Datasets present number of households residing in Public Housing (PH) and State Owned and Managed Indigenous Housing (SOMIH) across Local Government Areas (LGAs) in South Australia as at 30 June. PH and SOMIH refers to dwellings owned and managed by the SA Housing Authority. These rentals are accessed by those on low income and/or with special needs. Strategies have been employed to mitigate the risk of releasing any identifying data, which may occur in smaller areas. Data specifications of measures and data quality statements for these files are maintained by the Australian Institute of Health and Welfare (AIHW) and available in their metadata online registry (METEOR), see https://meteor.aihw.gov.au/content/711016 and https://meteor.aihw.gov.au/content/749351 .

2016 Digital Landscape Report The 2016 South Australian Government Digital Landscape Report examines the provision of digital government services from the perspectives of the public and the SA Government. Its findings are based on surveys undertaken by 1100 SA citizens and 560 SA public servants, as well as interviews with 17 Executives from SA Government Agencies. Developed by Ernst and Young, with assistance from the Department of the Premier and Cabinet, the report contains a frank account of progress to date as well as key insights and recommended priorities for the road ahead. 2015 Digital Landscape Report Survey results collected by Deloitte and Square Holes on behalf of the South Australian Government to inform the SA Digital Landscape Report. Online surveys of government staff and members of ICT industry groups to assess their organisations' capacity and capability to deliver digital government services. Focus group and interview responses from the public to understand their willingness and ability to use government digital services. Four citizen groups examined include 18-40 y/o, 41-60 y/o, regional participants, and vision-impaired participants.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

SA Country Fire Service (CFS) responds to a range of incidents across South Australia.
A Bushfire Safer Settlement is a place of relative safety and may be used as a place for people to stay in or as a place of first resort for those people who have decided that they will leave high risk locations early on a bad fire weather day. This precinct is considered to be relatively safe from fire due to its distance from areas with a high fuel level. Although the CFS has taken every care and precaution in identifying this area it may be subjected to spark and ember attack in the event of a fire. This KML file describes the Safer Settlement area.

SA Country Fire Service (CFS) responds to a range of incidents across South Australia.
A Bushfire Safer Precinct is a place of relative safety and may be used as a place for people to stay in or as a place of first resort for those people who have decided that they will leave high risk locations early on a bad fire weather day.

Larger rural townships, provided they meet established criteria, will be classified by CFS as having a Bushfire Safer Precinct. Any Bushfire Safer Precinct within those townships will be clearly defined on a map by CFS that is available from the CFS website.

Properties on the outskirts of such townships generally face a higher level of risk when compared with those nearer the centre of town. The relative safety of these properties can be improved by property owners undertaking appropriate bushfire safety works to ensure they don't place themselves or the greater community at risk. This KML file describes the Safer Precincts areas in South Australia.

As of June 2023, there were approximately 8.33 million residents in the New South Wales region in Australia. In comparison, there were around 252 thousand residents in the Northern Territory region.

The projected population for 2025 by 5 year groups: 0-4 years to 85+ years and their proportion of the projected total population (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: These are customised projections prepared for the Australian Government Department of Social Services by the Australian Bureau of Statistics and published by the Australian Institute of Health and Welfare. PHA data were compiled by PHIDU based on these customised projections 2015, 2020 and 2025 (2012 base).

*** The beta release of this web service provides a preview of the capability that will be delivered through the Digital Atlas of Australia. Availability of this dataset through this web service is not guaranteed and the data may be subject to change. ***

2021 Statistical Areas Level 2 (SA2) are part of the Main Structure of the Australian Statistical Geography Standard (ASGS) Edition 3.

SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. Most SA2s have a population range of 3,000 to 25,000 people. SA2s are built from whole Statistical Areas Level 1 (SA1), while whole SA2s aggregate to form Statistical Areas Level 3 (SA3) in the ASGS Main Structure.

The ASGS is a classification of Australia into a hierarchy of statistical areas. It is a social geography, developed to reflect the location of people and communities. It is used for the publication and analysis of official statistics and other data. The ASGS is updated every 5 years to account for growth and change in Australia’s population, economy and infrastructure.

Currency: Date modified: 20 July 2021 Update frequency: Not planned. Data Extent: Spatial Extent: West longitude: 96.816941 South latitude: -43.740510 East longitude: 167.998035 North latitude: -9.142176

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 is working in partnership with Geoscience Australia to establish a set of web services to make 2021 Census data available in the Digital Atlas.

The Digital Atlas of Australia beta will be available by mid-2023.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web services, 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: Australian Statistical Geography Standard (ASGS) Edition 3 Source web service: ASGS2021/SA2 (MapServer) Data services and APIs source: ASGS geospatial web service links Source: Australian Bureau of Statistics (ABS)

The projected population for 2020 by 5 year groups: 0-4 years to 85+ years and their proportion of the projected total population (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia).The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: These are customised projections prepared for the Australian Government Department of Social Services by the Australian Bureau of Statistics and published by the Australian Institute of Health and Welfare. PHA data were compiled by PHIDU based on these customised projections 2015, 2020 and 2025 (2012 base).

This dataset includes 952 selected oral history transcripts from the J.D. Somerville Oral History Collection, the central repository for unpublished oral history tapes and transcripts in South Australia. The collection is intended to provide an oral record of all aspects of the South Australian experience and particularly of those who are poorly represented in documentary records, such as low income earners, people of non-English speaking background, women, and country people. The collection also provides a representative sample of the various uses of oral history, such as academic, commissioned, local history, community arts, school and family history. The transcripts are in PDF format.

Housing Affordability Supply and Demand Data. \r \r Number of South Australian households paying more than 30% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets.\r \r This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. \r \r The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area\r \r Explanatory Notes:\r \r Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. \r \r The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress.\r \r Income bands are based on household income.\r \r High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets.\r \r Please note that 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.

Abstract

This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The Cooper subregion is within the Lake Eyre Basin bioregion. It covers an area of 130,000 km2 of which two-thirds is in south-west Queensland with the remainder in north-east SA. The majority of the subregion is within the Cooper Creek - Bulloo river basin with a small part in the north-west within the Diamantina-Georgina river basin. Key features of the Cooper subregion are its large area, sparse human population density (only 1032 residents in the 2011 census) and unpredictable rainfall resulting in natural and human systems driven by resource pulses and boom-bust dynamics. The low human population results in the natural vegetation being relatively intact. The dominant land use in the subregion is grazing of sheep and cattle on natural pastures (grazing native vegetation). Other major land uses are nature conservation and mining and waste (represented by an area of mining and intensive gas treatment, storage and distribution at Moomba). There is no pasture modification or intensive production within the Cooper PAE.

A landscape classification was developed to characterise the nature of water dependency among the assets of the Cooper PAE. Unlike other subregions where the PAE extends beyond the subregion boundaries (e.g. Galilee), the Cooper PAE is smaller in area than the subregion and mostly contained within its boundaries (Figure 17). The aim of the landscape classification is to systematically define geographical areas into classes based on similarity in physical and/or biological and hydrological character. The landscape classification includes natural and human ecosystems. The objective of the landscape classification is to present a conceptualisation of the main biophysical and human systems at the surface and describe their hydrological connectivity. This section describes the methodology and datasets used to arrive at the landscape classification for ecosystems within the Cooper PAE.

Multiple classification methodologies have been developed to provide consistent and functionally relevant representations of water-dependent ecosystems in Australia and globally (e.g. ANAE). The approach outlined in this product has built on and integrated these existing classification systems.

Dataset History

Preparation of wetlands

1. Merged "QLD_WETLAND_SYSTEM_100K_A" (GUID: 2a187a00-b01e-4097-9ca4-c9683e7f4786) and "Wetlands_GDE_Classification" (GUID: fc35d75a-f12e-494b-a7d3-0f27e7159b05) - output [a]

2. Extract polygons from [a] which intersect with "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607) - output [b]

3. a) Add field "Landform_LC" to [b]

b) Select where "WETLANDSYS" field is not blank

c) Update "Landform_LC" field to first letter from "WETLANDSYS" field

d) Selet where "WETCLASS field is not blank

e) Update "Landform_LC" with "WETCLASS value

f) export - output [c]

1. Eliminate spurious polygons from [c], merge to polygons with longest border - output [d]

2. Intersect [d] with itself to find overlaps, cut out overlapping section, use autocomplete polygon to fill gap with no overlap. Merge to longest border - iterate til no overlaps exist - output [e]

3. export [e] as 'COO_Wetlands' -output [f]

Preparation of LC_Landform

1. Create a 2 degree by 2 degree fishnet polygon feature class over the extent of the "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607) - output [g]

2. Clip "AHGFMappedStream" (GUID: 5342c4ba-f094-4ac5-a65d-071ff5c642bc) to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607) - output [h]

3. Split wetlands [f] into 2 degree tiles (from [g]) - output [i]

10 Split streams [h] into 2 degree tiles (from [g]) - output [j]

1. Buffer streams in [j] by 1m to create polygons - output [k]

2. Erase wetlands [i] from buffered streams [k] - output [l]

3. explode multi-part wetlands [i] into single part polygons - output [m]

4. explode multi-part buffered streams [l] into single part polygons - output [n]

5. Merge [m] and [n] into single feature class; Landform_LC - output [o]

Preparation of LC_GW_SRCE

1. a) Clip "GDE_Terrestrial_Areas_v01_1" (GUID: 10940dfa-d7ef-44fb-8ac2-15d75068fff8) to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607)

b) Add field "GW_SRCE_LC"

c) where Attribute field [Rule_Name] is "Aquifers associated with springs that form saline scolds" OR "Sandstone aquifers with fresh permanent groundwater connectivity regime associated with discharge springs, populate "GW_SRCE_LC" to "Artesian"

d) Update the "GW_SRCE_LC" attribute for all other features to; "Non-Artesian"

e) output [p]

1. a) Clip "GDE_Sub" and GDE_Sur" features from "COO_asset_database_20150827" (GUID: 0b122b2b-e5fe-4166-93d1-3b94fc440c82) to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607)

b) Add field "GW_SRCE_LC"

c) update "GW_SRCE_LC" field to "Artesian"

d) output [q]

1. a) Clip spring features from "GEODATA TOPO 250K Series 3" (GUID: a0650f18-518a-4b99-a553-44f82f28bb5f) to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607)

b) Buffer to 20m

c) Add field "GW_SRCE_LC"

d) update "GW_SRCE_LC" field to "Artesian"

e) output [r]

1. Erase [r] from [q] - output [s]

2. Merge [p], [r] and [s] - output [t]

Create topography landclass

1. Select Floodplain - "F" features from "QLD_Wetland_System" (GUID: 2a187a00-b01e-4097-9ca4-c9683e7f4786) and clip to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607) - output [u]

2. Select "LandSubjectToInundation", "MarineSwamp", "SalineCoastalFlat", "Swamp" features from "GEODATA TOPO 250K Series 3" (GUID: a0650f18-518a-4b99-a553-44f82f28bb5f) and clip to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607) - output [v]

3. Merge [u] and [v], add field "LC_Code" and assign value '10,000' - output [w]

4. convert [w] to 25m gridded raster, gridvalue: "LC_Code" - output [x]

Stream Classification

1. a)Clip "AHGFMappedStream" (GUID: 5342c4ba-f094-4ac5-a65d-071ff5c642bc) to "COO_PAE_v03.shp" (GUID: 872353f8-9795-42c6-8819-047e1fb05607)

b) buffer to 1m - Dissolve all

c) Add Attribute field "LC_Class"

d) Split features based on cell value of "landclass_wgs" (GUID: 8915d14d-7c22-404a-ba11-07f0c25fd177)

e) split features again based on 'Perenniality' attribute from original "AHGFMappedStream" (GUID: 5342c4ba-f094-4ac5-a65d-071ff5c642bc)

f) for features where "Perenniality" = Perennial, update "LC_Class" to "Near permanent", for all other features, update "LC_Class" to "Temporary"

g) for features where "landclass_wgs" = 1, append ", lowland stream" to "LC_Class", for all other features, append ", upland stream" to "LC_Class"

h) output [y]

Preparation of LC_WaterType

1. From "QLD_GDETerr" (GUID: 10940dfa-d7ef-44fb-8ac2-15d75068fff8), select where 'Salinity of groundwater source' >= 3000mg/L TDS and clip to "COO_SW_PAE_v02" (GUID: 7069521d-51cf-41c5-b9ef-ebb424af5361) - output [z]

2. From "QLD_GDESur" (GUID: 10940dfa-d7ef-44fb-8ac2-15d75068fff8), select where 'Salinity of groundwater source' >= 3000mg/L TDS and clip to "COO_SW_PAE_v02" (GUID: 7069521d-51cf-41c5-b9ef-ebb424af5361) - output [aa]

3. From "QLD_Wetlands" (GUID: 2a187a00-b01e-4097-9ca4-c9683e7f4786), select where 'SALIMOD' in (S2, S3, T1) and clip to "COO_SW_PAE_v02" (GUID: 7069521d-51cf-41c5-b9ef-ebb424af5361) - output [ab]

4. a) Merge [z], [aa] and [ab]

b) add field "lc_code"

c) where: 'WTRRegime' in (WR0, T1,WT1, WR2), update "lc_code" to 30 (intermittent)

d) where: 'WTRRegime' in (WR3 (orWT3)), update "lc_code" to 20 (near permanent)

e) output - [ac]

Dataset Citation

Bioregional Assessment Programme (2015) Landscape classification of the Cooper preliminary assessment extent. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/883926c0-82a3-4e9e-89fc-99557fd0a371.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From SA Petroleum Exploration Licence/Permit Applications

• Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases

• Derived From QLD Current Exploration Permits for Coal 6/3/2013

• Derived From QLD Mining Claim 6/3/2013

• Derived From South Australia Mineral Leases Production, 6 March 2013

• Derived From SA Petroleum Production License Applications

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Geofabric Surface Network - V2.1

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Asset database for the Cooper subregion on 27 August 2015

• Derived From [South Australia SA - Regional - NRM Board

The number of Aboriginal people and their proportion of the total population. The data is estimated resident populations (ERP non-ABS) developed by Prometheus Information, 2015 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on Indigenous population estimates refer to: http://phidu.torrens.edu.au/. Source: Compiled by PHIDU based on data developed by Prometheus Information Pty Ltd, under a contract with the Australian Government Department of Health.

The number of people with indigenous status in 5 year groups: 0-4 years to 65+ years and their proportion of the total population within that age group. The estimated resident population values (ERP non-ABS) were developed by Prometheus Information, 2015 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on Indigenous population estimates refer to: http://phidu.torrens.edu.au/. Source: Compiled by PHIDU based on data developed by Prometheus Information Pty Ltd, under a contract with the Australian Government Department of Health.

Updated data for the Australian Government’s Income Management (IM) program will be available on the third Thursday of every month. The data summary will include: Table 1. Number of IM participants …Show full descriptionUpdated data for the Australian Government’s Income Management (IM) program will be available on the third Thursday of every month. The data summary will include: Table 1. Number of IM participants by location and measure 1.1 Northern Territory 1.2 Western Australia 1.3 Queensland 1.4 South Australia 1.5 Victoria 1.6 New South Wales Table 2. Number of IM participants with an active BasicsCard by State/Territory Table 3. Number of IM exemptions by Indigenous Indicator The data provided will be the last weekly data update for the previous month. Legislation Legislation for IM is located here in the Social Security (Administration) Act 1999. The Guide to Social Policy Law for IM is located here. Information about IM More information about Income Management is located here on the Department of Human Services web site. Locations The places that have IM, by State and Territory, is located here on the Department of Human Services web site. Data Confidentialisation Policy Table cells are suppressed where the count refers to less than five, but more than zero, people. The method is: Cells with counts between one (1) and four (4) are presented as <5. Cells with counts between one (1) and four (4) are presented as <5. Cells with a count of zero (0) are not suppressed. Where suppression has been applied and it is still possible to derive the cell value from other information in the table, the total/s or the next lowest aggregate cells are suppressed and presented with ‘n.p.’ (not provided). Data Caveats Any variance from data reported prior to 28 August 2015 is due to a change to conform to the Australian Statistical Geography Standard (ASGS). Inconsistencies may be attributed to participants moving from the location where they were originally triggered onto the program. Participants with ‘Unknown’ locations did not have a recorded address at the time of data extraction. This often occurs because a participant is in the process of moving address. ‘Uncategorised CIM’ customers are instances where a customer was assessed for Income Management but was never switched ON and assigned a Category Code. ‘Greater Brisbane’ includes ‘Logan’. ‘Far North’ includes ‘Cape York’. For ‘Current Income Management Exemptions by Indigenous Indicator’, automatic exemptions for <25% of Max Payment is not included.

The number of People in 5 year groups: 0-4 years to 85+ years and their proportion of the total population, 2015 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). Source: Compiled by PHIDU based on the (Australian Bureau of Statistics (ABS) Estimated Resident Population, 30 June 2015.

There are over 24,000 specimens in this collection, including skulls, skins, skeletons, spirit specimens, photographs and frozen tissue. It includes over 1600 marine mammals and its comprehensiveness makes this collection the best of its kind in Australia. Other strengths of the collection include South Australian arid zone native mammals, many specimens of extinct species such as the thylacine and large numbers of bat species.

The collection has also historical importance as it includes many specimens from early expeditions in Australia and to the subantarctic Islands and Antarctica. Well-known people such as Sir Douglas Mawson, Edgar R. Waite and Hedley Finlayson have contributed to the collection.

The mammal collection includes sub-fossils and remains from owl pellets. This collection consists entirely of Australian material with 20000 - 25000 specimens covering 76 mammal species (including introduced species). The collection is made up of bulk bone deposits from the floor of caves, bones excavated from sinkholes, bones extracted from predator scats (eg. dingoes, foxes and Ghost Bats), pellets from birds of prey, particularly barn owls (both recent and pre-settlement material), and stick nest rat nests and middens. The sub-fossil collection is the second best of its kind in Australia.

The SA Museum manages this dataset using the KE EMu collection management system. It is interpreted into the Darwin Core metadata schema (DwC) and semi-regularly exported to the Atlas of Living Australia (ALA: http://www.ala.org.au/) and the Online Zoological Collections of Australian Museums (OZCAM: http://www.ozcam.org.au/). Information about Darwin Core can be found here: http://rs.tdwg.org/dwc/index.htm. Data sourced from Australian museums on both the ALA and OZCAM should be identical, but on ALA they are combined with observational data from citizen science initiatives and other sources. Both of those sites make it possible to combine, interrogate and analyse data through web services such as the Spatial Analysis Portal (http://spatial.ala.org.au/). In the Spatial Portal ALA data can be combined with meteorological and other environmental data sourced from and made accessible by relevant government agencies.

Data about endangered species are either withheld from online publication, or coordinates or other data are obscured on the ALA and OZCAM. In those circumstances more specific information is available directly from SA Museum collection managers if it is genuinely required for research purposes.

SA Museum data can be downloaded in full from the Atlas of Living Australia, or broken down into discipline specific parts (e.g. Herpetology, Mammalogy etc). On download the ALA will request an email address (not mandatory) and a reason for download (mandatory) – this is required to track usage of the ALA data to help data providers determine priorities for upload and improvement.

The Australian Longitudinal Study of Ageing, which ran from 1992 to 2014, was devised to generate longitudinal data over multiple time points. Thirteen waves were carried out. Waves 1, 3, 6, 7, 9, 11 and 12 comprised of a full face-to-face ‘household’ interview and a clinical assessment. Waves 2, 4, 5, 8, 10, 13 consisted of shorter telephone household interviews.The initial sample of the older old (70 and older) was randomly drawn from the database of the South Australian Electoral Roll. Persons in the older age groups as well as males were deliberately oversampled to compensate for the higher mortality that could be expected over the study period. In addition, spouses of primary respondents (aged 65 and over) and other household members aged 70 and over were asked to participate. 2087 participants were initially interviewed at Wave 1 in 1992. Over the years, attrition due to either death, ill health, moving out of scope, being uncontactable, or refusal has reduced the number of participants to 94 in 2014. Information covering the data, questionnaires and relevant details are openly available.Items in the household interview schedule represent a comprehensive set of measures chosen for their reliability and validity in previous studies, sensitivity to change over time, and suitability for use in a study of elderly persons. The domains assessed included demography, health, depression, morbid conditions, hospitalisation, hearing and vision difficulties, cognition, gross mobility and physical performance, activities of daily living and instrumental activities of daily living, lifestyle activities, exercise education and income.At the completion of the household interview, participants were left with self-administered questionnaires, which were mailed back in pre- paid envelopes or collected at the time of the clinical assessment. The domains covered by the questionnaires were dental health, sexual activity and psychological measures of self-esteem, morale and perceived control.The individual clinical assessment objectively measured both physical and cognitive functioning. The physical examination included measures of blood pressure, anthropometry, visual acuity, audiometry and physical performance. The cognitive assessment included measures of memory, information processing efficiency, verbal ability and executive function. The clinical assessments were conducted by nurses who received special training in the standard administration of all psychological instruments and the anthropometric measures. In addition, fasting blood samples and urine specimens were collected on the morning following the clinical assessment at Wave 1, and blood samples were again taken at Wave 3.Some data have been provided by secondary sources. Participant deaths have been systematically monitored through the government Registry of Births, Deaths and Marriages.From Wave 7 onward, collateral data were gathered from the files of the Health Insurance Commission (HIC). Permission was sought for access to the Health Insurance Commission HIC for purposes of establishing use of medical care and services and expenditure. The information sought from the HIC database included: the number of medical care services, and for each service, the nature of the service, date, charge, and benefit; the number of PBS prescriptions, and for each prescription, the drug prescribed, number of repeats, date, charge, and benefit.

This dataset, released in August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following countries are designated as ES: Canada, Ireland, New Zealand, South Africa, United Kingdom and the United States of America; the remaining countries are designated as NES. The dataset also includes the population of people born overseas and report poor proficiency in English. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016.

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.

The number of Aboriginal females and their proportion of the total Aboriginal female population for 5 year age groups to 65+, 2015. Aboriginal as used in this workbook refers to Aboriginal and Torres Strait Islander people (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on Indigenous population estimates refer to: http://phidu.torrens.edu.au/. Source: Compiled by PHIDU based on data developed by Prometheus Information Pty Ltd, under a contract with the Australian Government Department of Health.