Australia Population in Largest City: as % of Urban Population data was reported at 22.768 % in 2024. This records an increase from the previous number of 22.673 % for 2023. Australia Population in Largest City: as % of Urban Population data is updated yearly, averaging 24.964 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 27.701 % in 1971 and a record low of 22.181 % in 2013. Australia Population in Largest City: as % of Urban Population 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: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Australia Population in Largest City data was reported at 5,315,600.000 Person in 2024. This records an increase from the previous number of 5,235,407.000 Person for 2023. Australia Population in Largest City data is updated yearly, averaging 3,709,165.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 5,315,600.000 Person in 2024 and a record low of 2,134,673.000 Person in 1960. Australia Population in Largest City 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: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

This dataset presents the preliminary estimates of the resident population by age and sex as at 30 June 2017. The data is aggregated to Greater Capital City Statistical Areas (GCCSA), according to …Show full descriptionThis dataset presents the preliminary estimates of the resident population by age and sex as at 30 June 2017. The data is aggregated to Greater Capital City Statistical Areas (GCCSA), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period. This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Explanatory Notes. AURIN has spatially enabled the data. Regions which contain unpublished data have been left blank in the dataset. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

Australia Population Density: People per Square Km data was reported at 3.382 Person/sq km in 2022. This records an increase from the previous number of 3.339 Person/sq km for 2021. Australia Population Density: People per Square Km data is updated yearly, averaging 2.263 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 3.382 Person/sq km in 2022 and a record low of 1.365 Person/sq km in 1961. Australia Population Density: People per Square Km 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: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

Forecasts of Geelong's population growth for the next 50 years. The data shows a breakdown of both resident and private dwelling information and breaks this down by suburb.

Although all due care has been taken to ensure that these data are correct, no warranty is expressed or implied by the City of Greater Geelong in their use.

This data collection contains de-identified clinical health service utilisation data from Bendigo Health and the General Practitioners Practices associated with the Loddon Mallee Murray Medicare Local. The collection also includes associated population health data from the ABS, AIHW and the Municipal Health Plans. Health researchers have a major interest in how clinical data can be used to monitor population health and health care in rural and regional Australia through analysing a broad range of factors shown to impact the health of different populations. The Population Health data collection provides students, managers, clinicians and researchers the opportunity to use clinical data in the study of population health, including the analysis of health risk factors, disease trends and health care utilisation and outcomes.Temporal range (data time period):2004 to 2014Spatial coverage:Bendigo Latitude -36.758711200000010000, Bendigo Longitude 144.283745899999990000

The Population Health Area (PHA) data include totals for the Greater Capital City Statistical Areas/ Rest of States/NT, States/ Territories and Australia; and for the Statistical Areas Level 3 and …Show full descriptionThe Population Health Area (PHA) data include totals for the Greater Capital City Statistical Areas/ Rest of States/NT, States/ Territories and Australia; and for the Statistical Areas Level 3 and Level 4. Attribution: Torrens University Australia

The workforce dataset contains monthly workforce sizes from July 2005 to June 2018 in the eight Australian capital cities with estimated stratification by indoor and outdoor workers. It is included in both csv and rda format. It includes variables for:

Year Month GCCSA (Greater Capital City Statistical Area, which is used to define capital cities) Date (using the first day of the month) fulltime: Fulltime workers parttime: Parttime workers n. Overall workers outorin. Estimated indoor or outdoor status

This data are derived from the Australian Bureau of Statistics (ABS) Labour Force, Australia, Detailed, LM1 dataset: LM1 - Labour force status by age, greater capital city and rest of state (ASGS), marital status and sex, February 1978 onwards (pivot table). Occupational data from the 2006, 2011 and 2016 Census of Population and Housing (ABS Census TableBuilder Basic data) were used to stratify this dataset into indoor and outdoor classifications as per the "Indooroutdoor classification.xlsx" file. For the Census data, GCCSA for the place of work was used, not the place of usual residence.

Occupations were defined by the Australian and New Zealand Standard Classification of Occupations (ANZSCO). Each 6-digit ANZSCO occupation (the lowest level classification) was manually cross-matched with their corresponding occupation(s) from the Canadian National Occupation System (NOC). ANZSCO and NOC share a similar structure, because they are both derived from the International Standard Classification of Occupations. NOC occupations listed with an “L3 location” (include main duties with outdoor work for at least part of the working day) were classified as outdoors, including occupations with multiple locations. Occupations without a listing of "L3 location" were classified as indoors (no outdoor work). 6-digit ANZSCO occupations were then aggregated to 4-digit unit groups to match the ABS Census TableBuilder Basic data. These data were further aggregated into indoor and outdoor workers. The 4-digit ANZSCO unit groups’ indoor and outdoor classifications are listed in "Indooroutdoor classification.xlsx."

ANZSCO occupations associated with both indoor and outdoor listings were classified based on the more common listing, with indoors being selected in the event of a tie. The cross-matching of ANZSCO and NOC occupation was checked against two previous cross-matches used in published Australian studies utilising older ANZSCO and NOC versions. One of these cross-matches, the original cross-match, was validated with a strong correlation between ANZSCO and NOC for outdoor work (Smith, Peter M. Comparing Imputed Occupational Exposure Classifications With Self-reported Occupational Hazards Among Australian Workers. 2013).

To stratify the ABS Labour Force detailed data by indoors or outdoors, workers from the ABS Census 2006, 2011 and 2016 data were first classified as indoors or outdoors. To extend the indoor and outdoor classification proportions from 2005 to 2018, the population counts were (1) stratified by workplace GCCSA (standardised to the 2016 metrics), (2) logit-transformed and then interpolated using cubic splines and extrapolated linearly for each month, and (3) back-transformed to the normal population scale. For the 2006 Census, workplace location was reported by Statistical Local Area and then converted to GCCSA. This interpolation method was also used to estimate the 1-monthly worker count for Darwin relative to the rest of Northern Territory (ABS worker 1-monthly counts are reported only for Northern Territory collectively).

ABS data are owned by the Commonwealth Government under a CC BY 4.0 license. The attached datasets are derived and aggregated from ABS data.

This dataset presents the estimates of the internal and overseas migration statistics of Australia by age by Greater Capital City Statistical Area (GCCSA) following the 2016 Australian Statistical Geography Standard (ASGS). The dataset spans from the 2016-17 financial year up to the 2019-20 financial year. Overseas migration is the movement of people from overseas to Australia's sub-state areas and vice-versa. It cannot be directly measured and is estimated by breaking down overseas migrant arrivals and departures at the state level to sub-state areas, using information from the most recent Census. The state-level overseas migration data is sourced from Department of Home Affairs processing systems, visa information, and incoming passenger cards, and is published in National, state and territory population. Internal migration is the movement of people across a specified boundary within Australia involving a change in place of usual residence. It cannot be directly measured and is instead estimated using administrative data. The movement of people between and within Australia's states and territories cannot be directly measured and is estimated using administrative data. Internal migration is estimated based on a combination of Census data (usual address one year ago), Medicare change of address data (provided by Services Australia), and Department of Defence records (for military personnel only). The Medicare source data is assigned to a state or territory and GCCSA for a person's departure and arrival locations, based on the postcodes of their residential addresses as registered with Medicare. Postcodes are assigned wholly to a state/territory and GCCSA based on best fit. Where a postcode is split across areas, it is assigned to the area that contains the majority of that postcode's population. For more information please visit the Regional population methodology. AURIN has spatially enabled the original data.

Population density metrics for 2011 Statistical Area Level 2 (SA2) within 2011 Greater Capital City Statistical Areas (GCCSA), including SA2 Population-weighted density (PWD) for 2011 and 2014, PWD change 2011-2014, and ERP population counts by density classes. Selected Density Classes were based on the Australian Population Density Grid published by the ABS, December 2014 (cat. no. 1270.0.55.007). Corresponding population metrics for 2011 GCCSAs. PWD using standardised 1km grid cells provides a more comparable measure of the density in larger regions. It does this by weighting the density using the proportion of population living at that density. In this way the density measure reflects the density at which people actually live. This removes the effect of large unpopulated areas that may be within the regions being compared. In this way comparisons between regions are more valid.

The map service can be viewed at http://soe.terria.io/#share=s-AgXEN0N0Q95icRW7M9JIC9IYBdE

Downloadable spatial data also available below.

Map prepared by the ABS and presented as Figure BLT3 in Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au.

Spatial data is not currently available. Please refer to https://www.tmr.qld.gov.au/travel-and-transport/long-distance-air-services for information and pdf map. Queensland is an extensive area with a population unevenly distributed within its borders and numerous island communities in the far north. It has the highest percentage of population outside a capital city of any mainland state within Australia, and land transport is disrupted by the annual wet season in northern and western regions. The Department of Transport and Main Roads regulates and in some cases subsidises a number of regular passenger transport air services to ensure that identified transport-disadvantaged communities have year-round access to a range of essential business, educational, medical, and cultural services in larger centres, reducing the social and economic isolation of rural and remote Queenslanders.

The Australian Marine Microbial Biodiversity Initiative (AMMBI) provides methodologically standardized, continental scale, temporal phylogenetic amplicon sequencing data describing Bacteria, Archaea and microbial Eukarya assemblages. Sequence data is linked to extensive physical, biological and chemical oceanographic contextual information. Samples are collected monthly to seasonally from multiple depths at seven National Reference Stations (NRS) sites: Darwin Harbour (Northern Territory), Yongala (Queensland), North Stradbroke Island (Queensland), Port Hacking (New South Wales), Maria Island (Tasmania), Kangaroo Island (South Australia), Rottnest Island (Western Australia). The Integrated Marine Observing System (IMOS) NRS network is described at http://imos.org.au/facilities/nationalmooringnetwork/nrs/ North Stradbroke Island NRS is located 6.6 nm north east of North Stradbroke Island at a depth of 60 m over sandy substrate. It is 30 km southeast of the major city of Brisbane, Queensland (population 2.099 million), at the opening to large, shallow, Moreton Bay. The site is impacted by the southerly flowing EAC and its eddies, which may cause periodic nutrient enrichment through upwelling. This latitude is the biogeographic boundary for many tropical and subtropical species. The water column is well mixed between May-August and stratified for the remainder of the year and salinity may at times be affected by floodwaters from the nearby Brisbane River outflow.

Site details from Brown, M. V. et al. Continental scale monitoring of marine microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci. Data 5:180130 doi: 10.1038/sdata.2018.130 (2018). Site location: North Stradbroke Island National Reference Station (NRS), Queensland, Australia Note on data download/processing: Data downloaded from Australian Microbiome Initiative via Bioplatforms Australia Data Portal on 17 June 2022. The search filter applied to download data from Bioplatforms Australia Data portal are stored in the Darwin Core property (identificationRemarks). Taxonomy is assigned according to the taxonomic database (SILVA 138) and method (Sklearn) which is stored in the Darwin Core Extension DNA derived data property (otu_db). Prefix were removed from the taxonomic names as shown in the example (e.g. d_Bacteria to Bacteria). Scientific name is assigned to the valid name available from the highest taxonomic rank. This collection is published as Darwin Core Occurrence, so the event level measurements need to be replicated for every occurrence. Instead of data replication, the event level eMoF data are made available separately at https://www.marine.csiro.au/data/services/obisau/emof_export.cfm?ipt_resource=bioplatforms_mm_nrs_nsi Please see https://www.australianmicrobiome.com/protocols/acknowledgements/ for citation examples and links to the data policy.

The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations.

The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25.

Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet download, including complete subsetting and data format conversion services.

This dataset presents data on education and employment available from the ABS Data by Region statistics. This release of Data by Region presents various data for 2011-2019 and Census of Population and Housing data for 2011 and 2016 and is based on the Greater Capital City Statistical Area (GCCSA) 2016 boundaries. The dataset includes information in the following specified areas of education and employment: Early Childhood - Enrolment and Attendance in Preschool Programs, Non-School Qualifications, Higher Education Loan Program (HELP) Repayments, Highest Year of School Completed, Occupation of Employed Persons, Youth Engagement in Work or Study, Jobs in Australia and Labour Force.

Data by Region contains a standard set of data for each region type, depending on the availability of statistics for particular geographies. Data are sourced from a wide variety of collections, both ABS and non-ABS. When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope and coverage can differ across collections. Where available, data have been presented as a time series - to enable users to assess changes over time. However, when looked at on a period to period basis, some series may sometimes appear volatile. When analysing the data, users are encouraged to consider the longer term behaviour of the series, where this extra information is available.

For more information please visit the Explanatory Notes.

AURIN has made the following changes to the original data:

• Spatially enabled the original data with the ABS Australian Statistical Geography Standard (ASGS) GCCSA 2016 dataset.

• Some data values in Data by Region have been randomly adjusted or suppressed to avoid the release of confidential details.

• Where data was not available, not available for publication, nil or rounded to zero in the original data, it has been set to null.

• Columns and rows that did not contain any values in the original data have been removed.

This dataset presents the footprint of statistics related to the leading causes of death by sex. The reported statistics include cause of death, ranking, total deaths, crude rates, age-standardised rates and rate ratio. The data spans the period between 2012-2016 and is aggregated to Greater Capital City Statistical Area (GCCSA) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS).

Mortality Over Regions and Time (MORT) books are workbooks that contain recent deaths data for specific geographical areas, sourced from the Australian Institute of Health and Welfare (AIHW) National Mortality Database. They present various statistics related to deaths by all causes and leading causes of death by sex for each geographical area.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - MORT Books.

Please note:

• AURIN has spatially enabled the original data.

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the National Mortality Database.

• Year refers to the year of registration of death. Deaths registered in 2013 and earlier are based on the final version of the cause of death data; deaths registered in 2014 are based on revised version; deaths registered in 2015 and 2016 are based on preliminary versions. Revised and preliminary versions are subject to further revision by the ABS.

• Cause of death information are based on the underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Unknown/missing includes deaths where place of usual residence was overseas, no fixed abode, offshore and migratory, and undefined. Summary measures and cause of death data are not presented for any GCCSA with less than 10 deaths in a single year; they are not presented for 'Other territories' because there were only 42 deaths recorded in 2012-2016.

• Population counts are based on estimated resident populations at 30 June for each year. Australian estimated resident population data are sourced from Australian demographic statistics (ABS cat. no. 3101.0).

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Greater Capital City Statistical Areas (GCCSA) from the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a GCCSA. The number of people who could not be assigned a GCCSA is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA.\r \r The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate.\r The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays.\r \r Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households.\r \r Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday.\r \r \r \r Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards).\r \r 1. Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19.\r \r 2. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. \r \r 3. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings.\r \r Changes to HTS post-COVID (2020/21 onwards)\r \r HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples.\r \r Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates.\r \r Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID:\r \r SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection.\r LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved\r Mode categories for all geographies are aggregated differently to the pre-COVID categories\r Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22.\r A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22.\r Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details.\r \r \r RELEASE NOTE\r \r The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details.\r \r A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables.\r \r Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.\r

This dataset presents the footprint of statistics related to deaths due to all causes (combined) by sex. The reported statistics include year of death, total deaths, crude rates, age-standardised rates, rate ratio, median age at death, premature deaths, potential years of life lost and potentially avoidable deaths. The data spans the years of 2012-2016 and is aggregated to Greater Capital City Statistical Area (GCCSA) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS).

Mortality Over Regions and Time (MORT) books are workbooks that contain recent deaths data for specific geographical areas, sourced from the Australian Institute of Health and Welfare (AIHW) National Mortality Database. They present various statistics related to deaths by all causes and leading causes of death by sex for each geographical area.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - MORT Books.

Please note:

• AURIN has spatially enabled the original data.

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the National Mortality Database.

• Year refers to the year of registration of death. Deaths registered in 2013 and earlier are based on the final version of the cause of death data; deaths registered in 2014 are based on revised version; deaths registered in 2015 and 2016 are based on preliminary versions. Revised and preliminary versions are subject to further revision by the ABS.

• Cause of death information are based on the underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Unknown/missing includes deaths where place of usual residence was overseas, no fixed abode, offshore and migratory, and undefined. Summary measures and cause of death data are not presented for any GCCSA with less than 10 deaths in a single year; they are not presented for 'Other territories' because there were only 42 deaths recorded in 2012-2016.

• Population counts are based on estimated resident populations at 30 June for each year. Australian estimated resident population data are sourced from Australian demographic statistics (ABS cat. no. 3101.0).