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.

A Heat Vulnerability Index was built with Open Data for Metropolitan Sydney, for the years 2011 and 2016. Vulnerability is defined as the propensity of a population to be adversely affected by extreme heat and depends on 3 components: the exposure, sensitivity and adaptive capacity of the population. These 3 sub-indexes were calculated with various indicators that you can find as attributes to this layer. The scale of the study is the Statistical Areas 2 (SA2) of the Australian Bureau of Statistics. Bodilis, Carole ; Yenneti, Komali; Hawken, Scott (2018): Heat Vulnerability Index for Sydney. Faculty of Built Environment, UNSW Sydney.

The 2022 Heat Vulnerability Index (HVI) for Greater Sydney aims to combine information on urban heat, built form and population demographics to provide a fine-grained understanding of the spatial distribution of heat vulnerable populations.

The Index combines indicators of heat exposure, sensitivity to heat, and adaptive capacity to produce the composite vulnerability index. The 2022 HVI dataset is built upon the methodology established in the creation of the 2016 Sydney HVI dataset (Sun et al 2018), integrating land cover, urban heat, and demographic data, aggregated to Statistical Area Level 1 (SA1) of the Australian Statistical Geography Standard (ASGS) produced by the Australian Bureau of Statistics (ABS).

Broad comparisons can be made between the 2022 and 2016 HVI datasets, however there are multiple factors that may limit direct comparability over time. This includes variations in underlying datasets, the relative nature of the HVI, and the change in size of the study area between 2016 and 2022. When undertaking comparison it is recommended to examine the changes in the underlying datasets and the absolute values of the heat exposure, sensitivity and adaptive capacity indicators. This approach helps to explain the variations in HVI and informs effective heat mitigation strategies.

The 2022 HVI is most useful at the SA1 scale. It is not recommended to aggregate the HVI dataset to larger scales (i.e. average HVI for a suburb or LGA). Aggregating spatially specific and individual data to geographic areas smooths out local variation, losing locational specificity and population variation. In cases where individual human exposure is of concern, this may either increase or decrease the representation of the actual exposure of a given individual, causing the neighbourhood effect averaging problem (NEAP) (Kwan 2018).

Please refer to the methodology report for more information. Please note that the methodology report was updated in October 2025, adjusting the adaptive capacity SEIFA IER parameter label.

The 2022 Heat Vulnerability Index (HVI) for Greater Sydney aims to combine information on urban heat, built form and population demographics to provide a fine-grained understanding of the spatial distribution of heat vulnerable populations.\r \r The Index combines indicators of heat exposure, sensitivity to heat, and adaptive capacity to produce the composite vulnerability index. The 2022 HVI dataset is built upon the methodology established in the creation of the 2016 Sydney HVI dataset (Sun et al 2018), integrating land cover, urban heat, and demographic data, aggregated to Statistical Area Level 1 (SA1) of the Australian Statistical Geography Standard (ASGS) produced by the Australian Bureau of Statistics (ABS).\r \r Broad comparisons can be made between the 2022 and 2016 HVI datasets, however there are multiple factors that may limit direct comparability over time. This includes variations in underlying datasets, the relative nature of the HVI, and the change in size of the study area between 2016 and 2022. When undertaking comparison it is recommended to examine the changes in the underlying datasets and the absolute values of the heat exposure, sensitivity and adaptive capacity indicators. This approach helps to explain the variations in HVI and informs effective heat mitigation strategies.\r \r The 2022 HVI is most useful at the SA1 scale. It is not recommended to aggregate the HVI dataset to larger scales (i.e. average HVI for a suburb or LGA). Aggregating spatially specific and individual data to geographic areas smooths out local variation, losing locational specificity and population variation. In cases where individual human exposure is of concern, this may either increase or decrease the representation of the actual exposure of a given individual, causing the neighbourhood effect averaging problem (NEAP) (Kwan 2018).\r \r Please refer to the methodology report for more information. Please note that the methodology report was updated in October 2025, adjusting the adaptive capacity SEIFA IER parameter label.

This data is part of the strategic transport modelling undertaken for Outer Urban Public Transport Maps, which was released in October 2018.

The Outer Urban Public Transport Maps showcases through interactive maps, the comparative public transport network performance for Greater Sydney.

The following resources are available, links have been provided that direct you to the current source of data.

Walking access to medium- to high-frequency public transport

This layer presents the proportion of people within walking distance to high-medium frequency public transport stops/stations in 2017. Walking distance is defined as 800 metres for heavy rail, and 400 metres for all other modes. High-frequency public transport is defined as having at least four services per hour during AM peak. This analysis was performed using 2017 timetables.

Public transport travel times to Sydney CBD

This layer presents travel times, by public transport during AM peak, to a series of destinations. This analysis was performed using 2017 public transport timetables. The layers represents the geographical extent of the inner, middle, and outer sectors used to perform the analysis in the report.

Public transport service frequency

This layer presents public transport stop frequency during weekday AM peak (8-9am) and weekday off peak (11am-12am). This analysis was performed using 2017 public transport timetables.

These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western …Show full descriptionThese aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. Version Description:

BackgroundMultiple Sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. Although psoriasis and psoriasiform dermatitis are reported in MS patients, the prevalence of the diseases is uncertain globally and unstudied in Australia. This study aims to determine the frequency of psoriasis in a clinic-based cohort of Australian MS patients.MethodsA survey was conducted on 204 consecutive MS patients aged 18 and over who attended a tertiary MS clinic in Northern Sydney from July 2018 to December 2022.ResultsA total of 204 patients were examined, comprising 137 female (67.2%) and 67 male (32.8%). The mean age was 48.8 years (SD = 13.6). Psoriasis was identified in 13.7% (28/204; 95% CI: 9.63% to 19.20%).DiscussionThe frequency of psoriasis in MS is high and may be underestimated, given that many more patients have symptoms without signs. This implies an immunopathological link between the two conditions and is worthy of further study.

Admixture between species is a cause for concern in wildlife management. Canids are particularly vulnerable to inter-specific hybridisation, and genetic admixture has shaped their evolutionary history. Microsatellite DNA testing, relying on a small number of genetic markers and geographically restricted reference populations, has identified extensive domestic dog admixture in Australian dingoes and driven conservation management policy. There has been concern that geographic variation in dingo genotypes could confound ancestry analyses that use a small number of genetic markers. Here we apply genome-wide single nucleotide polymorphism (SNP) genotyping to a set of 385 wild and captive dingoes from across Australia and then carry out comparisons to domestic dogs, and perform ancestry modelling and biogeographic analyses to characterize population structure in dingoes and investigate the extent of admixture between dingoes and dogs in different regions of the continent. We show that there ..., This data was collected by microarray SNP genotyping using Axiom Canine Set A, Axiom Canine Set B and Axiom CanineHD arrays (Thermo Fischer Scientific Inc). Microarrays were processed at either the Ramaciotti Centre for Genomics (Sydney, Australia) or Thermo Scientific Microarray Research Services Laboratory (Santa Clara, CA, USA). Both the raw CEL files off the GeneTitan microarray scanner and Plink SNP datasets are provided., Included is a readme file, an Excel .xlsx file with sample metadata for all 543 samples, an Excel .xlsx file with Axiom Set A and Set B CEL file names for the 112 dog samples, and a binary (.bed, .bim, .fam) Plink dataset containing the filtered SNP dataset for 543 canids. Raw .CEL files for the 431 dingo and domestic dog samples genotyped on the Axiom Canine HD array at the Ramaciotti Centre for Genomics are provided. Raw .CEL files for the 112 domesitc dog samples genotyped on the Axiom Canine Set A and Axiom Canine Set B array at the Thermo Scientific Microarray Research Services Laboratory are provided. , # Axiom canine microarray data from Australian dingoes and domestic dogs for admixture and population structure analysis

This readme file was generated on 2022-10-10 by Kylie M Cairns

GENERAL INFORMATION

Date of data collection: 2018-2021

Geographic location of data collection: Australia and United States of America

SHARING/ACCESS INFORMATION

Links to publications that cite or use the data: Cairns et al. Genome-Wide Variant Analyses Reveals New Patterns of Admixture and Population Structure in Australian Dingoes. Molecular Ecology.

Links to other publicly accessible locations of the data: na.

Links/relationships to ancillary data sets: na.

Was data derived from another source?: No.

DATA & FILE OVERVIEW

File List:

MS1_sample_metadata_v5_upload.xlsx - metadata Cairnsetal_admix_dataset_20221012.fam - plink files - fixed order. Cairnsetal_admix_dataset_20221012.bim - plink files Cairnsetal_admix_dataset_20221012.bed - plink files Axiom_Canine_SetA_SetB_cels_112.zip - Cel files f...,

The Gippsland Basin is the location of Australia’s oldest offshore oil and gas (O&G) structures, with hydrocarbon production beginning in the 1960s. The Bass Strait flows over this area with fisheries providing seafood for the major population centers of Melbourne, Sydney and beyond. Since Australia’s maritime legislation restricts activities to outside of 500 meters from O&G structures as a security exclusion zone, these O&G structures may serve as de facto marine protected areas that may have spillover effects to local fisheries. Therefore, it is critical to understand the habitat value of O&G infrastructure to marine life in the Bass Strait and whether decommissioning of these structures affect local marine ecosystems and fisheries. We analyzed industry-collected remotely operated vehicle (ROV) imagery from 2008-2018 and compared this data with reported catch data from fishing vessels operating in this region collected by the Australian Fisheries Management Authority (AFMA) from 2008-2018. We assessed species richness and relative abundance on two platforms and two pipelines and compared the species composition with retained catch reported by commercial fishers operating in Commonwealth fisheries. We found diverse communities of fishes and invertebrates around O&G structures, with a different subset of species inhabiting pipelines than platforms. We found little overlap between the species that were targeted by commercial fishers and those found around O&G structures (10% overlap), however, species composition data from fisheries often groups species making the data coarse and under-representative of true species diversity. Fishery-independent data from ROV imagery or other methods greatly augments our understanding of deepwater marine communities, including those around O&G structures. Combining data sources provides a holistic look at these novel ecosystems and provides better insight into future decommissioning scenarios.

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. 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. The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays. 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. 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. 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). Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19. 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. 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. Changes to HTS post-COVID (2020/21 onwards) 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. 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. Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID: SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection. LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved Mode categories for all geographies are aggregated differently to the pre-COVID categories Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22. A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22. Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details. RELEASE NOTE 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. 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. 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.

The rent price index in Australia in the first quarter of 2025 was *****, marking an increase from the same quarter of the previous year. Rent prices had decreased in 2020; in Melbourne and Sydney, this was mainly attributed to the absence of international students during the coronavirus outbreak. The current state of the rental market in Australia The rental market in Australia has been marked by varying conditions across different regions. Among the capital cities, Sydney has long been recognized for having some of the highest average rents. As of March 2025, the average weekly rent for a house in Sydney was *** Australian dollars, which was the highest average rent across all major cities in Australia that year. Furthermore, due to factors like population growth and housing demand, regional areas have also seen noticeable increases in rental prices. For instance, households in the non-metropolitan area of New South Wales’ expenditure on rent was around ** percent of their household income in the year ending June 2024. Housing affordability in Australia Housing affordability remains a significant challenge in Australia, contributing to a trend where many individuals and families rent for prolonged periods. The underlying cause of this issue is the ongoing disparity between household wages and housing costs, especially in large cities. While renting offers several advantages, it is worth noting that the associated costs may not always align with the expectation of affordability. Approximately one-third of participants in a recent survey stated that they pay between ** and ** percent of their monthly income on rent. Recent government initiatives, such as the 2024 Help to Buy scheme, aim to make it easier for people across Australia to get onto the property ladder. Still, the multifaceted nature of Australia’s housing affordability problem requires continued efforts to strike a balance between market dynamics and the need for accessible housing options for Australians.