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;

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.

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.

Australia Population Density: People per Square Km data was reported at 3.382 Person/sq km in 2022. This records an increase from the previous number of 3.339 Person/sq km for 2021. Australia Population Density: People per Square Km data is updated yearly, averaging 2.263 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 3.382 Person/sq km in 2022 and a record low of 1.365 Person/sq km in 1961. Australia Population Density: People per Square Km 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 density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

The final Australian National Liveability Study 2018 datasets comprise a suite of policy relevant spatial indicators of local neighbourhood liveability and amenity access estimated for residential address points across Australia's 21 largest cities, and summarised at range of larger area scales (Mesh Block, Statistical Areas 1-4, Suburb, LGA, and overall city summaries). The indicators and measures included encompass topics including community and health services, employment, food, housing, public open space, transportation, walkability and overall liveability. The datasets were produced through analysis of built environment and social data from multiple sources including OpenStreetMap the Australian Bureau of Statistics, and public transport agency GTFS feed data. These are provided in CSV format under an Open Data Commons Open Database licence. The 2018 Australian National Liveability data will be of interest to planners, population health and urban researchers with an interest in the spatial distribution of built environment exposures and outcomes for data linkage, modelling and mapping purposes. Area level summaries for the data were used to create the indicators for the Australian Urban Observatory at its launch in 2020. A detailed description of the datasets and the study has been published in Nature Scientific Data, and notes and code illustrating usage of the data are located on GitHub. The spatial data were developed by the Healthy Liveable Cities Lab, Centre for Urban Research with funding support provided from the Australian Prevention Partnership Centre #9100003, NESP Clean Air and Urban Landscapes Hub, NHMRC Centre of Research Excellence in Healthy, Liveable Communities #1061404 and an NHMRC Senior Principal Research Fellowship GNT1107672; with interactive spatial indicator maps accessible via the Australian Urban Observatory. Any publications utilising the data are not necessarily the view of or endorsed by RMIT University or the Centre of Urban Research. RMIT excludes all liability for any reliance on the data.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Australia. Power Plant emissions from all power plants in Australia were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/18

Coding data put forward in article: http://www.abc.net.au/triplej/programs/hack/welcome-to-the-town-with-no-single-women/9092386.Note: 32 postal codes were listed for 'more single men' and 30 were listed for 'more single women'. One postal code for 'more single women' was labelled in the article as 'null' and could not be matched to a Australian remoteness category (ARIA) nor socioeconomic index for areas (SEIFA). It looks like a remote area of Queensland to me: http://australia.postcode.info/p/4892Comments in FB feed were based on a raw proportion of areas listed as 'Major cities' for the first 29 areas which could be assigned a valid remoteness category. Two additional areas were listed as 'major cities/inner regional' areas for men and were not counted as major cities. A priori 4 lyf. Variables in datasetPostalcode: post code for location - used to merge data for ARIA and SEIFA indices.Remoteness: Australian Remoteness Index of Australia (ARIA) by postal code as extracted from http://www.pocog.org.au/aria/default.aspxGender: f = more single women; m = more single menSEIFA: Socio-economic Indexes for Areas (SEIFA) as extracted from http://stat.data.abs.gov.au/Index.aspx?DataSetCode=SEIFA_POA

This dataset displays the locations of all operating renewable energy generators. The generators are classified by technology and by state. The renewables webmap contains locations of Australian renewable power stations that are greater than 3kW. Each power station has such information as fuel type, technology used, size (kW), ownership, latitude and longitude and data source. Web links and site photographs are provided where possible. A download feature is provided for clients who want the base data.

The average commuting distance in kilometres by place of residence by major cities 2011. This information provided by the Bureau of Infrastructure, Transport and Regional Economics (BITRE). Further information can be found at www.bitre.gov.au. Australia’s commuting distance:cities and regions.\r \r Figure BLT30 in Built environment. See; https://soe.environment.gov.au/theme/built-environment/topic/2016/livability-transport#built-environment-figure-BLT30

Price-To-Cashflow-Ratio Time Series for Dexus Industria REIT. Dexus Industria REIT (ASX code: DXI) is a listed Australian real estate investment trust which is primarily invested in high-quality industrial warehouses. At 31 December 2024, the fund's investment property portfolio is valued at $1.4 billion and is located across the major Australian cities, providing sustainable income and capital growth prospects for security holders over the long term. The fund has a target gearing range of 30"40%. Dexus Industria REIT is governed by a majority Independent Board and managed by Dexus (ASX code: DXS), a leading Australasian fully integrated real asset group, with four decades of expertise in real estate and infrastructure investment, funds management, asset management and development.

Other-Current-Assets Time Series for Goodman Group. Goodman Group is a provider of essential infrastructure. It owns, develops and manages high quality, sustainable logistics properties and data centres in major global cities, that are critical to the digital economy. Goodman has operations in key consumer markets across Australia, New Zealand, Asia, Europe, the United Kingdom, and the Americas. Goodman Group, comprised of the stapled entities Goodman Limited, Goodman Industrial Trust and Goodman Logistics (HK) Limited, is the largest property group on the Australian Securities Exchange (ASX: GMG), a top 10 entity by market capitalisation, and one of the largest listed specialist investment managers of industrial property globally The Group's property portfolio includes logistics and distribution centres, data centres, warehouses, light industrial, multi-storey industrial, and business parks. Goodman takes a long-term view, investing significantly alongside its capital Partners in its investment management platform and concentrating the portfolio where it can create the most value for customers and investors.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

This dataset includes historical shark attacks by territory. It is a simplified version of the large database of shark attacks created by The Global Shark Attack File. About GASF: The Global Shark Attack File was created to provide medical personnel, shark behaviorists, lifesavers, and the media with meaningful information resulting from the scientific forensic examination of shark accidents. Whenever possible, GSAF investigators conduct personal interviews with patients and witnesses, medical personnel and other professionals, and conduct examinations of the incident site. Weather and sea conditions and environmental data are evaluated in an attempt to identify factors that contributed to the incident. Source: http://www.sharkattackfile.net/incidentlog.htm Accessed: 9.27.07

Data for the ACT is (C) Access canberra and licenced for reuse under the CC By 4.0 International, https://creativecommons.org/licenses/by/4.0/

Data for NSW was provided by the Office of Environment and Heritage, NSW Government.

Data for the Northern Territory was sourced from the Northern Territory Environment Protection Authority.

Data for Queensland was provided by the State of Queensland, Department of Science, Information Technology and Innovation.

Data for South Australia was created and supplied by the Environment Protection Authority, SA.

Data for Tasmania was provided by EPA Tasmania, DPIPWE.

Data for Victoria was provided by the Environment Protection Authority Victoria.

Data for Western Australia was provided by the Western Australian Department of Environment Regulation.

Data used to produce figure ATM36 of the Atmosphere theme of SoE2016 available at https://soe.environment.gov.au/theme/ambient-air-quality/topic/2016/ozone#ambient-air-quality-figure-ATM36

Weighted dementia prevalence by age and sex in Australia.

This dataset displays the annual import of both beef and veal stocks into the United States. The figures are given in a carcass wt. 1,000 pounds scale. Data is available from 2003 to January of 2008. The main sources being Australia, Canada, and New Zealand.