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.

Chart and table of population level and growth rate for the Sydney, Australia metro area from 1950 to 2025.

This statistic shows a breakdown of the student population of Australia in 2016, by city. In 2016, there were just under 295 thousand students in Melbourne, followed by Sydney with around 219 thousands.

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 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. 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.

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 …Show full descriptionThese 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. Version Description:

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.