate records span a large climatic gradient (Fig. S2), the individual trends were found to be coherent, justifying their compositing (Fig. S6). All data were individually z-scored and then merged using MS Excel, before GAM smoothing (k=100), to create a composite record for the southeastern Australian region. Lake level reconstructions from sediment grain size analysis at the maar crater Lakes Keilambete and Gnotuk were used as indicators of regional precipitation patterns from westerly wind circulation. Both lakes have relatively simple hydrological inputs and are considered good indicators of evaporation-precipitation oscillations. Palaeosalinity reconstructions as Total Dissolved Solids (TDS g/L) for Lake Keilambete were originally procured through ostracod Modern Analogue Technique (MAT) reconstructions using an analogue database of 491 samples. A depauperate fossil record (n=3 species) of ostracods at Lake Gnotuk meant only grain size lake levels were included in the index. Palaeosalinity reconstructions (Log g/L-1) for Lake Jacka and NW Jacka (72) were calculated from ostracod assemblages using a weighted-averaging transfer function with 119 modern analogue samples. The main control on modern assemblage composition was the total salinity of the lakes. At Blue Lake, palaeosalinity (Log10TDS g/L) were originally derived from the Weighted Averaging of Modern Analogues Technique (WMAT) utilizing 534 analogue samples. The rainfall reconstruction at Swallow Lagoon (56) was included to consider ENSO-derived palaeomoisture signals. The rainfall reconstruction of Swallow Lagoon used δ13C ratios from ancient Melaleuca quinquenervia leaf fragments as a proxy of historical rainfall (mm), calibrated against a 12-year monthly record of Melaleuca quinquenervia litter δ13C and recorded rainfall. The record reflects mean annual rainfall for the total ENSO system rather than El Niño/La Niña events, where the 1cm samples represent an average of 24.4 years of data. Generalized linear modelling Generalized linear model (GLM) was used to identify the main driver(s) of ladder fuels (shrub) cover with palaeoclimate synthesis, biomass burned, and SPD of archaeological ages set as predictors. Variables were randomly sampled 100 times without replacement at a lower resolution to remove the effect of autocorrelation. The lower resolution includes 30, 60 and 90% of datasets to check the consistency of the results (Table S3). A separate GLM was also fitted to predict mid-late Holocene shrub cover changes using early-mid Holocene GLM (predictors: human population –SPD, palaeoclimate index and charcoal influx) as a training set. The mid-late Holocene model was intended to reflect shrub cover changes under the scenario of no human influence and so, only palaeoclimate index and charcoal influx were included as predictors. GLMs were fitted using the MASS package version 7.3 in R (74).

SA3 based data for Highest Year of School Completed by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons aged 15 years and over who are no longer attending primary or secondary school. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small …Show full descriptionSA3 based data for Highest Year of School Completed by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons aged 15 years and over who are no longer attending primary or secondary school. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Data analyzed in GenABEL using genotyped or 1000G imputed data for all individuals with doctor-diagnosed T2D. Results are for allele-wise tests under an additive model of inheritance. Bold indicates top hit for T2D based on both genotyped and imputed data. Full lists of the top 50 hits for genotyped SNPs, and the top 100 SNPs for imputed data, appear in S5 Table and S7 Table, respectively.* Genes separated by forward slash indicate nearest protein coding genes upstream/downstream of the SNP. NCBI37 = bp location on chromosome for NCBI Build 37. A1 = major allele; A2 = minor allele.Top GWAS SNP hits in genes of functional interest for T2D, organized by chromosome.

GCCSA based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes …Show full descriptionGCCSA based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes the following characteristics: age bracket, location on census night, language spoken at home, English proficiency, Religion, age bracket of those attending an educational institution, highest year level of schooling complete and type of usual residence. I01 is also broken up into 3 sections (I01a – I01c), this section contains ' Visitor from Different SA2 in Northern Territory Indigenous status not stated Persons’ - ' Highest year of school completed Year 10 or equivalent Non-Indigenous Males’. The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Results are for GWAS analysis in GenABEL using each individual BMI reading as a separate observation, modelling the correlation between readings via the estimated kinship and using the Genomic Control deflation factor to avoid inflation of the overall distribution of test statistics. Results are for allele-wise tests under an additive model of inheritance for genotyped SNPs and for imputed data. Bold indicates the top hit for BMI based on both genotyped and 1000G imputed data. Full lists of the top 50 hits for genotyped SNPs, and the top 100 SNPs for imputed data, appear in S3 Table and S6 Table, respectively.* Genes separated by forward slash indicate nearest protein coding genes upstream/downstream of the SNP. NCBI37 = bp location on chromosome for NCBI Build 37. A1 = major allele; A2 = minor allele.Top GWAS SNP hits in genes of functional relevance for BMI, organized by chromosome.

SA4 based data for Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in the following categories: identified themselves as …Show full descriptionSA4 based data for Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in the following categories: identified themselves as Aboriginal and/or Torres Strait Islanders, identified themselves as non-indigenous, did not state their indigenous status. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA2 based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes …Show full descriptionSA2 based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes the following characteristics: age bracket, location on census night, language spoken at home, English proficiency, Religion, age bracket of those attending an educational institution, highest year level of schooling complete and type of usual residence. I01 is also broken up into 3 sections (I01a – I01c), this section contains 'Total persons Aboriginal and or Torres Strait Islander Males’ - 'Visitor from Different SA2 in Northern Territory Indigenous status not stated Females’. The data is by SA2 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

LGA based data for Non-School Qualification: Level of Education by Indigenous Status by Age by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons …Show full descriptionLGA based data for Non-School Qualification: Level of Education by Indigenous Status by Age by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons aged 15 years and over with a qualification. Excludes schooling up to Year 12. Excludes persons with a qualification out of the scope of the Australian Standard Classification of Education (ASCED). I15 is broken up into 4 sections (I15a – I15d), this section contains 'Males Aboriginal and or Torres Strait Islander 15-19 years Postgraduate Degree Graduate Diploma and Graduate Certificate Level’ - 'Males Total 15-19 years Total’. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA3 based data for Total Personal Income (Weekly) by Sex for Aboriginal and/or Torres Strait Islander Persons, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of Aboriginal and/or Torres Strait Islander persons aged 15 years and over. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to …Show full descriptionSA3 based data for Total Personal Income (Weekly) by Sex for Aboriginal and/or Torres Strait Islander Persons, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of Aboriginal and/or Torres Strait Islander persons aged 15 years and over. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA4 based data for Non-School Qualification: Level of Education by Indigenous Status by Age by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons …Show full descriptionSA4 based data for Non-School Qualification: Level of Education by Indigenous Status by Age by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons aged 15 years and over with a qualification. Excludes schooling up to Year 12. Excludes persons with a qualification out of the scope of the Australian Standard Classification of Education (ASCED). I15 is broken up into 4 sections (I15a – I15d), this section contains 'Males Total 20-24 years Postgraduate Degree Graduate Diploma and Graduate Certificate Level' - 'Females Indigenous status not stated 20-24 years Total'. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA3 based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes …Show full descriptionSA3 based data for Selected Person Characteristics by Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons. The dataset includes the following characteristics: age bracket, location on census night, language spoken at home, English proficiency, Religion, age bracket of those attending an educational institution, highest year level of schooling complete and type of usual residence. I01 is also broken up into 3 sections (I01a – I01c), this section contains 'Highest year of school completed Year 10 or equivalent Non-Indigenous Females’ - ‘Type of usual residence Non private dwelling Total Persons’. The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

LGA based data for Indigenous Status by Sex, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of persons in the following categories: identified themselves as Aboriginal and/or Torres Strait Islanders, identified themselves as non-indigenous, did not state their indigenous status. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.