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 2 (SA2) 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 SA2 boundaries.

Disclaimer: The material contained in this publication is made available on the understanding that the Commonwealth is not providing professional advice, and that users exercise their own skill and care with respect to its use, and seek independent advice if necessary. The Commonwealth makes no representations or warranties as to the contents or accuracy of the information contained in this publication. To the extent permitted by law, the Commonwealth disclaims liability to any person or organisation in respect of anything done, or omitted to be done, in reliance upon information contained in this publication.

The Progress in Australian Regions and Cities dataset presents the underlying data from the Progress in Australian Regions Dashboard – which was released as an online interactive dashboard for the first time in 2020. The Dashboard is a statistical resource that shows how regions are progressing against a range of key indicators from the following themes: labour market, infrastructure, housing, economic activity, environment, demography and well-being. Users can access the data by indicator theme, drilling down to their region and indicator of interest. The Dashboard can be accessed at the Bureau of Communications, Arts and Regional Research (BCARR) website.

This Dashboard supersedes the paper-based Progress in Australian Regions Yearbook publication. Datasets from previous editions of the Progress in Australian Regions Yearbook are also provided here for continuity.

Note that data for areas with very small populations should be used with caution, as small numbers can be significantly impacted by random adjustment.

This dataset has been developed by the Australian Government as an authoritative source of indigenous location names across Australia. It is sponsored by the Spatial Policy Branch within the Department of Communications and managed solely by the Department of Human Services.

The dataset is designed to support the accurate positioning, consistent reporting, and effective delivery of Australian Government programs and services to indigenous locations.

The dataset contains Preferred and Alternate names for indigenous locations where Australian Government programs and services have been, are being, or may be provided. The Preferred name will always default to a State or Territory jurisdiction's gazetted name so the term 'preferred' does not infer that this is the locally known name for the location. Similarly, locational details are aligned, where possible, with those published in State and Territory registers.

This dataset is NOT a complete listing of all locations at which indigenous people reside. Town and city names are not included in the dataset. The dataset contains names that represent indigenous communities, outstations, defined indigenous areas within a town or city or locations where services have been provided.

The number of unemployed persons in Australia increased to 659.58 Thousand in June of 2025 from 625.97 Thousand in May of 2025. This dataset provides - Australia Unemployed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Housing serves many purposes in our society. It provides space for raising families, for leisure and rest, and increasingly, our housing doubles as a workspace. Housing also impacts our mental and physical health due to factors such as cold, mould, poorly managed maintenance issues, unaffordability, and inequality. Despite the centrality of housing in our everyday lives, we as researchers are yet to have a systematic understanding of Australian housing conditions and changes over time. Building on the earlier housing conditions projects in this series, including the Australian Housing Conditions Dataset (2016) and the Australian Rental Housing Conditions Dataset (2020), in 2022 we collected data on the housing conditions of 15,000 rental (including private and public) households and 7,500 homeowners, covering all Australian states and territories. Recognising the emerging importance of renting in Australia, the sampling was weighted to oversample rental households. This data infrastructure will provide the knowledge base for national and international research and allow better urban, economic and social policy development. The project is funded by the Australian Research Council through the Linkage Infrastructure, Equipment and Facilities (LIEF) grant program, in partnership with The University of Adelaide, the University of South Australia, the University of Melbourne, Swinburne University of Technology, Curtin University and Torrens University Australia and is led by Professor Emma Baker at the University of Adelaide.

A continuation of the "Phytochemistry of Australian Plants" database compiled by David Collins and Don McGilvery. Contains chemical structures, references, species names, with persistent identifiers to the literature and Atlas of Living Australia (ALA) for geographical distributions. The current curation effort here adds DOIs/ISBNs/ISSNs for ~80% of references, persistent IDs for all species or genus to the ALA or other datasets, and validated structures (smiles) for ~70% of structures. No new entries have been added since the last update to the original database in 2022. Change log is in the README file.

Data provided here was obtained by the listed authors on linked publications, and these authors may have no association with CSIRO. CSIRO acknowledges that the publications linked here may contain Indigenous Cultural and Intellectual Property (ICIP), including traditional knowledge. CSIRO recognizes that First Nations peoples have the right to control, own and maintain their ICIP in accordance with Article 31 of the United Nations Declaration on the Rights of Indigenous Peoples. Users of this dataset may need to obtain permission from First Nations peoples for use of the information in linked publications. Users intending to collect and use biological specimens containing the compounds described in the dataset may also require permission of First Nations peoples, and may require permits and access permission from landholders. Recognizing that any ICIP in the linked publications is already publicly available but that the publications are not readily accessible by First Nations peoples, CSIRO is committed to finding ways to make the ICIP in these publications more findable and accessible to the First Nations communities from which the knowledge was originally obtained. Users should be aware that because of the historical context of some of the linked publications, they may contain words, descriptions, images or terms which may be culturally sensitive and/or offensive and that reflect authors’ views, or those of the period in which the content was created but may not be considered appropriate today. If First Nations people identify content within this dataset that they consider breaches cultural protocols they are encouraged to contact CSIRO on csiroenquiries@csiro.au or +61 3 9545 2176 to request its removal from the dataset. Please note that while CSIRO is able to administer the data housed within this dataset, this control does not extend to the associated publications. Requests to remove publications should be directed to the associated publishing company. Lineage: Original data extracted in 2022 from https://fms05.filemakerstudio.com.au/fmi/webd?homeurl=http://www.monash.edu/#PhytoChem by kind permission of David Collins and Don McGilvery.

This data set shows designated and actual irrigation areas in Australia compiled by the National Land Use Mapping Project of the National Land and Water Resources Audit to assist in the identification of irrigation areas in Australia. Additional data custodians include Agriculture WA, Queensland Department of Natural Resources and Mines, Murray-Darling Basin Commission, New South Wales Department of Land and Water Conservation and Tasmanian Department of Primary Industries, Water and Environment Designated irrigation areas indicate areas with administratively defined boundaries which have associated rights and obligations pertaining to use of water for irrigation. The precise meaning of the term designated irrigation area varies from region to region. aActual irrigation areasa indicate areas with observationally defined boundaries within which irrigation is practised. The boundaries have been supplied by various agencies and cover the more important irrigation areas in Australia. Users of this data set should be aware that there are irrigated areas outside the designated and actual areas shown and that there are non-irrigated areas inside them. This is particularly true of Tasmania and the Murray-Darling Basin.The data set is available in both vector and raster formats. The raster data set can be used as a companion to the 1996/97 Land Use of Australia data set which is also in raster format. Both data sets have the same coordinate system, boundary coordinates and cell size so that they can easily be overlaid. Users may find, however, that some cells are classified as irrigated by the Australian Irrigation Areas data set and as non-agricultural land by the 1996/97 Land Use of Australia data set.The Version 1a data set may be of use to researchers and policy makers in need of national, regional or local scale irrigation data, though the scale of the source material is highly variable and completness of coverage is poor in some regions.

See further metadata for more detail.

Lineage: The data set was constructed in vector format by appending irrigation area boundary data sets supplied by various agencies. The component data sets are listed below. One of the component data sets which was supplied as separate tiles in ArcView shapefile format was assembled into a single shapefile data set in ArcView 3.1. All other processing was carried out in ARC/INFO 7.2.1 under SunOS using double precision coordinates. For all operations in which processing used a fuzzy tolerance, the value specified was 0.00001 degrees (about 1 m). The raster form of the data set was made from the vector form. 1) Ord River Scheme, Stage 1, irrigation area boundaries. This data set was supplied by Agriculture WA in ArcView shapefile format.2) Boundary of Gazetted Irrigation Areas in Queensland. This data set was supplied by the Queensland Department of Natural Resources and Mines in ARC/INFO export file format. It shows designated irrigation areas. The coordinate datum is not known with certainty and was assumed to be the Australian Geodetic Datum 1984.3) Northern Murray-Darling Basin irrigation area boundaries. This data set was supplied by the Murray-Darling Basin Commission in ARC/INFO export file format. It shows actual irrigation areas, based on interpretation of coarse scale imagery derived from Landsat TM images. The data set comprises 11 polygons. Of these, five have not been retained in the national data set because they largely coincide with the much higher resolution polygons of the Northern New South Wales Cotton Development data set. A sixth has not been retained because it proves to be almost entirely covered by a national park and a perennial lake. One of the polygons that was retained was edited to give precedence to a higher resolution polygon in the Boundary of Gazetted Irrigation Areas in Queensland data set which it partly overlies.4) Northern New South Wales Cotton Development data set. This data set was supplied by the New South Wales Department of Land and Water Conservation, in ArcView shapefile format, as 50 separate tiles. It shows actual irrigation areas and a variety of other land uses and land covers related to cotton growing. It is based on interpretation of aerial photography and satellite imagery with extensive field checking. Only the polygons showing irrigation areas were included in the national data set. The tiles were merged in ArcView using the merge function of the GeoProcessing Wizard. Further processing was undertaken in AR

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the May 2025 release

• Nationally, the May 2025 update of G-NAF shows an overall increase of 47,194 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,753,927 of which 14,909,770 or 94.64% are principal.

• At some locations, there are unit-numbered addresses that appear to be duplicate addresses. Geoscape is working to identify these locations and include these addresses as separate addresses in G-NAF. To facilitate this process, some secondary addresses have had the word RETAIL added to their building names. In the first instance, this process is being progressively rolled out to identified locations, but it is expected that the requirement for this will become ongoing.

• There is one new locality in G-NAF: Keswick Island, QLD.

• The source data used for generating G-NAF STREET_LOCALITY_POINT data in New South Wales has an updated datum and changed from GDA94 to GDA2020. This has resulted in updates to the STREET_LOCALITY_POINT geometry for approximately 91,000 records, however, more than 95% of these have moved less than a metre.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data was reported at 1.396 % in 2023. This records a decrease from the previous number of 1.670 % for 2022. AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data is updated yearly, averaging 1.150 % from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 1.670 % in 2022 and a record low of 0.850 % in 2014. AU: Educational Attainment: Doctoral or Equivalent: Population 25+ Years: Female: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

The number of Facebook users in Australia was forecast to continuously decrease between 2024 and 2028 by in total 1.5 million users (-9.94 percent). After the eighth consecutive decreasing year, the Facebook user base is estimated to reach 13.62 million users and therefore a new minimum in 2028. User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 Facebook users in countries like Fiji and New Zealand.

AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data was reported at 1.888 % in 2022. This records an increase from the previous number of 1.405 % for 2021. AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data is updated yearly, averaging 1.240 % from Dec 1993 (Median) to 2022, with 7 observations. The data reached an all-time high of 1.888 % in 2022 and a record low of 0.650 % in 1996. AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

AU: Secondary Education: General Pupils: % Female data was reported at 49.502 % in 2017. This records a decrease from the previous number of 49.511 % for 2016. AU: Secondary Education: General Pupils: % Female data is updated yearly, averaging 49.605 % from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 50.158 % in 2000 and a record low of 47.771 % in 1970. AU: Secondary Education: General Pupils: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training.;UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.;Weighted average;

AU: School Enrollment: Primary: Male: % Gross data was reported at 99.048 % in 2022. This records a decrease from the previous number of 99.797 % for 2021. AU: School Enrollment: Primary: Male: % Gross data is updated yearly, averaging 105.714 % from Dec 1971 (Median) to 2022, with 52 observations. The data reached an all-time high of 112.456 % in 1971 and a record low of 99.048 % in 2022. AU: School Enrollment: Primary: Male: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

AusCPR: Phytoplankton Abundance The Australian Continuous Plankton Recorder (AusCPR) survey is a joint project of CSIRO Oceans and Atmosphere (O&A) and the Australian Antarctic Division (AAD) to measure plankton communities as a guide to the health of Australia's oceans. The phytoplankton abundance data are analysed as per Richardson et al 2006. It is advised that anyone using the data should first read this methodology or contact the project contact person. The aims of the AusCPR survey are to: * map plankton biodiversity and distribution * develop the first long-term plankton baseline for Australian waters * document plankton changes in response to climate change * provide indices for fisheries management * detect harmful algal blooms * validate satellite remote sensing * initialise and test ecosystem models Servicing and maintenance of the CPRs and analysis of the samples for the AusCPR survey will be carried out by staff based at the CMAR laboratories in Queensland and at the AAD in Hobart. The project was funded by the Integrated Marine Observing System (IMOS) and falls with the Ships of Opportunity facility. Data storage and access is planned to be interoperable with other national and international programs through the IMOS eMarine Information Infrastructure (eMII). Results from the AusCPR survey are available through the IMOS data portal: https://imos.org.au/facilities/shipsofopportunity/auscontinuousplanktonrecorder For information on using the data please refer to the Richardson et al. 2006. As the taxonomic resolution of the data has changed over time, due to continual training, it is important that users refer to the change log tables included in your data download. These will provide information on the validity of the taxa, from what date we have been identifying certain taxa etc. Classification fields may be blank depending on the level to which that taxa has been identified, i.e. if only identified to family, genus and species will be blank. This data is freely available but please acknowledgment all relevant parties, as detailed in acknowledgement section: ---- These data are from the Australian Continuous Plankton Recorder (AusCPR) survey part of the Integrated Marine Observing System (IMOS) - IMOS is a national collaborative research infrastructure, supported by the Australian Government. Please also acknowledge the relevant party that helped to collect the data. This information is contained in the download of the zooplankton abundance. ANL - ANL Windarra container shipping CSIRO - Commonwealth Scientific and Industrial Research Organisation MNF - Marine National Facility Sealord Group Ltd. SOCPR - Southern Ocean Continuous Plankton Recorder Survey AIMS - Australian Institute of Marine Science GA - Geoscience Australia Swire Shipping Malaspina Cruise - March 2011 aboard vessel Hespérides ---- Additional information for this dataset may be available via the metadata link below: http://www.marlin.csiro.au/geonetwork/srv/eng/search?uuid=c1344979-f701-0916-e044-00144f7bc0f4

Some of the records are from the CSIRO Marine National Facility and if used in any products, please acknowledge with the following: We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353) in undertaking this research.

AU: Over-Age Students: Primary: Female: % of Female Enrollment data was reported at 3.405 % in 2017. This records an increase from the previous number of 3.370 % for 2016. AU: Over-Age Students: Primary: Female: % of Female Enrollment data is updated yearly, averaging 5.662 % from Dec 1971 (Median) to 2017, with 46 observations. The data reached an all-time high of 6.796 % in 2011 and a record low of 3.370 % in 2016. AU: Over-Age Students: Primary: Female: % of Female Enrollment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.;UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.;;

Household Spending MoM in Australia increased to 0.90 percent in May from 0 percent in April of 2025. This dataset includes a chart with historical data for Australia Household Spending MoM.

The Australian Census Longitudinal Dataset (ACLD) brings together a 5% sample from the 2006 Census with records from the 2011 Census to create a research tool for exploring how Australian society is changing over time. In taking a longitudinal view of Australians, the ACLD may uncover new insights into the dynamics and transitions that drive social and economic change over time, conveying how these vary for diverse population groups and geographies. It is envisaged that the 2016 and successive Censuses will be added in the future, as well as administrative data sets. The ACLD is released in ABS TableBuilder and as a microdata product in the ABS Data Laboratory.

The Census of Population and Housing is conducted every five years and aims to measure accurately the number of people and dwellings in Australia on Census Night.

Microdata products are the most detailed information available from a Census or survey and are generally the responses to individual questions on the questionnaire. They also include derived data from answers to two or more questions and are released with the approval of the Australian Statistician. The following microdata products are available for this longitudinal dataset: •ACLD in TableBuilder - an online tool for creating tables and graphs. •ACLD in ABS Data Laboratory (ABSDL) - for in-depth analysis using a range of statistical software packages.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

Part Time Employment in Australia increased to 40185 Persons in June from -42979 Persons in May of 2025. This dataset provides - Australia Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.