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

Original provider: Dr Belinda Cannell, Murdoch University

Dataset credits: Data provider Murdoch University - Biological Sciences and Biotechnology Originating data center Satellite Tracking and Analysis Tool (STAT) Project partner Murdoch University
University of NSW Project sponsor or sponsor description This project has been funded under the Australian Research Council Linkage Project Scheme. Funds have also been contributed by Department of Environment and Conservation,
Fremantle Ports, Department of Defence, Tiwest and the Winifred Violet Scott Trust fund.

Abstract: Little Penguins from Penguin and Garden islands in Perth, Western Australia, are tracked to determine the areas in which they travel and feed throughout the breeding season. Once the areas they regularly use are determined, the threats the penguins are exposed to, and their likelihood of occurrence, can be elucidated. This forms part of a broader project to determine the population viability analysis of the Little Penguins in the Perth metropolitan region.

Population: Resident: Estimated: Annual: Western Australia: Greater Perth data was reported at 2,039,041.000 Person in 2017. This records an increase from the previous number of 2,019,263.000 Person for 2016. Population: Resident: Estimated: Annual: Western Australia: Greater Perth data is updated yearly, averaging 1,863,214.500 Person from Jun 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 2,039,041.000 Person in 2017 and a record low of 1,576,912.000 Person in 2006. Population: Resident: Estimated: Annual: Western Australia: Greater Perth 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.

The dataset within this group contains the National Records of Scotland (NRS) mid-year population density estimates for the Perth and Kinross Council area for the years 2000 to 2020.

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.

With 109.9 men per one hundred women in the city, the greater Darwin area in Australia has the highest sex ratio. This is in stark contrast to the demographics of the other major cities in Australia which have more women than men. This is consistent with the fact that more than two thirds of all women between 25 and 64 participating in the workforce.Despite this fact, there is still some disparity between men and women in high level position as women are multiple times more likely to be sexually assaulted while men are much more likely to be victims of murder.The perpetrators of crimes are also much more likely to be men as there are

Identifying population structure and boundaries among communities of wildlife exposed to anthropogenic threats is key to successful conservation management. Previous studies on the demography, social and spatial structure of Indo-Pacific bottlenose dolphins (Tursiops aduncus) suggested four nearly discrete behavioral communities in Perth metropolitan waters, Western Australia. We investigated the genetic structure of these four communities using highly polymorphic microsatellite markers and part of the hypervariable segment of the mitochondrial control region. Overall, there was no evidence of spatial genetic structure. We found significant, yet very small genetic differentiation between some communities, most likely due to the presence of highly related individuals within these communities. Our findings of high levels of contemporary migration and highly related individuals among communities point toward a panmictic genetic population with continuous gene flow among each of the communities. In species with slow life histories and fission-fusion dynamics, such as Tursiops spp., genetic and socio-spatial structures may reflect different timescales. Thus, despite genetic similarity, each social community should be considered as a distinct ecological unit to be conserved because they are exposed to different anthropogenic threats and occur in different ecological habitats, social structure being as important as genetic information for immediate conservation management. The estuarine community, in particular, is highly vulnerable and appropriate conservation measures are needed in order to maintain its connectivity with the adjacent, semi-enclosed coastal communities.

Identifying population structure and boundaries among communities of wildlife exposed to anthropogenic threats is key to successful conservation management. Previous studies on the demography, social and spatial structure of Indo-Pacific bottlenose dolphins (Tursiops aduncus) suggested four nearly discrete behavioral communities in Perth metropolitan waters, Western Australia. We investigated the genetic structure of these four communities using highly polymorphic microsatellite markers and part of the hypervariable segment of the mitochondrial control region. Overall, there was no evidence of spatial genetic structure. We found significant, yet very small genetic differentiation between some communities, most likely due to the presence of highly related individuals within these communities. Our findings of high levels of contemporary migration and highly related individuals among communities point toward a panmictic genetic population with continuous gene flow among each of the communities. In species with slow life histories and fission-fusion dynamics, such as Tursiops spp., genetic and socio-spatial structures may reflect different timescales. Thus, despite genetic similarity, each social community should be considered as a distinct ecological unit to be conserved because they are exposed to different anthropogenic threats and occur in different ecological habitats, social structure being as important as genetic information for immediate conservation management. The estuarine community, in particular, is highly vulnerable and appropriate conservation measures are needed in order to maintain its connectivity with the adjacent, semi-enclosed coastal communities.

The data were collected to test hypotheses that microplastic concentrations in stormwater drains would be able to be predicted from: (1) the proportions of different land uses in stormwater catchments; (2) catchment population and land area; (3) rainfall preceding sample collection. The data show that microplastic fibres were the most common morphology across all drains, followed by fragments. Most microplastics detected were in the 100-530 µm size range, with lower proportions ≤ 25 µm or > 530 µm. The most common colour was black, followed by red, blue, and green with other colours < 5% of total particle counts. There was no statistically significant variation in microplastic concentrations between or within stormwater catchments. Linear mixed-effects models showed significant positive effects of catchment area, catchment population, and the proportion of industrial land, natural land and public open space on microplastic concentrations. The proportion of residential land had a significant negative effect on microplastic concentrations. The proportion of agricultural land in each catchment, and preceding rainfall, had no effect on microplastic concentrations. The majority of data are presented as a single comma-separated value file with 144 rows representing 3 replicates of 4 size fractions from 12 sampling sites. Samples have unique names and are categorised by Size (4 categories), Drain (6 categories) and Site (12 categories, 2 per Drain). Quantitative data relating to microplastics measurement include: sample volume; raw counts of total microplastics and microplastics separated into fragment, fibre, film, and microbead categories; concentrations of total microplastics and microplastics separated into fragment, fibre, film, and microbead categories; blank corrections (fibres only); corrected raw counts and concentrations of fibres; corrected raw counts and concentrations of total microplastics. Catchment demographic and land use data are: catchment area and population; proportions of land use in residential, industrial, services, agricultural, natural, and public open space categories. Rainfall for the 7 days prior to sample collection is also recorded. A separate comma-separated value file summarises the microplastic colour data, and an image shows aerial photograph maps of each site.

The statistic depicts Australia's gross domestic product (GDP) from 1987 to 2024, with projections up until 2030. In 2024, GDP in Australia amounted to about 1.8 trillion US dollars. See global GDP for a global comparison. Australia’s economy and population Australia’s gross domestic product has been growing steadily, and all in all, Australia and its economic key factors show a well-set country. Australia is among the countries with the largest gross domestic product / GDP worldwide, and thus one of the largest economies. It was one of the few countries not severely stricken by the 2008 financial crisis; its unemployment rate, inflation rate and trade balance, for example, were hardly affected at all. In fact, the trade balance of Australia – a country’s exports minus its imports – has been higher than ever since 2010, with a slight dip in 2012. Australia mainly exports wine and agricultural products to countries like China, Japan or South Korea. One of Australia’s largest industries is tourism, which contributes a significant share to its gross domestic product. Almost half of approximately 23 million Australian residents are employed nowadays, life expectancy is increasing, and the fertility rate (the number of children born per woman) has been quite stable. A look at the distribution of the world population by continent shows that Australia is ranked last in terms of population and population density. Most of Australia's population lives at the coast in metropolitan areas, since parts of the continent are uninhabitable. Unsurprisingly, Australia is known as a country with very high living standards, four of its biggest cities – Melbourne, Adelaide, Sydney and Perth – are among the most livable cities worldwide.