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.

Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data was reported at 5,132,355.000 Person in 2017. This records an increase from the previous number of 5,024,923.000 Person for 2016. Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data is updated yearly, averaging 4,643,072.500 Person from Jun 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 5,132,355.000 Person in 2017 and a record low of 4,256,161.000 Person in 2006. Australia Population: Resident: Estimated: Annual: New South Wales: Greater Sydney data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G002: Estimated Resident Population.

This dataset contains projected population figures from Transport for NSW’s Travel Zone Projection 2016 (TZP2016) model (formally known as LU16*). The data includes: • Estimated Resident Population …Show full descriptionThis dataset contains projected population figures from Transport for NSW’s Travel Zone Projection 2016 (TZP2016) model (formally known as LU16*). The data includes: • Estimated Resident Population (ERP) (including 5-year age categories by sex); • Population in occupied private dwellings (POPD) • Population in non-private dwellings (PNPD); and • Occupied private dwellings (OPD) The TZP2016 projections reflect the Sydney Greater Metropolitan Area (GMA) and are provided on a 5-yearly basis for the period 2011-2056.

This dataset contains projected population figures from Transport for NSW’s Travel Zone Projection 2016 (TZP2016) model (formally known as LU16*). The data includes:\r \r \r •\tEstimated Resident Population (ERP) (including 5-year age categories by sex);\r \r •\tPopulation in occupied private dwellings (POPD)\r \r •\tPopulation in non-private dwellings (PNPD); and\r \r •\tOccupied private dwellings (OPD)\r \r \r The TZP2016 projections reflect the Sydney Greater Metropolitan Area (GMA) and are provided on a 5-yearly basis for the period 2011-2056.\r

This dataset contains projected population figures from Transport for NSW’s Travel Zone Projection 2016 (TZP2016) model (formally known as LU16*). The data includes:

• Estimated Resident Population (ERP) (including 5-year age categories by sex);

• Population in occupied private dwellings (POPD)

• Population in non-private dwellings (PNPD); and

• Occupied private dwellings (OPD)

The TZP2016 projections reflect the Sydney Greater Metropolitan Area (GMA) and are provided on a 5-yearly basis for the period 2011-2056.

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.

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.

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.

Residential property information web-scraped off https://domain.com.au, joined with suburb data and economic data.

The NSW Heat Vulnerability Index (HVI) dataset identifies areas to monitor where populations in the Sydney Greater Metropolitan Area are more vulnerable to the adverse effects of urban heat, as of Summer 2015-2016. HVI utilises indicators for exposure, sensitivity and adaptive capacity to calculate an overall heat vulnerability index. Expressed through the data, a vulnerability of 1 represents a combination of low exposure, low sensitivity and/or high adaptive capacity. A vulnerability of 5 represents high exposure, high sensitivity and/or low adaptive capacity. The calculation of HVI and the inputs to the exposure, sensitivity and adaptive capacity indicators are explained in the metadata. The HVI data is aggregated to the Australian Bureau of Statistics (ABS) Statistical Area Level 1 (SA1) polygon dataset to enable spatial analysis to support local policy and decision making. It can be used in conjunction with the NSW urban vegetation cover dataset for the same time period for broader analysis of the relationship of heat to green cover.

Context

The dataset is originated from Sydney Suburb Reviews. It includes 30 features over 421 suburbs in Sydney. The features are:

Name Region Population (rounded)* Postcode Ethnic Breakdown 2016 Median House Price (2020) Median House Price (2021) % Change Median House Rent (per week) Median Apartment Price (2020) Median Apartment Rent (per week) Public Housing % Avg. Years Held Time to CBD (Public Transport) [Town Hall St] Time to CBD (Driving) [Town Hall St] Nearest Train Station Highlights/Attractions Ideal for Traffic Public Transport Affordability (Rental) Affordability (Buying) Nature Noise Things to See/Do Family-Friendliness Pet Friendliness Safety Overall Rating Review Link

These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. 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. A data update was provided in August, 2020 to correct some incorrectly given longitude values.

人口：居民：估计：年度：新南威尔士州：大悉尼 在06-01-2017达5,132,355.000人，相较于06-01-2016的5,024,923.000人有所增长。人口：居民：估计：年度：新南威尔士州：大悉尼 数据按年更新，06-01-2006至06-01-2017期间平均值为4,643,072.500人，共12份观测结果。该数据的历史最高值出现于06-01-2017，达5,132,355.000人，而历史最低值则出现于06-01-2006，为4,256,161.000人。CEIC提供的人口：居民：估计：年度：新南威尔士州：大悉尼 数据处于定期更新的状态，数据来源于Australian Bureau of Statistics，数据归类于全球数据库的澳大利亚 – 表 AU.G002：估计常住人口。

Over **** million people attended performing arts events at the Sydney Opera House in Australia in the year ending June 2024. This marked a significant increase in attendance compared to 2021 and 2022, which were greatly impacted by the suspension of public-facing activities due to the COVID-19 pandemic. The highest Sydney Opera House performing arts events attendance during the given period was recorded in financial year 2016, at over *** million people.

In 2024, there were 7,313 health and fitness centers in operation in Australia. Since 2020, the number of health and fitness centers in the country have grown by over 1,400, though the number of new gyms in recent years has slowed significantly.
How strong is the industry? Australians are becoming more health conscious, and this is reflected in the fitness and gym industry showing steady growth in recent years. More and more Australians are opting for gym memberships as they appreciate the benefits of physical activity. Australians of all ages use gyms, fitness clubs, sports or leisure centers, with the largest number belonging to the 25 to 35 years age bracket. Physical activity in Australia Australians are a very active people, with many engaging in group and solo ohysical activities. One of the more popular forms of physical activities in the country is walking for recreation, with several million Australians engaging in regular walks. Nealry a third of Australians participate in some form of sport at least once per week.

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.