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;

The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

This dataset is about countries in Australia. It has 1 row. It features 5 columns: currency, capital city, continent, and fertility rate.

This table contains education indicators (public primary school student teacher ratio, enrollment rate, high educated persons) for ACT (SA3) and surrounding NSW Councils (LGA) from various sources such as ACARA, ACT Department of Education and Population Census.

Derived projected datasets for the eight Australian capital cities in 2016-2045 and 2036-2065, centred around 2030 and 2050, respectively. Projects used eight general circulation models (GCMs) under Representative Concentration Pathway [RCP]2.6, RCP4.5, RCP6.0 and RCP8.5. The scenarios were under Coupled Model Intercomparison Project [CMIP]5. The eight GCM models are ACCESS1-0, CESM1-CAM5, CNRM-CM5, CanESM2, GFDL-ESM2M, HadGEM2-CC, MIROC5 and NorESM1-M, and are described online: https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/. Only data from five GCMs are available for RCP2.6 and four for RCP6.0.

For each city, seven*seven 5 km grids were extracted at grid centroids correlating to the centre of its central business district. These coordinates are in the file "City coordinate." The corresponding datasets for each city, RCP, GCM, time period, and meteorological variable are located in their respective city folder in the folder "future." The meteorological variables are relative humidity ("hurs"), solar radiation ("rsds"), average air temperature ("tas"), maximum air temperature "(tasmax") and minimum air temperature ("tasmin"). These were used to create derived .csv files also stored in the "future" folder, which in turn were used to create derived R datasets ("ccia_future.rda" and "ccia_future2.rda") combining all the datasets into one and creating additional meteorological indices using the available data. The R code used to create these datasets is included "CCiA data manipulation.R". It uses functions stored in the R code file "Climate functions.R". The additional meteorological indices include alternate humidity variables, apparent temperature variables and the Excess Heat Factor (EHF). The heatwave thresholds values used to calculate EHF (the 95th percentile of daily mean temperature from a reference period) per city are included in "barra_ehfr.R" and were calculated from a separate dataset (not included) derived from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis (BARRA).

The original projected climate datasets were sourced from Climate Change in Australia (CCiA), published by the Commonwealth Science Industrial Research Organisation (CSIRO). The original datasets are available online: https://data-cbr.csiro.au/thredds/catalog/catch_all/oa-aus5km/Climate_Change_in_Australia_User_Data/Application_Ready_Data_Gridded_Daily/catalog.html. The license under which the data were used is available online: https://www.climatechangeinaustralia.gov.au/en/overview/about-site/licences-and-acknowledgements/.

I acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modelling groups (listed at https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Further information regarding these datasets and meteorological variables is listed in the author's PhD thesis, available online: https://digital.library.adelaide.edu.au/dspace/handle/2440/137773. For any queries, please do not hesitate to contact the author: matthew.borg@adelaide.edu.au.

A dataset indicating which days from 2004 to 2022 are public holidays in the eight Australian capital cities (Adelaide, Brisbane, Canberra, Darwin, Hobart, Melbourne, Perth and Sydney). The dataset is attached in both .xlsx format and .rda format.

Each observation represent a date. Variables are included for the date, year, month, day, day of the week, name of the public holiday(s), binary markers for whether a public holiday occurs in the relevant city (1 if it does, missing otherwise), the number of capital cities where a public holiday occurs for a given day, and a binary TRUE/FALSE marker indicating if all cities have a public holiday.

Part-time holidays that only occur after 5pm, in particular New Year's Eve in some cities, are excluded. No public holidays are listed as occuring on a Sunday in Adelaide. Under the Holidays Act 1910, all Sundays are nominally public holidays in South Australia. In Hobart, public holidays were included for the Royal Hobart Regatta and Royal Hobart Show but not for Easter Tuesday; Easter Tuesday is only observed by the state public sector.

The data for each city was manually collected and combined from the state and territory government webpages included in the references. For any queries regarding this dataset, please do not hesitate to contact the author: matthew.borg@adelaide.edu.au.

This dataset is the Vulnerability Indices for Mortgage, Petroleum and Inflation Risks and Expenditure (VAMPIRE) for Australian Capital Cities for the year of 2016. The data has been calculated for each SA1 region region within the Greater Capital City regions following the 2016 Australian Statistical Geography Standard (ASGS). The VAMPIRE index is a research method developed at RMIT University's Centre for Urban Research (CUR) commissioned by AURIN. It assesses socio-economic oil price vulnerability in Australian cities based on an analysis of socio-economic indicators from the ABS. This technique has been successful in linking socio-economic data with an improved understanding of socio-spatial structure of vulnerability from rising transport and housing costs. Providing the VAMPIRE index in AURIN's existing data infrastructure will permit researchers and practitioners to access and evaluate VAMPIRE within their own local contexts. The key (and only) dataset used to construct the VAMPIRE index is ABS census data for 2016. For each census year four Basic Community Profile (BCP) variables are used: (1) median household weekly income; (2) proportion of households owning two or more vehicles; (3) proportion of people traveling to work by car; and (4) number of homes being purchased with a mortgage. For more information please view the Technical Documentation.

Abstract copyright UK Data Service and data collection copyright owner. This is one dataset arising from a project whose main aims are: 1. To contribute to knowledge by engaging in a study of the relationship between Australia, New Zealand and international capital markets 1850-1950 which would focus on three key themes: i. The history of Australia and New Zealand as borrowers and debtors. ii. The rise and consolidation of the British 'colonial' market in the London capital market from the mid-nineteenth century to the late 1920s. iii. The interaction between the market disciplines to which all borrowers were subject, and the opportunities and constraints created by membership of the British Empire. The study would also evaluate recent arguments (Cain and Hopkins, 1993) about the role of the City of London in the dynamics of British imperial expansion and control with respect to two British settler societies, Australia and New Zealand. 2. To extend and revise the statistics of Australasian public debt in the period 1850-1950. 3. To create a database of Australasian overseas public loans during that period. The projects specific objectives were to complete three stages of research: 1. The consultation of archival and printed official sources in the United Kingdom and Australia relating to Australasian borrowing activity and relations with overseas creditors during nineteenth century. These either had not been available to, or were not consulted by, earlier historians. 2. The collection of quantitative data for revised statistics of Australian and New Zealand public debt between 1850 and 1950. 3. The collection of data for a database of Australasian overseas public loans during that period. Main Topics: This dataset publishes new statistics of Australian colonial and state debt, and of capital raised by all Australian public borrowers (including corporation) in London, until 1914. Current historical statistics do not distinguish between stocks of debt held locally or abroad. Moreover, the time series of new capital subscribed or received in London prepared by Butlin, Simon, Hall, and others often aggregate all colonial public borrowing, have different terminal dates, and are inconsistent with each other. The new statistics remedy these deficiencies. Three types of table are presented. The first disaggregates, and where necessary corrects, the official annual statistics of stocks of outstanding debt of each Australian colony, distinguishing between the place of original sale, long and short-term securities, and gross new issues (i.e. the nominal value of all securities sold) and repayments. The second shows the stocks of long and short term debt held in Australia and the United Kingdom. These are taken principally from Statistical Registers, and include debt (e.g. stock issued by Savings Banks) omitted from the official statistics in the early years. The final type of table summarises the principal annual flows in London of capital created (including as a result of conversions and exchanges), subscribed, received, and amortized for each colonial government and for public corporations as a single group. It excludes flows arising from remittance of securities originally sold in the colonies, but includes transfers from London to colonial registers and purchases from sinking funds where they are known. The data is presented in 18 spreadsheets and are of seven separate borrowers: New South Wales (3 spreadsheets), Victoria (3), Queensland (3), South Australia (3), Tasmania (2), Western Australia (2), and public corporations (1). Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

This dataset presents the estimates of the internal migration statistics of Australia by Greater Capital City Statistical Area (GCCSA) following the 2011 Australian Statistical Geography Standard (ASGS). The dataset spans from the 2006-07 financial year up to the 2015-16 financial year.

Regional internal migration is the movement of people from one region to another within Australia (both interstate and intrastate). For example, it incorporates moves from a GCCSA to any other GCCSA within the country. Net regional internal migration is the net gain or loss of population through this movement.

The ABS has developed a new series of annual regional internal migration estimates (RIME) based on the 2011 edition of the Australian Statistical Geography Standard (ASGS). The Medicare and Defence data used for estimating interstate migration is now also used to estimate internal migration below the state/territory level. A similar method was used to prepare RIME at the LGA level, based on 2011 boundaries.

This data is Australian Bureau of Statistics (ABS) data (catalogue number: 3412.0) used with permission from the ABS.

For more information please visit the ABS Explanatory Notes.

Please note: RIME are not directly comparable with estimated resident populations (ERPs) because of the different methods and source data used to prepare each series. The combination of natural increase and net migration (internal and overseas) therefore may not correspond with change in ERP. AURIN has spatially enabled the original data.

Comprehensive dataset containing 38 verified Delivery service businesses in Australian Capital Territory, Australia with complete contact information, ratings, reviews, and location data.

Plant Machinery Capital Expenditure in Australia increased to 0.30 percent in the second quarter of 2025 from -1.70 percent in the first quarter of 2025. This dataset includes a chart with historical data for Australia Plant Machinery Capital Expenditure.

Outline map of Australia (with state borders and capital city locations)

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This dataset is the Greater Capital City Statistical Area (GCCSA) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016. For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

This dataset contains observed bike counts from sites across the city known as "Super Sunday". This is Australia’s biggest survey of recreational travel. Held annually in mid-November, the count looks at how runners, walkers, bike riders and other recreational users move around

There is a large number of fields captured for this dataset, which has been compiled into an attached metadata document.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Australian Bureau of Statistics (ABS) Catalogue Number: 3218.0 Population Estimates by Local Government Area, 2011 to 2012.

Dataset History

ABS Catalogue Number 3218.0 Population Estimates by Local Government Area, 2011 to 2012.

This dataset was downloaded as a single spreadsheet file (.xls) on 29 May, 2014 from the following website: http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3218.02011-12?OpenDocument

(Metadata taken from the Explanatory Notes section of the ABS website http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/3218.0Explanatory%20Notes12011-12?OpenDocument)

INTRODUCTION

1 This product contains estimates of the resident population of Statistical Areas Level 2 to 4 (SA2s - SA4s) and Greater Capital City Statistical Areas (GCCSAs) of Australia. These estimates plus those for Local Government Areas, Significant Urban Areas, Remoteness Areas and Electoral Divisions are also provided in the Downloads tab of this issue.

2 To meet the conflicting demands for accuracy and timeliness there are several versions of sub-state/territory population estimates. Preliminary estimates as at 30 June are normally available by April of the following year, revised estimates twelve months later and rebased and final estimates after the following Census. The estimates in this issue are preliminary rebased for 2011, based on the results of the 2011 Census, and preliminary for 2012.

For an ABS Glossary of Terms visit:

http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/2901.0Main%20Features12011

Dataset Citation

Australian Bureau of Statistics (2013) ABS Regional Population Growth Australia 2011-2012. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/c27fc127-3743-4805-b4b6-f50712cd655f.

This dataset is the Greater Capital City Statistical Area (GCCSA) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011. For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

Comprehensive dataset containing 3 verified Move Yourself locations in Australian Capital Territory, Australia with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 2 verified Department of Transportation businesses in Australian Capital Territory, Australia with complete contact information, ratings, reviews, and location data.

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.

Supplementary Information Files for Socio-economic groups moving apart: An analysis of recent trends in residential segregation in Australia's main capital citiesWe study changes in the spatial distribution and segregation of socio-economic groups in Australia using a new data set with harmonised census data for 1991 and 2011. We find a general increase in residential segregation by education and occupation groups across the major capital cities in Australia. Importantly, these trends cannot be explained in general by changes in the demographic structure of groups and areas but rather by the rise in the over and underrepresentation of groups across areas. In particular, our analysis reveals clear diverging trends in the spatial configuration of high and low socio-economic groups as measured by their occupation and education. Whereas high-skilled groups became more concentrated in the inner parts of cities, the low-educated and those working in low-status occupations became increasingly overrepresented in outer areas. This pattern is observed in all five major capital cities, but it is especially marked in Sydney, Melbourne and Brisbane.